From 71e6441b2f7c2ce34a5a21b3ddd8a559355facc7 Mon Sep 17 00:00:00 2001
From: bruAristimunha <a.bruno@aluno.ufabc.edu.br>
Date: Fri, 10 Nov 2023 17:58:53 +0100
Subject: [PATCH] new version 0.8

---
 0.8/.buildinfo                                |     4 +
 0.8/.nojekyll                                 |     0
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 .../plot_sleep_staging_chambon2018.ipynb      |    52 +-
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 0.8/auto_examples/applied_examples/index.html |   562 +
 .../plot_bcic_iv_4_ecog_trial.html            |   932 +-
 .../plot_sleep_staging_chambon2018.html       |  1007 ++
 .../plot_sleep_staging_eldele2021.html        |  1104 ++
 .../plot_sleep_staging_usleep.html            |  1059 ++
 .../applied_examples/plot_tuh_eeg_corpus.html |   884 ++
 .../applied_examples/sg_execution_times.html  |   500 +
 .../benchmark_lazy_eager_loading.html         |   758 +-
 0.8/auto_examples/datasets_io/index.html      |   571 +
 .../plot_benchmark_preprocessing.html         |  1038 ++
 .../plot_custom_dataset_example.html          |   746 ++
 .../datasets_io/plot_load_save_datasets.html  |   886 ++
 .../datasets_io/plot_mne_dataset_example.html |   802 ++
 .../plot_moabb_dataset_example.html           |   810 ++
 .../datasets_io/plot_split_dataset.html       |  1066 ++
 .../plot_tuh_discrete_multitarget.html        |   785 ++
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 .../plot_bcic_iv_2a_moabb_cropped.html        |  1022 ++
 .../plot_bcic_iv_2a_moabb_trial.html          |   970 ++
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 ...indecode.augmentation.ChannelsShuffle.html |   752 ++
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 ...aindecode.augmentation.SmoothTimeMask.html |   771 ++
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 .../braindecode.classifier.EEGClassifier.html |  1084 ++
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 ...raindecode.datasets.BaseConcatDataset.html |   824 ++
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literal 0
HcmV?d00001

diff --git a/stable/_downloads/9d9ad03ba61ec2571831dd8a39013a5d/plot_sleep_staging_chambon2018.ipynb b/0.8/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb
similarity index 80%
rename from stable/_downloads/9d9ad03ba61ec2571831dd8a39013a5d/plot_sleep_staging_chambon2018.ipynb
rename to 0.8/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb
index 91becf40..9252cf54 100644
--- a/stable/_downloads/9d9ad03ba61ec2571831dd8a39013a5d/plot_sleep_staging_chambon2018.ipynb
+++ b/0.8/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sleep staging on the Sleep Physionet dataset using Chambon2018 network\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the time distributed approach of [1]_ to learn on\nsequences of EEG windows using the openly accessible Sleep Physionet dataset\n[2]_ [3]_.\n\n## References\n.. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n"
+        "\n# Sleep staging on the Sleep Physionet dataset using Chambon2018 network\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the time distributed approach of [1]_ to learn on\nsequences of EEG windows using the openly accessible Sleep Physionet dataset\n[2]_ [3]_.\n"
       ]
     },
     {
@@ -62,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import preprocess, Preprocessor, scale\n\nhigh_cut_hz = 30\n\npreprocessors = [\n    Preprocessor(scale, factor=1e6, apply_on_array=True),\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {
@@ -80,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import create_windows_from_events\n\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping\n)"
+        "from braindecode.preprocessing import create_windows_from_events\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping\n)"
       ]
     },
     {
@@ -134,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
+        "import numpy as np\nfrom braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
       ]
     },
     {
@@ -188,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_classes=n_classes,\n    input_size_s=input_size_samples / sfreq,\n    return_feats=True\n)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_outputs=n_classes,\n    n_times=input_size_samples,\n    return_feats=True\n)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
       ]
     },
     {
@@ -206,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 10\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 10\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=np.unique(y_train),\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
       ]
     },
     {
@@ -242,7 +231,25 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import confusion_matrix, classification_report\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nprint(confusion_matrix(y_true, y_pred))\nprint(classification_report(y_true, y_pred))"
+        "from sklearn.metrics import confusion_matrix, classification_report\nfrom braindecode.visualization import plot_confusion_matrix\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\nplot_confusion_matrix(confusion_mat=confusion_mat,\n                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])\n\nprint(classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true, color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
       ]
     },
     {
@@ -251,6 +258,13 @@
       "source": [
         "Our model was able to learn despite the low amount of data that was available\n(only two recordings in this example) and reached a balanced accuracy of\nabout 36% in a 5-class classification task (chance-level = 20%) on held-out\ndata.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To further improve performance, more recordings should be included in the\n   training set, and hyperparameters should be selected accordingly.\n   Increasing the sequence length was also shown in [1]_ to help improve\n   performance, especially when few EEG channels are available.</p></div>\n\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -269,7 +283,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py b/0.8/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py
similarity index 71%
rename from stable/_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py
rename to 0.8/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py
index bc91fbbe..d5ad7952 100644
--- a/stable/_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py
+++ b/0.8/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py
@@ -1,30 +1,33 @@
 """
-Trialwise Decoding on BCIC IV 2a Dataset
+Basic Brain Decoding on EEG Data
 ========================================
 
 This tutorial shows you how to train and test deep learning models with
 Braindecode in a classical EEG setting: you have trials of data with
 labels (e.g., Right Hand, Left Hand, etc.).
 
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
 """
 
 ######################################################################
-# Loading and preprocessing the dataset
+# Loading and preparing the data
 # -------------------------------------
 #
 
 
 ######################################################################
-# Loading
-# ~~~~~~~
+# Loading the dataset
+# ~~~~~~~~~~~~~~~~~~~~~~~
 #
 
 
 ######################################################################
-# First, we load the data. In this tutorial, we use the functionality of
-# braindecode to load datasets through
-# `MOABB <https://github.com/NeuroTechX/moabb>`__ to load the BCI
-# Competition IV 2a data.
+# First, we load the data. In this tutorial, we load the BCI Competition
+# IV 2a data [1]_ using braindecode's wrapper to load via
+# `MOABB library <https://github.com/NeuroTechX/moabb>`__ [2]_.
 #
 # .. note::
 #    To load your own datasets either via mne or from
@@ -36,7 +39,7 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
 
 ######################################################################
@@ -54,16 +57,18 @@
 # numpy array.
 #
 # .. note::
-#    These prepocessings are now directly applied to the loaded
-#    data, and not on-the-fly applied as transformations in
+#    Generally, braindecode prepocessing is directly applied to the loaded
+#    data, and not applied on-the-fly as transformations, such as in
 #    PyTorch-libraries like
 #    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
 #
 
-from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor)
 from numpy import multiply
 
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
+
 low_cut_hz = 4.  # low cut frequency for filtering
 high_cut_hz = 38.  # high cut frequency for filtering
 # Parameters for exponential moving standardization
@@ -81,21 +86,21 @@
 ]
 
 # Transform the data
-preprocess(dataset, preprocessors)
+preprocess(dataset, preprocessors, n_jobs=-1)
 
 
 ######################################################################
-# Cut Compute Windows
-# ~~~~~~~~~~~~~~~~~~~
+# Extracting Compute Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
 
 ######################################################################
-# Now we cut out compute windows, the inputs for the deep networks during
-# training. In the case of trialwise decoding, we just have to decide if
-# we want to cut out some part before and/or after the trial. For this
-# dataset, in our work, it often was beneficial to also cut out 500 ms
-# before the trial.
+# Now we extract compute windows from the signals, these will be the inputs
+# to the deep networks during training. In the case of trialwise
+# decoding, we just have to decide if we want to include some part
+# before and/or after the trial. For our work with this dataset,
+# it was often beneficial to also include the 500 ms before the trial.
 #
 
 from braindecode.preprocessing import create_windows_from_events
@@ -118,24 +123,24 @@
 
 
 ######################################################################
-# Split dataset into train and valid
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Splitting the dataset into training and validation sets
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
 
 ######################################################################
 # We can easily split the dataset using additional info stored in the
 # description attribute, in this case ``session`` column. We select
-# ``session_T`` for training and ``session_E`` for validation.
+# ``T`` for training and ``test`` for validation.
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 
 
 ######################################################################
-# Create model
+# Creating a model
 # ------------
 #
 
@@ -143,17 +148,16 @@
 ######################################################################
 # Now we create the deep learning model! Braindecode comes with some
 # predefined convolutional neural network architectures for raw
-# time-domain EEG. Here, we use the shallow ConvNet model from `Deep
-# learning with convolutional neural networks for EEG decoding and
-# visualization <https://arxiv.org/abs/1703.05051>`__. These models are
+# time-domain EEG. Here, we use the shallow ConvNet model from [3]_. These models are
 # pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
 # to use your own model, it just has to be a normal PyTorch
 # `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
 #
 
 import torch
-from braindecode.util import set_random_seeds
+
 from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
 
 cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
 device = 'cuda' if cuda else 'cpu'
@@ -169,6 +173,7 @@
 set_random_seeds(seed=seed, cuda=cuda)
 
 n_classes = 4
+classes = list(range(n_classes))
 # Extract number of chans and time steps from dataset
 n_chans = train_set[0][0].shape[0]
 input_window_samples = train_set[0][0].shape[1]
@@ -180,27 +185,31 @@
     final_conv_length='auto',
 )
 
+# Display torchinfo table describing the model
+print(model)
+
 # Send model to GPU
 if cuda:
-    model.cuda()
+    model = model.cuda()
 
 
 ######################################################################
-# Training
-# --------
+# Model Training
+# --------------
 #
 
 
 ######################################################################
-# Now we train the network! EEGClassifier is a Braindecode object
+# Now we will train the network! ``EEGClassifier`` is a Braindecode object
 # responsible for managing the training of neural networks. It inherits
-# from skorch.NeuralNetClassifier, so the training logic is the same as in
-# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+# from skorch `NeuralNetClassifier <https://skorch.readthedocs.io/en/stable/classifier.html#>`__,
+# so the training logic is the same as in `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
 #
 
 
 ######################################################################
-#    **Note**: In this tutorial, we use some default parameters that we
+# .. note::
+#    In this tutorial, we use some default parameters that we
 #    have found to work well for motor decoding, however we strongly
 #    encourage you to perform your own hyperparameter optimization using
 #    cross validation on your training data.
@@ -210,7 +219,8 @@
 from skorch.helper import predefined_split
 
 from braindecode import EEGClassifier
-# These values we found good for shallow network:
+
+# We found these values to be good for the shallow network:
 lr = 0.0625 * 0.01
 weight_decay = 0
 
@@ -233,14 +243,15 @@
         "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
 )
-# Model training for a specified number of epochs. `y` is None as it is already supplied
-# in the dataset.
-clf.fit(train_set, y=None, epochs=n_epochs)
+# Model training for the specified number of epochs. `y` is None as it is
+# already supplied in the dataset.
+_ = clf.fit(train_set, y=None, epochs=n_epochs)
 
 
 ######################################################################
-# Plot Results
+# Plotting Results
 # ------------
 #
 
@@ -251,8 +262,8 @@
 #
 
 import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
 import pandas as pd
+from matplotlib.lines import Line2D
 
 # Extract loss and accuracy values for plotting from history object
 results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
@@ -263,7 +274,6 @@
 df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
                valid_misclass=100 - 100 * df.valid_accuracy)
 
-plt.style.use('seaborn')
 fig, ax1 = plt.subplots(figsize=(8, 3))
 df.loc[:, ['train_loss', 'valid_loss']].plot(
     ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
@@ -289,17 +299,18 @@
 
 
 ######################################################################
-# Plot Confusion Matrix
-# ---------------------
+# Plotting a  Confusion Matrix
+# ----------------------------
 #
 
 
 #######################################################################
-# Generate a confusion matrix as in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730
+# Here we generate a confusion matrix as in [3]_.
 #
 
 
 from sklearn.metrics import confusion_matrix
+
 from braindecode.visualization import plot_confusion_matrix
 
 # generate confusion matrices
@@ -312,9 +323,29 @@
 
 # add class labels
 # label_dict is class_name : str -> i_class : int
-label_dict = valid_set.datasets[0].windows.event_id.items()
+label_dict = windows_dataset.datasets[0].window_kwargs[0][1]['mapping']
 # sort the labels by values (values are integer class labels)
-labels = list(dict(sorted(list(label_dict), key=lambda kv: kv[1])).keys())
+labels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]
 
 # plot the basic conf. matrix
 plot_confusion_matrix(confusion_mat, class_names=labels)
+
+#############################################################
+#
+#
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/25408d8d92a873e370d4750ba414b84a/benchmark_lazy_eager_loading.ipynb b/0.8/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb
similarity index 72%
rename from stable/_downloads/25408d8d92a873e370d4750ba414b84a/benchmark_lazy_eager_loading.ipynb
rename to 0.8/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb
index c44725b9..8d23c068 100644
--- a/stable/_downloads/25408d8d92a873e370d4750ba414b84a/benchmark_lazy_eager_loading.ipynb
+++ b/0.8/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Benchmarking eager and lazy loading\n\nIn this example, we compare the execution time and memory requirements of 1)\neager loading, i.e., preloading the entire data into memory and 2) lazy loading,\ni.e., only loading examples from disk when they are required. We also include\nsome other experiment parameters in the comparison for the sake of completeness\n(e.g., `num_workers`, `cuda`, `batch_size`, etc.).\n\nWhile eager loading might be required for some preprocessing steps that require\ncontinuous data (e.g., temporal filtering, resampling), it also allows\nfast access to the data during training. However, this might come at the expense\nof large memory usage, and can ultimately become impossible if the dataset does\nnot fit into memory (e.g., the TUH EEG dataset's >1,5 TB of recordings will\nnot fit in the memory of most machines).\n\nLazy loading avoids this potential memory issue by loading examples from disk\nwhen they are required. This means large datasets can be used for training,\nhowever this introduces some file-reading overhead every time an example must\nbe extracted. Some preprocessing steps that require continuous data also have to\nbe implemented differently to accomodate the nature of windowed data. Overall\nthough, we can reduce the impact of lazy loading by using the `num_workers`\nparameter of pytorch's `Dataloader` class, which dispatches the data loading to\nmultiple processes.\n\nTo enable lazy loading in braindecode, data files must be saved in an\nMNE-compatible format (e.g., 'fif', 'edf', etc.), and the `Dataset` object must\nhave been instantiated with parameter `preload=False`.\n"
+        "# Benchmarking eager and lazy loading\n\nIn this example, we compare the execution time and memory requirements of 1)\neager loading, i.e., preloading the entire data into memory and 2) lazy loading,\ni.e., only loading examples from disk when they are required. We also include\nsome other experiment parameters in the comparison for the sake of completeness\n(e.g., `num_workers`, `cuda`, `batch_size`, etc.).\n\nWhile eager loading might be required for some preprocessing steps that require\ncontinuous data (e.g., temporal filtering, resampling), it also allows\nfast access to the data during training. However, this might come at the expense\nof large memory usage, and can ultimately become impossible if the dataset does\nnot fit into memory (e.g., the TUH EEG dataset's >1,5 TB of recordings will\nnot fit in the memory of most machines).\n\nLazy loading avoids this potential memory issue by loading examples from disk\nwhen they are required. This means large datasets can be used for training,\nhowever this introduces some file-reading overhead every time an example must\nbe extracted. Some preprocessing steps that require continuous data also have to\nbe implemented differently to accommodate the nature of windowed data. Overall\nthough, we can reduce the impact of lazy loading by using the `num_workers`\nparameter of pytorch's `Dataloader` class, which dispatches the data loading to\nmultiple processes.\n\nTo enable lazy loading in braindecode, data files must be saved in an\nMNE-compatible format (e.g., 'fif', 'edf', etc.), and the `Dataset` object must\nhave been instantiated with parameter `preload=False`.\n"
       ]
     },
     {
@@ -26,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom itertools import product\nimport time\n\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\n\nimport mne\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nfrom braindecode.datasets import TUHAbnormal\nfrom braindecode.preprocessing import create_fixed_length_windows\nfrom braindecode.models import ShallowFBCSPNet, Deep4Net\n\n\nmne.set_log_level('WARNING')  # avoid messages everytime a window is extracted"
+        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom itertools import product\nimport time\n\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\n\nimport mne\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nfrom braindecode.datasets import TUHAbnormal\nfrom braindecode.preprocessing import create_fixed_length_windows\nfrom braindecode.models import ShallowFBCSPNet, Deep4Net\n\n\nmne.set_log_level('WARNING')  # avoid messages every time a window is extracted"
       ]
     },
     {
@@ -62,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "def load_example_data(preload, window_len_s, n_recordings=10):\n    \"\"\"Create windowed dataset from subjects of the TUH Abnormal dataset.\n\n    Parameters\n    ----------\n    preload: bool\n        If True, use eager loading, otherwise use lazy loading.\n    window_len_s: int\n        Window length in seconds.\n    n_recordings: list of int\n        Number of recordings to load.\n\n    Returns\n    -------\n    windows_ds: BaseConcatDataset\n        Windowed data.\n\n    .. warning::\n        The recordings from the TUH Abnormal corpus do not all share the same\n        sampling rate. The following assumes that the files have already been\n        resampled to a common sampling rate.\n    \"\"\"\n\n    recording_ids = list(range(n_recordings))\n\n    ds = TUHAbnormal(\n        TUH_PATH, recording_ids=recording_ids,\n        target_name='pathological',\n        preload=preload)\n\n    fs = ds.datasets[0].raw.info['sfreq']\n    window_len_samples = int(fs * window_len_s)\n    window_stride_samples = int(fs * 4)\n    # window_stride_samples = int(fs * window_len_s)\n    windows_ds = create_fixed_length_windows(\n        ds, start_offset_samples=0, stop_offset_samples=None,\n        window_size_samples=window_len_samples,\n        window_stride_samples=window_stride_samples, drop_last_window=True,\n        preload=preload, drop_bad_windows=True)\n\n    # Drop bad epochs\n    # XXX: This could be parallelized.\n    # XXX: Also, this could be implemented in the Dataset object itself.\n    for ds in windows_ds.datasets:\n        ds.windows.drop_bad()\n        assert ds.windows.preload == preload\n\n    return windows_ds\n\n\ndef create_example_model(n_channels, n_classes, window_len_samples,\n                         kind='shallow', cuda=False):\n    \"\"\"Create model, loss and optimizer.\n\n    Parameters\n    ----------\n    n_channels : int\n        Number of channels in the input\n    n_times : int\n        Window length in the input\n    n_classes : int\n        Number of classes in the output\n    kind : str\n        'shallow' or 'deep'\n    cuda : bool\n        If True, move the model to a CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Model to train.\n    loss :\n        Loss function\n    optimizer :\n        Optimizer\n    \"\"\"\n    if kind == 'shallow':\n        model = ShallowFBCSPNet(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            n_filters_time=40, filter_time_length=25, n_filters_spat=40,\n            pool_time_length=75, pool_time_stride=15, final_conv_length='auto',\n            split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,\n            drop_prob=0.5)\n    elif kind == 'deep':\n        model = Deep4Net(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            final_conv_length='auto', n_filters_time=25, n_filters_spat=25,\n            filter_time_length=10, pool_time_length=3, pool_time_stride=3,\n            n_filters_2=50, filter_length_2=10, n_filters_3=100,\n            filter_length_3=10, n_filters_4=200, filter_length_4=10,\n            first_pool_mode=\"max\", later_pool_mode=\"max\", drop_prob=0.5,\n            double_time_convs=False, split_first_layer=True, batch_norm=True,\n            batch_norm_alpha=0.1, stride_before_pool=False)\n    else:\n        raise ValueError\n\n    if cuda:\n        model.cuda()\n\n    optimizer = optim.Adam(model.parameters())\n    loss = nn.NLLLoss()\n\n    return model, loss, optimizer\n\n\ndef run_training(model, dataloader, loss, optimizer, n_epochs=1, cuda=False):\n    \"\"\"Run training loop.\n\n    Parameters\n    ----------\n    model : torch.nn.Module\n        Model to train.\n    dataloader : torch.utils.data.Dataloader\n        Data loader which will serve examples to the model during training.\n    loss :\n        Loss function.\n    optimizer :\n        Optimizer.\n    n_epochs : int\n        Number of epochs to train the model for.\n    cuda : bool\n        If True, move X and y to CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Trained model.\n    \"\"\"\n    for i in range(n_epochs):\n        loss_vals = list()\n        for X, y, _ in dataloader:\n            model.train()\n            model.zero_grad()\n\n            y = y.long()\n            if cuda:\n                X, y = X.cuda(), y.cuda()\n\n            loss_val = loss(model(X), y)\n            loss_vals.append(loss_val.item())\n\n            loss_val.backward()\n            optimizer.step()\n\n        print(f'Epoch {i + 1} - mean training loss: {np.mean(loss_vals)}')\n\n    return model"
+        "def load_example_data(preload, window_len_s, n_recordings=10):\n    \"\"\"Create windowed dataset from subjects of the TUH Abnormal dataset.\n\n    Parameters\n    ----------\n    preload: bool\n        If True, use eager loading, otherwise use lazy loading.\n    window_len_s: int\n        Window length in seconds.\n    n_recordings: list of int\n        Number of recordings to load.\n\n    Returns\n    -------\n    windows_ds: BaseConcatDataset\n        Windowed data.\n\n    .. warning::\n        The recordings from the TUH Abnormal corpus do not all share the same\n        sampling rate. The following assumes that the files have already been\n        resampled to a common sampling rate.\n    \"\"\"\n\n    recording_ids = list(range(n_recordings))\n\n    ds = TUHAbnormal(\n        TUH_PATH, recording_ids=recording_ids,\n        target_name='pathological',\n        preload=preload)\n\n    fs = ds.datasets[0].raw.info['sfreq']\n    window_len_samples = int(fs * window_len_s)\n    window_stride_samples = int(fs * 4)\n    # window_stride_samples = int(fs * window_len_s)\n    windows_ds = create_fixed_length_windows(\n        ds, start_offset_samples=0, stop_offset_samples=None,\n        window_size_samples=window_len_samples,\n        window_stride_samples=window_stride_samples, drop_last_window=True,\n        preload=preload)\n\n    # Drop bad epochs\n    # XXX: This could be parallelized.\n    # XXX: Also, this could be implemented in the Dataset object itself.\n    for ds in windows_ds.datasets:\n        ds.windows.drop_bad()\n        assert ds.windows.preload == preload\n\n    return windows_ds\n\n\ndef create_example_model(n_channels, n_classes, window_len_samples,\n                         kind='shallow', cuda=False):\n    \"\"\"Create model, loss and optimizer.\n\n    Parameters\n    ----------\n    n_channels : int\n        Number of channels in the input\n    n_times : int\n        Window length in the input\n    n_classes : int\n        Number of classes in the output\n    kind : str\n        'shallow' or 'deep'\n    cuda : bool\n        If True, move the model to a CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Model to train.\n    loss :\n        Loss function\n    optimizer :\n        Optimizer\n    \"\"\"\n    if kind == 'shallow':\n        model = ShallowFBCSPNet(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            n_filters_time=40, filter_time_length=25, n_filters_spat=40,\n            pool_time_length=75, pool_time_stride=15, final_conv_length='auto',\n            split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,\n            drop_prob=0.5)\n    elif kind == 'deep':\n        model = Deep4Net(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            final_conv_length='auto', n_filters_time=25, n_filters_spat=25,\n            filter_time_length=10, pool_time_length=3, pool_time_stride=3,\n            n_filters_2=50, filter_length_2=10, n_filters_3=100,\n            filter_length_3=10, n_filters_4=200, filter_length_4=10,\n            first_pool_mode=\"max\", later_pool_mode=\"max\", drop_prob=0.5,\n            double_time_convs=False, split_first_layer=True, batch_norm=True,\n            batch_norm_alpha=0.1, stride_before_pool=False)\n    else:\n        raise ValueError\n\n    if cuda:\n        model.cuda()\n\n    optimizer = optim.Adam(model.parameters())\n    loss = nn.NLLLoss()\n\n    return model, loss, optimizer\n\n\ndef run_training(model, dataloader, loss, optimizer, n_epochs=1, cuda=False):\n    \"\"\"Run training loop.\n\n    Parameters\n    ----------\n    model : torch.nn.Module\n        Model to train.\n    dataloader : torch.utils.data.Dataloader\n        Data loader which will serve examples to the model during training.\n    loss :\n        Loss function.\n    optimizer :\n        Optimizer.\n    n_epochs : int\n        Number of epochs to train the model for.\n    cuda : bool\n        If True, move X and y to CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Trained model.\n    \"\"\"\n    for i in range(n_epochs):\n        loss_vals = list()\n        for X, y, _ in dataloader:\n            model.train()\n            model.zero_grad()\n\n            y = y.long()\n            if cuda:\n                X, y = X.cuda(), y.cuda()\n\n            loss_val = loss(model(X), y)\n            loss_vals.append(loss_val.item())\n\n            loss_val.backward()\n            optimizer.step()\n\n        print(f'Epoch {i + 1} - mean training loss: {np.mean(loss_vals)}')\n\n    return model"
       ]
     },
     {
@@ -186,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/9f3a9058fba292cd75e5ce23b492bc0c/plot_data_augmentation_search.py b/0.8/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py
similarity index 75%
rename from stable/_downloads/9f3a9058fba292cd75e5ce23b492bc0c/plot_data_augmentation_search.py
rename to 0.8/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py
index 2be589db..6fe716a8 100644
--- a/stable/_downloads/9f3a9058fba292cd75e5ce23b492bc0c/plot_data_augmentation_search.py
+++ b/0.8/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py
@@ -4,7 +4,7 @@
 
 This tutorial shows how to search data augmentations using braindecode.
 Indeed, it is known that the best augmentation to use often dependent on the task
-or phenomenon studied. Here we follow the methodology proposed in [1] on the
+or phenomenon studied. Here we follow the methodology proposed in [1]_ on the
 openly available BCI IV 2a Dataset.
 
 
@@ -18,23 +18,11 @@
     view of the data, both with pretext tasks and contrastive learning [2]_.
 
 
-Both approaches demand an intense comparison to find the best fit with the data.
-This view is supported by Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022),
-who demonstrate the importance of the selection the right transformation and
-strength for each different type of task considered.
-Here, we use the augmentation module present in braindecode in the context of
-trialwise decoding with the BCI IV 2a dataset.
-
-References
------------
-
-.. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)
-       Data augmentation for learning predictive models on EEG:
-       a systematic comparison. https://arxiv.org/abs/2206.14483
-
-.. [2] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2021).
-       Uncovering the structure of clinical EEG signals with self-supervised learning.
-       Journal of Neural Engineering, 18(4), 046020.
+Data augmentation and self-supervised learning approaches demand an intense comparison
+to find the best fit with the data. This view is demonstrated in [1]_ and shows the
+importance of selecting the right transformation and strength for different type of
+task considered. Here, we use the augmentation module present in braindecode in
+the context of trialwise decoding with the BCI IV 2a dataset.
 
 .. contents:: This example covers:
    :local:
@@ -52,6 +40,11 @@
 #
 # Loading
 # ~~~~~~~
+#
+# First, we load the data. In this tutorial, we use the functionality of braindecode
+# to load BCI IV competition dataset 1. The dataset is available on the BNCI website.
+# There is 9 subjects recorded with 22 electrodes while doing a motor imagery task,
+# with 144 trials per class. We will load this dataset through the MOABB library.
 
 from skorch.callbacks import LRScheduler
 
@@ -65,6 +58,8 @@
 # Preprocessing
 # ~~~~~~~~~~~~~
 #
+# We apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related
+# brain activity
 
 from braindecode.preprocessing import (
     exponential_moving_standardize, preprocess, Preprocessor)
@@ -78,6 +73,12 @@
 # Factor to convert from V to uV
 factor = 1e6
 
+######################################################################
+# In time series targets setup, targets variables are stored in mne.Raw object as channels
+# of type `misc`. Thus those channels have to be selected for further processing. However,
+# many mne functions ignore `misc` channels and perform operations only on data channels
+# (see https://mne.tools/stable/glossary.html#term-data-channels).
+
 preprocessors = [
     Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
     Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
@@ -86,12 +87,16 @@
                  factor_new=factor_new, init_block_size=init_block_size)
 ]
 
-preprocess(dataset, preprocessors)
+preprocess(dataset, preprocessors, n_jobs=-1)
 
 ######################################################################
 # Extracting windows
 # ~~~~~~~~~~~~~~~~~~
 #
+# Now we cut out compute windows, the inputs for the deep networks during
+# training. We use the braindecode function for this, provinding parameters
+# to define how trials should be used.
+
 
 from braindecode.preprocessing import create_windows_from_events
 from skorch.helper import SliceDataset
@@ -104,8 +109,6 @@
 # Calculate the trial start offset in samples.
 trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
 
-# Create windows using braindecode function for this. It needs parameters to
-# define how trials should be used.
 windows_dataset = create_windows_from_events(
     dataset,
     trial_start_offset_samples=trial_start_offset_samples,
@@ -116,10 +119,12 @@
 ######################################################################
 # Split dataset into train and valid
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# Following the rules of the BCI competition
+# Following the split defined in the BCI competition
+
+
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-eval_set = splitted['session_E']
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Defining a list of transforms
@@ -127,11 +132,14 @@
 #
 # In this tutorial, we will use three categories of augmentations.
 # This categorization has been proposed by [1]_ to explain and aggregate
-# the several possibilities of augmentations in EEG, being them: a) Frequency domain
-# augmentations, b) Time domain augmentations, and c) Spatial domain augmentations.
+# the several possibilities of augmentations in EEG, being them:
 #
-# From this same paper, we selected the best augmentations in each type: FTSurrogate,
-# SmoothTimeMask, ChannelsDropout, respectively.
+# a) Frequency domain augmentations,
+# b) Time domain augmentations,
+# c) Spatial domain augmentations.
+#
+# From this same paper, we selected the best augmentations in each type: ``FTSurrogate``,
+# ``SmoothTimeMask``, ``ChannelsDropout``, respectively.
 #
 # For each augmentation, we adjustable two values from a range for one parameter
 # inside the transformation.
@@ -177,17 +185,20 @@
 if cuda:
     torch.backends.cudnn.benchmark = True
 
+
+######################################################################
 # Set random seed to be able to roughly reproduce results
 # Note that with cudnn benchmark set to True, GPU indeterminism
 # may still make results substantially different between runs.
 # To obtain more consistent results at the cost of increased computation time,
-# you can set `cudnn_benchmark=False` in `set_random_seeds`
-# or remove `torch.backends.cudnn.benchmark = True`
+# you can set ``cudnn_benchmark=False`` in ``set_random_seeds``
+# or remove ``torch.backends.cudnn.benchmark = True``
+
 seed = 20200220
 set_random_seeds(seed=seed, cuda=cuda)
 
 n_classes = 4
-
+classes = list(range(n_classes))
 # Extract number of chans and time steps from dataset
 n_channels = train_set[0][0].shape[0]
 input_window_samples = train_set[0][0].shape[1]
@@ -222,7 +233,7 @@
 weight_decay = 0
 
 batch_size = 64
-n_epochs = 4
+n_epochs = 2
 
 clf = EEGClassifier(
     model,
@@ -239,6 +250,7 @@
         ('lr_scheduler', LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes
 )
 
 #####################################################################
@@ -304,3 +316,29 @@
 eval_y = SliceDataset(eval_set, idx=1)
 score = search.score(eval_X, eval_y)
 print(f'Eval accuracy is {score * 100:.2f}%.')
+
+######################################################################
+# Plot results
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+search_results.plot.bar(
+    x="param_iterator_train__transforms", y="mean_train_score", yerr="std_train_score",
+    rot=45, color=["C0", "C0", "C1", "C1", "C2", "C2"], legend=None, ax=ax)
+ax.set_xlabel("Data augmentation strategy")
+ax.set_ylim(0.2, 0.32)
+plt.tight_layout()
+
+######################################################################
+# References
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+#
+# .. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)
+#        Data augmentation for learning predictive models on EEG:
+#        a systematic comparison. https://arxiv.org/abs/2206.14483
+# .. [2] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2021).
+#        Uncovering the structure of clinical EEG signals with self-supervised learning.
+#        Journal of Neural Engineering, 18(4), 046020.
diff --git a/stable/_downloads/22c027ac1764bd1e50c7597f407c49bb/plot_bcic_iv_4_ecog_cropped.ipynb b/0.8/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb
similarity index 75%
rename from stable/_downloads/22c027ac1764bd1e50c7597f407c49bb/plot_bcic_iv_4_ecog_cropped.ipynb
rename to 0.8/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb
index 1c4c4767..ee5fde48 100644
--- a/stable/_downloads/22c027ac1764bd1e50c7597f407c49bb/plot_bcic_iv_4_ecog_cropped.ipynb
+++ b/0.8/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -80,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
+        "from braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
       ]
     },
     {
@@ -98,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "valid_set = preprocess(copy.deepcopy(train_set), [Preprocessor('crop', tmin=24, tmax=30)])\npreprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)])\npreprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)])"
+        "valid_set = preprocess(copy.deepcopy(train_set),\n                       [Preprocessor('crop', tmin=24, tmax=30)], n_jobs=-1)\npreprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)\npreprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)"
       ]
     },
     {
@@ -152,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 1\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0] - 5\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    final_conv_length=2,\n)\n# We are removing the softmax layer to make it a regression model\nnew_model = torch.nn.Sequential()\nfor name, module_ in model.named_children():\n    if \"softmax\" in name:\n        continue\n    new_model.add_module(name, module_)\nmodel = new_model\n\n# Send model to GPU\nif cuda:\n    model.cuda()\n\nfrom braindecode.models import to_dense_prediction_model, get_output_shape\n\nto_dense_prediction_model(model)"
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 1\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0] - 5\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    final_conv_length=2,\n    add_log_softmax=False,\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()\n\nfrom braindecode.models import get_output_shape, to_dense_prediction_model\n\nto_dense_prediction_model(model)"
       ]
     },
     {
@@ -224,7 +213,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode.training import TimeSeriesLoss\nfrom braindecode import EEGRegressor\nfrom braindecode.training import CroppedTimeSeriesEpochScoring\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 27  # only 27 examples in train set, otherwise set to 64\nn_epochs = 8\n\nregressor = EEGRegressor(\n    model,\n    cropped=True,\n    aggregate_predictions=False,\n    criterion=TimeSeriesLoss,\n    criterion__loss_function=torch.nn.functional.mse_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n        ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=True,\n                                                   name='r2_train')\n         ),\n        ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=False,\n                                                   name='r2_valid')\n         )\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGRegressor\nfrom braindecode.training import CroppedTimeSeriesEpochScoring, TimeSeriesLoss\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 27  # only 27 examples in train set, otherwise set to 64\nn_epochs = 8\n\nregressor = EEGRegressor(\n    model,\n    cropped=True,\n    aggregate_predictions=False,\n    criterion=TimeSeriesLoss,\n    criterion__loss_function=torch.nn.functional.mse_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n        ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=True,\n                                                   name='r2_train')\n         ),\n        ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=False,\n                                                   name='r2_valid')\n         )\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
       ]
     },
     {
@@ -274,7 +263,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We plot target and predicted finger flexion on training, validation, adn test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time neded to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
+        "We plot target and predicted finger flexion on training, validation, and test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time needed to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
       ]
     },
     {
@@ -285,7 +274,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport pandas as pd\n\nplt.style.use('seaborn')\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
       ]
     },
     {
@@ -341,7 +330,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py b/0.8/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py
new file mode 100644
index 00000000..5101398f
--- /dev/null
+++ b/0.8/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py
@@ -0,0 +1,257 @@
+"""
+Convolutional neural network regression model on fake data.
+===========================================================
+
+This example shows how to create a CNN regressor from a CNN classifier by removing `softmax`
+function from the classifier's output layer and how to train it on a fake regression dataset.
+
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#          Sara Sedlar <sara.sedlar@gmail.com>
+# License: BSD-3
+
+###################################################################################################
+# Fake regression data
+# --------------------
+# Function for generation of the fake regression dataset generates `n_fake_recs` recordings,
+# each containing sinusoidal signals with Gaussian noise. Each fake recording signal has
+# `n_fake_chs` channels, it lasts `fake_duration` [s] and it is sampled with `fake_sfreq` [Hz].
+# The recordings are split into train, validation and testing sessions.
+
+import numpy as np
+import pandas as pd
+
+from braindecode.datasets import BaseConcatDataset, BaseDataset
+from braindecode.util import create_mne_dummy_raw
+
+
+###################################################################################################
+# Function for generating fake regression data
+def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq,
+                            fake_duration, n_fake_targets,
+                            fake_data_split=[0.6, 0.2, 0.2]):
+    """Generate a fake regression dataset.
+
+    Parameters
+    ----------
+    n_fake_recs : int
+        Number of fake recordings.
+    n_fake_chs : int
+        Number of fake EEG channels.
+    fake_sfreq : float
+        Fake sampling frequency in Hz.
+    fake_duration : float
+        Fake recording duration in seconds.
+    n_fake_targets : int
+        Number of targets.
+    fake_data_split : list
+        List of train/valid/test subset fractions.
+
+    Returns
+    -------
+    dataset : BaseConcatDataset object
+        The generated dataset object.
+    """
+
+    datasets = []
+    for i in range(n_fake_recs):
+        if i < int(fake_data_split[0] * n_fake_recs):
+            target_subset = "train"
+        elif i < int((1 - fake_data_split[2]) * n_fake_recs):
+            target_subset = "valid"
+        else:
+            target_subset = "test"
+        raw, _ = create_mne_dummy_raw(n_channels=n_fake_chs,
+                                      n_times=fake_duration * fake_sfreq,
+                                      sfreq=fake_sfreq)
+
+        target = np.random.randint(0, 10, n_fake_targets)
+        for j in range(n_fake_targets):
+            x = np.sin(2 * np.pi * target[j] * raw.times)
+            raw._data += np.expand_dims(x, axis=0)
+
+        if n_fake_targets == 1:
+            target = target[0]
+        fake_description = pd.Series(data=[target, target_subset],
+                                     index=["target", "session"])
+        datasets.append(
+            BaseDataset(raw, fake_description, target_name="target"))
+
+    return BaseConcatDataset(datasets)
+
+
+####################################################################################################
+# Generating fake regression dataset
+# -----------------------------------
+#
+#
+n_fake_rec = 20
+n_fake_chans = 21
+fake_sfreq = 100
+fake_duration = 30
+n_fake_targets = 1
+dataset = fake_regression_dataset(n_fake_recs=n_fake_rec,
+                                  n_fake_chs=n_fake_chans,
+                                  fake_sfreq=fake_sfreq,
+                                  fake_duration=fake_duration,
+                                  n_fake_targets=n_fake_targets)
+
+###################################################################################################
+# Defining a CNN regression model
+# -------------------------------
+#
+# Choosing and defining a CNN classifier, `ShallowFBCSPNet` or `Deep4Net`, introduced in [1]_.
+# To convert a classifier to a regressor, `softmax` function is removed from its output layer.
+from braindecode.util import set_random_seeds
+from braindecode.models import Deep4Net
+from braindecode.models import ShallowFBCSPNet
+import torch
+
+# Choosing a CNN model
+model_name = "shallow"  # 'shallow' or 'deep'
+
+# Defining a CNN model
+if model_name in ["shallow", "Shallow", "ShallowConvNet"]:
+    model = ShallowFBCSPNet(in_chans=n_fake_chans,
+                            n_classes=n_fake_targets,
+                            input_window_samples=fake_sfreq * fake_duration,
+                            n_filters_time=40, n_filters_spat=40,
+                            final_conv_length=35,
+                            add_log_softmax=False,)
+elif model_name in ["deep", "Deep", "DeepConvNet"]:
+    model = Deep4Net(in_chans=n_fake_chans, n_classes=n_fake_targets,
+                     input_window_samples=fake_sfreq * fake_duration,
+                     n_filters_time=25, n_filters_spat=25,
+                     stride_before_pool=True,
+                     n_filters_2=n_fake_chans * 2,
+                     n_filters_3=n_fake_chans * 4,
+                     n_filters_4=n_fake_chans * 8,
+                     final_conv_length=1,
+                     add_log_softmax=False, )
+else:
+    raise ValueError(f'{model_name} unknown')
+
+###################################################################################################
+# Choosing between GPU and CPU processors
+# ---------------------------------------
+# By default, model's training and evaluation take place at GPU if it exists, otherwise on CPU.
+cuda = torch.cuda.is_available()
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+
+# Setting a random seed
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+if cuda:
+    model.cuda()
+
+###################################################################################################
+# Data windowing
+# ----------------
+# Windowing data with a sliding window into the epochs of the size `window_size_samples`.
+from braindecode.models.util import to_dense_prediction_model, get_output_shape
+from braindecode.preprocessing import create_fixed_length_windows
+
+window_size_samples = fake_sfreq * fake_duration // 3
+to_dense_prediction_model(model)
+n_preds_per_input = get_output_shape(model, n_fake_chans, window_size_samples)[
+    2]
+windows_dataset = create_fixed_length_windows(dataset,
+                                              start_offset_samples=0,
+                                              stop_offset_samples=0,
+                                              window_size_samples=window_size_samples,
+                                              window_stride_samples=n_preds_per_input,
+                                              drop_last_window=False,
+                                              preload=True)
+
+# Splitting windowed data into train, valid and test subsets.
+splits = windows_dataset.split("session")
+train_set = splits["train"]
+valid_set = splits["valid"]
+test_set = splits["test"]
+
+###################################################################################################
+# Model training
+# -----------------
+# Model is trained by minimizing MSE loss between ground truth and estimated value averaged over
+# a period of time using AdamW optimizer [2]_, [3]_. Learning rate is managed by CosineAnnealingLR
+# learning rate scheduler.
+from braindecode import EEGRegressor
+from braindecode.training.losses import CroppedLoss
+from skorch.callbacks import LRScheduler
+from skorch.helper import predefined_split
+
+batch_size = 4
+n_epochs = 3
+optimizer_lr = 0.001
+optimizer_weight_decay = 0.0
+regressor = EEGRegressor(model, cropped=True,
+                         criterion=CroppedLoss,
+                         criterion__loss_function=torch.nn.functional.mse_loss,
+                         optimizer=torch.optim.AdamW,
+                         optimizer__lr=optimizer_lr,
+                         optimizer__weight_decay=optimizer_weight_decay,
+                         train_split=predefined_split(valid_set),
+                         iterator_train__shuffle=True,
+                         batch_size=batch_size,
+                         callbacks=["neg_root_mean_squared_error",
+                                    ("lr_scheduler",
+                                     LRScheduler('CosineAnnealingLR',
+                                                 T_max=n_epochs - 1))],
+                         device=device, )
+regressor.fit(train_set, y=None, epochs=n_epochs)
+
+###################################################################################################
+# Model evaluation
+# -----------------
+# Plotting training and validation losses and negative root mean square error
+import matplotlib.pyplot as plt
+
+
+fig, axes = plt.subplots(1, 2, figsize=(10, 5))
+axes[0].set_title("Train and valid losses")
+axes[0].plot(regressor.history[:, "train_loss"])
+axes[0].plot(regressor.history[:, "valid_loss"])
+axes[0].set_xlabel("Epochs")
+axes[0].set_ylabel("Cropped MSE loss")
+axes[0].legend(["Train", "Valid"])
+
+axes[1].set_title("Train and valid errors")
+axes[1].plot(regressor.history[:, "train_neg_root_mean_squared_error"])
+axes[1].plot(regressor.history[:, "valid_neg_root_mean_squared_error"])
+axes[1].set_xlabel("Epochs")
+axes[1].set_ylabel("Negative RMSE")
+axes[1].legend(["Train", "Valid"])
+
+###################################################################################################
+# Model testing
+# -----------------
+# Plotting a scatter plot of estimated versus target values and corresponding trend line.
+fig, axes = plt.subplots(1, 1, figsize=(5, 5))
+y_estim = np.ravel(regressor.predict(test_set))
+y_gt = test_set.get_metadata()["target"].to_numpy()
+
+_ = axes.scatter(y_gt, y_estim)
+_ = axes.set_ylabel("Estimated targets.")
+_ = axes.set_xlabel("Ground truth targets.")
+
+z = np.polyfit(y_gt, y_estim, 1)
+p = np.poly1d(z)
+plt.plot(y_gt, p(y_gt), "r--")
+plt.show()
+###################################################################################################
+# References
+# ----------
+#
+# .. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Human brain mapping, 38(11), 5391-5420.
+#
+# .. [2] Kingma, Diederik P., and Jimmy Ba.
+#        "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
+#
+# .. [3] Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.
+#        "On the convergence of adam and beyond." arXiv preprint arXiv:1904.09237 (2019).
diff --git a/stable/_downloads/5ae34d87007c72630b1b1fd61f73ee5d/plot_benchmark_preprocessing.ipynb b/0.8/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb
similarity index 96%
rename from stable/_downloads/5ae34d87007c72630b1b1fd61f73ee5d/plot_benchmark_preprocessing.ipynb
rename to 0.8/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb
index 7bb6a3ba..58a1041a 100644
--- a/stable/_downloads/5ae34d87007c72630b1b1fd61f73ee5d/plot_benchmark_preprocessing.ipynb
+++ b/0.8/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -62,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "n_repets = 3  # Number of repetitions\nall_n_recs = 2  # Number of recordings to load and preprocess\nall_n_jobs = [1, 2]  # Number of parallel processes\n\nresults = list()\nfor _, n_recs, save, n_jobs in product(\n        range(n_repets), [all_n_recs], [True, False], all_n_jobs):\n\n    start = time.time()\n    mem = max(memory_usage(\n        proc=(prepare_data, [n_recs, save, False, n_jobs], {})))\n    time_taken = time.time() - start\n\n    results.append({\n        'n_recs': n_recs,\n        'max_mem': mem,\n        'save': save,\n        'n_jobs': n_jobs,\n        'time': time_taken\n    })"
+        "n_repets = 2  # Number of repetitions\nall_n_recs = 2  # Number of recordings to load and preprocess\nall_n_jobs = [1, 2]  # Number of parallel processes\n\nresults = list()\nfor _, n_recs, save, n_jobs in product(\n        range(n_repets), [all_n_recs], [True, False], all_n_jobs):\n\n    start = time.time()\n    mem = max(memory_usage(\n        proc=(prepare_data, [n_recs, save, False, n_jobs], {})))\n    time_taken = time.time() - start\n\n    results.append({\n        'n_recs': n_recs,\n        'max_mem': mem,\n        'save': save,\n        'n_jobs': n_jobs,\n        'time': time_taken\n    })"
       ]
     },
     {
@@ -107,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/8907c83db580c626a4858d4f3c3d5c35/plot_data_augmentation.ipynb b/0.8/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb
similarity index 85%
rename from stable/_downloads/8907c83db580c626a4858d4f3c3d5c35/plot_data_augmentation.ipynb
rename to 0.8/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb
index 4650f405..6b2da8ca 100644
--- a/stable/_downloads/8907c83db580c626a4858d4f3c3d5c35/plot_data_augmentation.ipynb
+++ b/0.8/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -62,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors, n_jobs=-1)"
       ]
     },
     {
@@ -98,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "splitted = windows_dataset.split('session')\ntrain_set = splitted['session_T']\nvalid_set = splitted['session_E']"
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
       ]
     },
     {
@@ -134,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\n\nepochs = train_set.datasets[0].windows  # original epochs\nX = epochs.get_data()\n# This allows to apply the transform with a fixed shift (10 Hz) for\n# visualization instead of sampling the shift randomly between -2 and 2 Hz\nX_tr, _ = transform.operation(torch.as_tensor(X).float(), None, 10., sfreq)"
+        "import torch\nimport numpy as np\n\nX = np.stack([X for X, y, i in train_set.datasets[0]])\n# This allows to apply the transform with a fixed shift (10 Hz) for\n# visualization instead of sampling the shift randomly between -2 and 2 Hz\nX_tr, _ = transform.operation(torch.as_tensor(X).float(), None, 10., sfreq)"
       ]
     },
     {
@@ -152,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "import mne\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef plot_psd(data, axis, label, color):\n    psds, freqs = mne.time_frequency.psd_array_multitaper(data, sfreq=sfreq,\n                                                          fmin=0.1, fmax=100)\n    psds = 10. * np.log10(psds)\n    psds_mean = psds.mean(0).mean(0)\n    axis.plot(freqs, psds_mean, color=color, label=label)\n\n\n_, ax = plt.subplots()\nplot_psd(X, ax, 'original', 'k')\nplot_psd(X_tr.numpy(), ax, 'shifted', 'r')\n\nax.set(title='Multitaper PSD (gradiometers)', xlabel='Frequency (Hz)',\n       ylabel='Power Spectral Density (dB)')\nax.legend()\nplt.show()"
+        "import mne\nimport matplotlib.pyplot as plt\n\n\ndef plot_psd(data, axis, label, color):\n    psds, freqs = mne.time_frequency.psd_array_multitaper(data, sfreq=sfreq,\n                                                          fmin=0.1, fmax=100)\n    psds = 10. * np.log10(psds)\n    psds_mean = psds.mean(0).mean(0)\n    axis.plot(freqs, psds_mean, color=color, label=label)\n\n\n_, ax = plt.subplots()\nplot_psd(X, ax, 'original', 'k')\nplot_psd(X_tr.numpy(), ax, 'shifted', 'r')\n\nax.set(title='Multitaper PSD (gradiometers)', xlabel='Frequency (Hz)',\n       ylabel='Power Spectral Density (dB)')\nax.legend()\nplt.show()"
       ]
     },
     {
@@ -170,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
+        "from braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
       ]
     },
     {
@@ -206,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=transforms,  # This sets the augmentations to use\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=transforms,  # This sets the augmentations to use\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
       ]
     },
     {
@@ -262,7 +251,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/763a361b3d3c09298e3afb84a82726b5/plot_sleep_staging_eldele2021.py b/0.8/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py
similarity index 81%
rename from stable/_downloads/763a361b3d3c09298e3afb84a82726b5/plot_sleep_staging_eldele2021.py
rename to 0.8/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py
index 8be139c5..800e73d9 100644
--- a/stable/_downloads/763a361b3d3c09298e3afb84a82726b5/plot_sleep_staging_eldele2021.py
+++ b/0.8/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py
@@ -6,27 +6,6 @@
 Braindecode. We use the attention-based model from [1]_ with the time distributed approach of [2]_
 to learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset [3]_ [4]_.
 
-References
-----------
-.. [1] E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage
-        Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and
-        Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
-
-.. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
-      (2018)A Deep Learning Architecture for Temporal Sleep Stage
-      Classification Using Multivariate and Multimodal Time Series.
-      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
-      (758-769)
-
-.. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-       a sleep-dependent neuronal feedback loop: the slow-wave
-       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
-
-.. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-       Research Resource for Complex Physiologic Signals.
-       Circulation 101(23):e215-e220
 """
 # Authors: Divyesh Narayanan <divyesh.narayanan@gmail.com>
 #
@@ -73,7 +52,7 @@
 factor = 1e6
 
 preprocessors = [
-    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
 ]
 
@@ -156,14 +135,15 @@
 n_windows = 3  # Sequences of 3 consecutive windows
 n_windows_stride = 3  # Maximally overlapping sequences
 
-train_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
 valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
 
 # Print number of examples per class
 print('Training examples: ', len(train_sampler))
 print('Validation examples: ', len(valid_sampler))
 
-
 ######################################################################
 # We also implement a transform to extract the label of the center window of a
 # sequence to use it as target.
@@ -221,9 +201,10 @@ def get_center_label(x):
 
 feat_extractor = SleepStagerEldele2021(
     sfreq,
-    n_classes=n_classes,
-    input_size_s=input_size_samples / sfreq,
-    return_feats=True)
+    n_outputs=n_classes,
+    n_times=input_size_samples,
+    return_feats=True,
+)
 
 model = nn.Sequential(
     TimeDistributed(feat_extractor),  # apply model on each 30-s window
@@ -238,7 +219,6 @@ def get_center_label(x):
 if cuda:
     model.cuda()
 
-
 ######################################################################
 # Training
 # --------
@@ -281,13 +261,13 @@ def get_center_label(x):
     optimizer__lr=lr,
     batch_size=batch_size,
     callbacks=callbacks,
-    device=device
+    device=device,
+    classes=np.unique(y_train)
 )
 # Model training for a specified number of epochs. `y` is None as it is already
 # supplied in the dataset.
 clf.fit(train_set, y=None, epochs=n_epochs)
 
-
 ######################################################################
 # Plot results
 # ------------
@@ -317,13 +297,31 @@ def get_center_label(x):
 #
 
 from sklearn.metrics import confusion_matrix, classification_report
+from braindecode.visualization import plot_confusion_matrix
 
 y_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]
 y_pred = clf.predict(valid_set)
 
-print(confusion_matrix(y_true, y_pred))
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+plot_confusion_matrix(confusion_mat=confusion_mat)
+#                      class_names=['Wake', 'N1', 'N2', 'N3', 'N4', 'REM'])
+
 print(classification_report(y_true, y_pred))
 
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true, color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
 
 ######################################################################
 # The model was able to learn despite the low amount of data that was available
@@ -338,3 +336,27 @@ def get_center_label(x):
 #    accuracy on training data and around 43% on held out validation data.
 #    Increasing the number of training recordings and optimizing the hyperparameters
 #    will also help increase performance
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage
+#         Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and
+#         Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
+#
+# .. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
+#       Classification Using Multivariate and Multimodal Time Series.
+#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+#       (758-769)
+#
+# .. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/a90627a2f09081df5abbe979f9fa0217/plot_data_augmentation_search.ipynb b/0.8/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb
similarity index 61%
rename from stable/_downloads/a90627a2f09081df5abbe979f9fa0217/plot_data_augmentation_search.ipynb
rename to 0.8/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb
index 5308f5bd..333cf35c 100644
--- a/stable/_downloads/a90627a2f09081df5abbe979f9fa0217/plot_data_augmentation_search.ipynb
+++ b/0.8/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb
@@ -1,5 +1,12 @@
 {
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Searching the best data augmentation on BCIC IV 2a Dataset\n\nThis tutorial shows how to search data augmentations using braindecode.\nIndeed, it is known that the best augmentation to use often dependent on the task\nor phenomenon studied. Here we follow the methodology proposed in [1]_ on the\nopenly available BCI IV 2a Dataset.\n\n\n.. topic:: Data Augmentation\n\n    Data augmentation could be a step in training deep learning models.\n    For decoding brain signals, recent studies have shown that artificially\n    generating samples may increase the final performance of a deep learning model [1]_.\n    Other studies have shown that data augmentation can be used to cast\n    a self-supervised paradigm, presenting a more diverse\n    view of the data, both with pretext tasks and contrastive learning [2]_.\n\n\nData augmentation and self-supervised learning approaches demand an intense comparison\nto find the best fit with the data. This view is demonstrated in [1]_ and shows the\nimportance of selecting the right transformation and strength for different type of\ntask considered. Here, we use the augmentation module present in braindecode in\nthe context of trialwise decoding with the BCI IV 2a dataset.\n   :depth: 2\n"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -8,14 +15,14 @@
       },
       "outputs": [],
       "source": [
-        "%matplotlib inline"
+        "# Authors: Bruno Aristimunha <a.bruno@ufabc.edu.br>\n#          C\u00e9dric Rommel <cedric.rommel@inria.fr>\n# License: BSD (3-clause)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Searching the best data augmentation on BCIC IV 2a Dataset\n\nThis tutorial shows how to search data augmentations using braindecode.\nIndeed, it is known that the best augmentation to use often dependent on the task\nor phenomenon studied. Here we follow the methodology proposed in [1] on the\nopenly available BCI IV 2a Dataset.\n\n\n.. topic:: Data Augmentation\n\n    Data augmentation could be a step in training deep learning models.\n    For decoding brain signals, recent studies have shown that artificially\n    generating samples may increase the final performance of a deep learning model [1]_.\n    Other studies have shown that data augmentation can be used to cast\n    a self-supervised paradigm, presenting a more diverse\n    view of the data, both with pretext tasks and contrastive learning [2]_.\n\n\nBoth approaches demand an intense comparison to find the best fit with the data.\nThis view is supported by Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022),\nwho demonstrate the importance of the selection the right transformation and\nstrength for each different type of task considered.\nHere, we use the augmentation module present in braindecode in the context of\ntrialwise decoding with the BCI IV 2a dataset.\n\n## References\n\n.. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)\n       Data augmentation for learning predictive models on EEG:\n       a systematic comparison. https://arxiv.org/abs/2206.14483\n\n.. [2] Banville, H., Chehab, O., Hyv\u00e4rinen, A., Engemann, D. A., & Gramfort, A. (2021).\n       Uncovering the structure of clinical EEG signals with self-supervised learning.\n       Journal of Neural Engineering, 18(4), 046020.\n   :depth: 2\n"
+        "## Loading and preprocessing the dataset\n\n### Loading\n\nFirst, we load the data. In this tutorial, we use the functionality of braindecode\nto load BCI IV competition dataset 1. The dataset is available on the BNCI website.\nThere is 9 subjects recorded with 22 electrodes while doing a motor imagery task,\nwith 144 trials per class. We will load this dataset through the MOABB library.\n\n"
       ]
     },
     {
@@ -26,14 +33,14 @@
       },
       "outputs": [],
       "source": [
-        "# Authors: Bruno Aristimunha <a.bruno@ufabc.edu.br>\n#          C\u00e9dric Rommel <cedric.rommel@inria.fr>\n# License: BSD (3-clause)"
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Loading and preprocessing the dataset\n\n### Loading\n\n"
+        "### Preprocessing\n\nWe apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related\nbrain activity\n\n"
       ]
     },
     {
@@ -44,14 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Preprocessing\n\n\n"
+        "In time series targets setup, targets variables are stored in mne.Raw object as channels\nof type `misc`. Thus those channels have to be selected for further processing. However,\nmany mne functions ignore `misc` channels and perform operations only on data channels\n(see https://mne.tools/stable/glossary.html#term-data-channels).\n\n"
       ]
     },
     {
@@ -62,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors)"
+        "preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors, n_jobs=-1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Extracting windows\n\n\n"
+        "### Extracting windows\n\nNow we cut out compute windows, the inputs for the deep networks during\ntraining. We use the braindecode function for this, provinding parameters\nto define how trials should be used.\n\n"
       ]
     },
     {
@@ -80,14 +87,14 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import create_windows_from_events\nfrom skorch.helper import SliceDataset\nfrom numpy import array\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to\n# define how trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+        "from braindecode.preprocessing import create_windows_from_events\nfrom skorch.helper import SliceDataset\nfrom numpy import array\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Split dataset into train and valid\nFollowing the rules of the BCI competition\n\n"
+        "### Split dataset into train and valid\nFollowing the split defined in the BCI competition\n\n"
       ]
     },
     {
@@ -98,14 +105,14 @@
       },
       "outputs": [],
       "source": [
-        "splitted = windows_dataset.split('session')\ntrain_set = splitted['session_T']\neval_set = splitted['session_E']"
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\neval_set = splitted['1test']  # Session evaluation"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Defining a list of transforms\n\nIn this tutorial, we will use three categories of augmentations.\nThis categorization has been proposed by [1]_ to explain and aggregate\nthe several possibilities of augmentations in EEG, being them: a) Frequency domain\naugmentations, b) Time domain augmentations, and c) Spatial domain augmentations.\n\nFrom this same paper, we selected the best augmentations in each type: FTSurrogate,\nSmoothTimeMask, ChannelsDropout, respectively.\n\nFor each augmentation, we adjustable two values from a range for one parameter\ninside the transformation.\n\nIt is important to remember that you can increase the range.\nFor that, we need to define three lists of transformations and range for the parameter\n\u2206\u03c6max in FTSurrogate where \u2206\u03c6max \u2208 [0, 2\u03c0); for \u2206t in SmoothTimeMask is \u2206t \u2208 [0, 2];\nFor the method ChannelsDropout, we analyse the parameter p_drop \u2208 [0, 1].\n\n"
+        "## Defining a list of transforms\n\nIn this tutorial, we will use three categories of augmentations.\nThis categorization has been proposed by [1]_ to explain and aggregate\nthe several possibilities of augmentations in EEG, being them:\n\na) Frequency domain augmentations,\nb) Time domain augmentations,\nc) Spatial domain augmentations.\n\nFrom this same paper, we selected the best augmentations in each type: ``FTSurrogate``,\n``SmoothTimeMask``, ``ChannelsDropout``, respectively.\n\nFor each augmentation, we adjustable two values from a range for one parameter\ninside the transformation.\n\nIt is important to remember that you can increase the range.\nFor that, we need to define three lists of transformations and range for the parameter\n\u2206\u03c6max in FTSurrogate where \u2206\u03c6max \u2208 [0, 2\u03c0); for \u2206t in SmoothTimeMask is \u2206t \u2208 [0, 2];\nFor the method ChannelsDropout, we analyse the parameter p_drop \u2208 [0, 1].\n\n"
       ]
     },
     {
@@ -134,7 +141,25 @@
       },
       "outputs": [],
       "source": [
-        "import torch\n\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
+        "import torch\n\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set random seed to be able to roughly reproduce results\nNote that with cudnn benchmark set to True, GPU indeterminism\nmay still make results substantially different between runs.\nTo obtain more consistent results at the cost of increased computation time,\nyou can set ``cudnn_benchmark=False`` in ``set_random_seeds``\nor remove ``torch.backends.cudnn.benchmark = True``\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "seed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
       ]
     },
     {
@@ -170,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=[],  # This sets is handled by GridSearchCV\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,  # GridSearchCV will control the split and train/validation over the dataset\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'accuracy',\n        ('lr_scheduler', LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)"
+        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=[],  # This sets is handled by GridSearchCV\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,  # GridSearchCV will control the split and train/validation over the dataset\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'accuracy',\n        ('lr_scheduler', LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes\n)"
       ]
     },
     {
@@ -226,6 +251,31 @@
       "source": [
         "import pandas as pd\nimport numpy as np\n\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results['rank_test_score'] == 1].squeeze()\nbest_aug = best_run['params']\nvalidation_score = np.around(best_run['mean_test_score'] * 100, 2).mean()\ntraining_score = np.around(best_run['mean_train_score'] * 100, 2).mean()\n\nreport_message = 'Best augmentation is saved in best_aug which gave a mean validation accuracy' + \\\n                 'of {}% (train accuracy of {}%).'.format(validation_score, training_score)\n\nprint(report_message)\n\neval_X = SliceDataset(eval_set, idx=0)\neval_y = SliceDataset(eval_set, idx=1)\nscore = search.score(eval_X, eval_y)\nprint(f'Eval accuracy is {score * 100:.2f}%.')"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nsearch_results.plot.bar(\n    x=\"param_iterator_train__transforms\", y=\"mean_train_score\", yerr=\"std_train_score\",\n    rot=45, color=[\"C0\", \"C0\", \"C1\", \"C1\", \"C2\", \"C2\"], legend=None, ax=ax)\nax.set_xlabel(\"Data augmentation strategy\")\nax.set_ylim(0.2, 0.32)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### References\n\n\n.. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)\n       Data augmentation for learning predictive models on EEG:\n       a systematic comparison. https://arxiv.org/abs/2206.14483\n.. [2] Banville, H., Chehab, O., Hyv\u00e4rinen, A., Engemann, D. A., & Gramfort, A. (2021).\n       Uncovering the structure of clinical EEG signals with self-supervised learning.\n       Journal of Neural Engineering, 18(4), 046020.\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -244,7 +294,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb b/0.8/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb
new file mode 100644
index 00000000..d84d1781
--- /dev/null
+++ b/0.8/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb
@@ -0,0 +1,375 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# How to train, test and tune your model?\n\nThis tutorial shows you how to properly train, tune and test your deep learning\nmodels with Braindecode. We will use the BCIC IV 2a dataset [1]_ as a showcase example.\n\nThe methods shown can be applied to any standard supervised trial-based decoding setting.\nThis tutorial will include additional parts of code like loading and preprocessing of data,\ndefining a model, and other details which are not exclusive to this page (see\n[Cropped Decoding Tutorial](./plot_bcic_iv_2a_moabb_cropped.html)_). Therefore we\nwill not further elaborate on these parts and you can feel free to skip them.\n\nIn general, we distinguish between \"usual\" training and evaluation and hyperparameter search.\nThe tutorial is therefore split into two parts, one for the three different training schemes\nand one for the two different hyperparameter tuning methods.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Why should I care about model evaluation?\nShort answer: To produce reliable results!\n\nIn machine learning, we usually follow the scheme of splitting the\ndata into two parts, training and testing sets. It sounds like a\nsimple division, right? But the story does not end here.\n\n- What are model's parameters?\n\nModel's parameters are learnable weights which are used in the\nextraction of the relevant features and in performing the final inference.\nIn the context of deep learning, these are usually fully connected weights,\nconvolutional kernels, biases, etc.\n\n- What are model's hyperparameters?\n\nModel's hyperparameters are used to set the capacity (size) of the model\nand to guide the parameter learning process.\nIn the context of deep learning, examples of the hyperparameters are the\nnumber of convolutional layers and the number of convolutional kernels in\neach of them, the number and size of the fully connected weights,\nchoice of the optimizer and its learning rate, the number of training epochs,\nchoice of the nonlinearities, etc.\n\n\nWhile developing a ML model you usually have to adjust and tune\nhyperparameters of your model or pipeline (e.g., number of layers,\nlearning rate, number of epochs). Deep learning models usually have\nmany free parameters; they could be considered as complex models with\nmany degrees of freedom. If you kept using the test dataset to\nevaluate your adjustment, you would run into data leakage.\n\nThis means that if you use the test set to adjust the hyperparameters\nof your model, the model implicitly learns or memorizes the test set.\nTherefore, the trained model is no longer independent of the test set\n(even though it was never used for training explicitly!).\nIf you perform any hyperparameter tuning, you need a third split,\nthe so-called validation set.\n\nThis tutorial shows the three basic schemes for training and evaluating\nthe model as well as two methods to tune your hyperparameters.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>You might recognize that the accuracy gets better throughout\n   the experiments of this tutorial. The reason behind that is that\n   we always use the same model with the same parameters in every\n   segment to keep the tutorial short and readable. If you do your\n   own experiments you always have to reinitialize the model before\n   training.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing of data, defining a model, etc.\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading data\n\nIn this example, we load the BCI Competition IV 2a data [1]_, for one\nsubject (subject id 3), using braindecode's wrapper to load via\n[MOABB library](https://github.com/NeuroTechX/moabb)_ [2]_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing data\n\nIn this example, preprocessing includes signal rescaling, the bandpass filtering\n(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using\nthe exponential moving mean and variance.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize,\n    preprocess,\n    Preprocessor,\n)\n\nlow_cut_hz = 4.0  # low cut frequency for filtering\nhigh_cut_hz = 38.0  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor(\"pick_types\", eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(\n        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV\n        factor=1e6,\n    ),\n    Preprocessor(\"filter\", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(\n        exponential_moving_standardize,  # Exponential moving standardization\n        factor_new=factor_new,\n        init_block_size=init_block_size,\n    ),\n]\n\n# Preprocess the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Windows\n\nExtraction of the trials (windows) from the time series is based on the\nevents inside the dataset. One event is the demarcation of the stimulus or\nthe beginning of the trial. In this example, we want to analyse 0.5 [s] long\nbefore the corresponding event and the duration of the event itself.\n#Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the\n``trial_stop_offset_seconds`` to 0 [s].\n\nWe extract from the dataset the sampling frequency, which is the same for\nall datasets in this case, and we tested it.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are\n   defined in seconds and need to be converted into samples (multiplication\n   with the sampling frequency), relative to the event.\n   This variable is dataset dependent.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info[\"sfreq\"]\nassert all([ds.raw.info[\"sfreq\"] == sfreq for ds in dataset.datasets])\n# Calculate the window start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to\n# define how windows should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and test\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset BCIC IV 2a dataset using additional\ninfo stored in the description attribute, in this case the ``session``\ncolumn. We select ``0train`` for training and ``0test`` for testing.\nFor other datasets, you might have to choose another column and/or column.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>No matter which of the three schemes you use, this initial\n   two-fold split into train_set and test_set always remains the same.\n   Remember that you are not allowed to use the test_set during any\n   stage of training or tuning.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split(\"session\")\ntrain_set = splitted['0train']  # Session train\ntest_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create model\n\nIn this tutorial, ShallowFBCSPNet classifier [3]_ is explored. The model\ntraining is performed on GPU if it exists, otherwise on CPU.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = \"cuda\" if cuda else \"cpu\"\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = windows_dataset[0][0].shape[0]\ninput_window_samples = windows_dataset[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=\"auto\",\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to train and evaluate your model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Simple Train-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is the easiest training scheme to use as the dataset is only\nsplit into two distinct sets (``train_set`` and ``test_set``).\nThis scheme uses no separate validation split and should only be\nused for the final evaluation of the (previously!) found\nhyperparameters configuration.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>If you make any use of the ``test_set`` during training\n   (e.g. by using EarlyStopping) there will be data leakage\n   which will make the reported generalization capability/decoding\n   performance of your model less credible.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\nclf.fit(train_set, y=None)\n\n# evaluated the model after training\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the first option with a util function.\nThe following figure illustrates split of entire dataset into the\ntraining and testing subsets.\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\n\nsns.set(font_scale=1.5)\n\n\ndef plot_simple_train_test(ax, all_dataset, train_set, test_set):\n    \"\"\"Create a sample plot for training-testing split.\"\"\"\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#DDF2FF\", \"#2196F3\"]\n\n    ax.barh(\"Original\\ndataset\", len(all_dataset), left=0,\n            height=0.5, color=bd_cmap[0])\n    ax.barh(\"Train-Test\\nsplit\", len(train_set), left=0,\n            height=0.5, color=bd_cmap[1])\n    ax.barh(\"Train-Test\\nsplit\", len(test_set), left=len(train_set),\n            height=0.5, color=bd_cmap[2])\n\n    ax.invert_yaxis()\n    ax.set(xlabel=\"Number of samples.\", title=\"Train-Test split\")\n    ax.legend([\"Original set\", \"Training set\", \"Testing set\"], loc='lower center',\n              ncols=4, bbox_to_anchor=(0.5, 0.5))\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 8))\nplot_simple_train_test(ax=ax, all_dataset=windows_dataset,\n                       train_set=train_set, test_set=test_set)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: Train-Val-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When evaluating different settings hyperparameters for your model,\nthere is still a risk of overfitting on the test set because the\nparameters can be tweaked until the estimator performs optimally.\nFor more information visit [sklearns Cross-Validation Guide](https://scikit-learn.org/stable/modules/cross_validation.html)_.\nThis second option splits the original ``train_set`` into two distinct\nsets, the training set and the validation set to avoid overfitting\nthe hyperparameters to the test set.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>If your dataset is really small, the validation split can become\n   quite small. This may lead to unreliable tuning results. To\n   avoid this, either use Option 3 or adjust the split ratio.</p></div>\n\nTo split the ``train_set`` we will make use of the\n``train_split`` argument of ``EEGClassifier``. If you leave this empty\n(not None!), skorch will make an 80-20 train-validation split.\nIf you want to control the split manually you can do that by using\n``Subset`` from torch and ``predefined_split`` from skorch.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import Subset\nfrom sklearn.model_selection import train_test_split\nfrom skorch.helper import predefined_split, SliceDataset\n\nX_train = SliceDataset(train_set, idx=0)\ny_train = np.array([y for y in SliceDataset(train_set, idx=1)])\ntrain_indices, val_indices = train_test_split(\n    X_train.indices_, test_size=0.2, shuffle=False\n)\ntrain_subset = Subset(train_set, train_indices)\nval_subset = Subset(train_set, val_indices)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>The parameter ``shuffle`` is set to ``False``. For time-series\n   data this should always be the case as shuffling might take\n   advantage of correlated samples, which would make the validation\n   performance less meaningful.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "clf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(val_subset),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\nclf.fit(train_subset, y=None)\n\n# evaluate the model after training and validation\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the second option with a util function.\nThe following figure illustrates split of entire dataset into the\ntraining, validation and testing subsets.\n``Making more compact plot_train_valid_test function.``\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):\n    \"\"\"Create a sample plot for training, validation, testing.\"\"\"\n\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\", ]\n\n    n_train, n_val, n_test = len(train_subset), len(val_subset), len(test_set)\n    ax.barh(\"Original\\ndataset\", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_train, left=0, height=0.5, color=bd_cmap[1])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_val, left=n_train, height=0.5, color=bd_cmap[2])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])\n\n    ax.invert_yaxis()\n    ax.set(xlabel=\"Number of samples.\", title=\"Train-Test-Valid split\")\n    ax.legend([\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n              loc=\"lower center\", ncols=2, bbox_to_anchor=(0.5, 0.4))\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 5))\nplot_train_valid_test(ax=ax, all_dataset=windows_dataset,\n                      train_subset=train_subset, val_subset=val_subset, test_set=test_set,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 3: k-Fold Cross Validation\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As mentioned above, using only one validation split might not be\nsufficient, as there might be a shift in the data distribution.\nTo compensate for this, one can run a k-fold Cross Validation,\nwhere every sample of the training set is in the validation set once.\nAfter averaging over the k validation scores afterwards, you get a\nvery reliable estimate of how the model would perform on unseen\ndata (test set).\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This k-Fold Cross Validation can be used without a separate\n   (holdout) test set. If there is no test set available, e.g. in a\n   competition, this scheme is highly recommended to get a reliable\n   estimate of the generalization performance.</p></div>\n\nTo implement this, we will make use of sklearn function\n[cross_val_score](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.cross_val_score.html)_ and the [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_\nselection.KFold.html)_. CV splitter.\nThe ``train_split`` argument has to be set to ``None``, as sklearn\nwill take care of the splitting.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\n\nfrom sklearn.model_selection import KFold, cross_val_score\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\n\ntrain_val_split = KFold(n_splits=5, shuffle=False)\n# By setting n_jobs=-1, cross-validation is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\ncv_results = cross_val_score(\n    clf, X_train, y_train, scoring=\"accuracy\", cv=train_val_split, n_jobs=1\n)\nprint(\n    f\"Validation accuracy: {np.mean(cv_results * 100):.2f}\"\n    f\"+-{np.std(cv_results * 100):.2f}%\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the third option with a util function.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plot_k_fold(ax, cv, all_dataset, X_train, y_train, test_set):\n    \"\"\"Create a sample plot for training, validation, testing.\"\"\"\n\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\", ]\n\n    ax.barh(\"Original\\nDataset\", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])\n\n    # Generate the training/validation/testing data fraction visualizations for each CV split\n    for ii, (tr_idx, val_idx) in enumerate(cv.split(X=X_train, y=y_train)):\n        n_train, n_val, n_test = len(tr_idx), len(val_idx), len(test_set)\n        n_train2 = n_train + n_val - max(val_idx) - 1\n        ax.barh(\"cv\" + str(ii + 1), min(val_idx), left=0, height=0.5, color=bd_cmap[1])\n        ax.barh(\"cv\" + str(ii + 1), n_val, left=min(val_idx), height=0.5, color=bd_cmap[2])\n        ax.barh(\"cv\" + str(ii + 1), n_train2, left=max(val_idx) + 1, height=0.5, color=bd_cmap[1])\n        ax.barh(\"cv\" + str(ii + 1), n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])\n\n    ax.invert_yaxis()\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    ax.set(xlabel=\"Number of samples.\", title=\"KFold Train-Test-Valid split\")\n    ax.legend([Patch(color=bd_cmap[i]) for i in range(4)],\n              [\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n              loc=\"lower center\", ncols=2)\n    ax.text(-0.07, 0.45, 'Train-Valid-Test split', rotation=90,\n            verticalalignment='center', horizontalalignment='left', transform=ax.transAxes)\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(15, 7))\nplot_k_fold(ax, cv=train_val_split, all_dataset=windows_dataset,\n            X_train=X_train, y_train=y_train, test_set=test_set,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to tune your hyperparameters\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "One way to do hyperparameter tuning is to run each configuration\nmanually (via Option 2 or 3 from above) and compare the validation\nperformance afterwards. In the early stages of your development\nprocess this might be sufficient to get a rough understanding of\nhow your hyperparameter should look like for your model to converge.\nHowever, this manual tuning process quickly becomes messy as the\nnumber of hyperparameters you want to (jointly) tune increases.\nTherefore you should, automate this process. We will present two\ndifferent options, analogous to Option 2 and 3 from above.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Train-Val-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will again make use of the [sklearn](https://scikit-learn.org/stable/)_\nlibrary to do the hyperparameter search. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)_\nwill perform a Grid Search over the parameters specified in ``param_grid``.\nWe use grid search for the model selection as a simple example, but you can use other strategies\nas well.\n([List of the sklearn classes for model selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection)_.)\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\nfrom sklearn.model_selection import GridSearchCV\n\ntrain_val_split = [\n    tuple(train_test_split(X_train.indices_, test_size=0.2, shuffle=False))\n]\n\nparam_grid = {\n    \"optimizer__lr\": [0.00625, 0.000625],\n}\n\n# By setting n_jobs=-1, grid search is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=train_val_split,\n    return_train_score=True,\n    scoring=\"accuracy\",\n    refit=True,\n    verbose=1,\n    error_score=\"raise\",\n    n_jobs=1,\n)\n\nsearch.fit(X_train, y_train)\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results[\"rank_test_score\"] == 1].squeeze()\n\nbest_parameters = best_run[\"params\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: k-Fold Cross Validation\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To perform a full k-Fold CV just replace ``train_val_split`` from\nabove with the [KFold](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.KFold.html)_ cross-validator from sklearn.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "train_val_split = KFold(n_splits=5, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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@@ -0,0 +1,226 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cropped Decoding on BCIC IV 2a Dataset\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Building on the [Trialwise decoding](./plot_bcic_iv_2a_moabb_trial.html)_,\nwe now do more data-efficient cropped decoding!\n\nIn Braindecode, there are two supported configurations created for\ntraining models: trialwise decoding and cropped decoding. We will\nexplain this visually by comparing trialwise to cropped decoding.\n\n<img src=\"https://braindecode.org/stable/_static/trialwise_explanation.png\" alt=\"Trialwise decoding\">\n<img src=\"https://braindecode.org/stable/_static/cropped_explanation.png\" alt=\"Cropped decoding\">\nOn the left, you see trialwise decoding:\n\n1. A complete trial is pushed through the network.\n2. The network produces a prediction.\n3. The prediction is compared to the target (label) for that trial to\n   compute the loss.\n\nOn the right, you see cropped decoding:\n\n1. Instead of a complete trial, crops are pushed through the network.\n2. For computational efficiency, multiple neighbouring crops are pushed\n   through the network simultaneously (these neighbouring crops are\n   called compute windows)\n3. Therefore, the network produces multiple predictions (one per crop in\n   the window)\n4. The individual crop predictions are *AVERAGED* before computing the\n   loss function\n\nThis averaging of predictions of small sub-windows is the key difference\nbetween trialwise and cropped decoding. It was introduced in [1]_ and it impact\non the parameters of the network.\n\nIt is important to note that the averaging of predictions is only done\nduring training. During testing, the network is still applied to crops\nand the predictions are averaged afterwards.\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>-  The network architecture implicitly defines the crop size (it is the\n       receptive field size, i.e., the number of timesteps the network uses\n       to make a single prediction)\n    -  The window size is a user-defined hyperparameter, called\n       ``input_window_samples`` in Braindecode. It mostly affects runtime\n       (larger window sizes should be faster). As a rule of thumb, you can\n       set it to two times the crop size.\n    -  Crop size and window size together define how many predictions the\n       network makes per window: ``#window \u2212 #crop + 1 = #predictions``</p></div>\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>For cropped decoding, the above training setup is mathematically\n    similar to sampling crops in your dataset, pushing them through the\n    network and training directly on the individual crops. However, the\n    if their position would be randomly selected, the crops would be less\n    correlated in contrast to the neighbourhood crops selected from a window.\n    At the same time, the above training setup is much faster as it avoids\n    redundant computations by using dilated convolutions, see [2]_.\n    However, the two setups are only mathematically related in case (1)\n    your network does not use any padding or only left padding and\n    (2) your loss function leads\n    to the same gradients when using the averaged output. The first is true\n    for our shallow and deep ConvNet models and the second is true for the\n    log-softmax outputs and negative log likelihood loss that is typically\n    used for classification in PyTorch.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\nLoading and preprocessing stays the same as in the [Trialwise decoding\ntutorial](./plot_bcic_iv_2a_moabb_trial.html)_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])\n\nfrom numpy import multiply\n\nfrom braindecode.preprocessing import (\n    Preprocessor,\n    exponential_moving_standardize,\n    preprocess,\n)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),\n    # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),\n    # Bandpass filter\n    Preprocessor(exponential_moving_standardize,\n                 # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model and compute windowing parameters\nIn contrast to trialwise decoding, we first have to create the model\nbefore we can cut the dataset into windows. This is because we need to\nknow the neural network parameters to know how large the sub-window\nstride should be.\n\nWe first choose the compute/input window size that will be fed to the\nnetwork during training. This has to be larger than the networks\nthe number of timesteps size and can otherwise be chosen for computational\nefficiency (see explanations in the beginning of this tutorial). Here we\nchoose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "input_window_samples = 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the model. To enable it to be used in cropped decoding\nefficiently, we manually set the length of the final convolution layer\nto some length that makes the number of timesteps of the ConvNet smaller\nthan ``input_window_samples`` (see ``final_conv_length=30`` in the model\ndefinition).\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans from dataset\nn_chans = dataset[0][0].shape[0]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=30,\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    _ = model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And now we transform model with strides to a model that outputs dense\nprediction, so we can use it to obtain predictions for all\ncrops.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model.to_dense_prediction_model()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To know the models\u2019 output shape without the last layer, we calculate the\nshape of model output for a dummy input.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_preds_per_input = model.get_output_shape()[2]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cut the data into windows\nIn contrast to trialwise decoding, we have to supply an explicit\nwindow size and window stride to the ``create_windows_from_events``\nfunction.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    preload=True\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Split the dataset\n\nThis code is the same as in trialwise decoding.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\nIn difference to trialwise decoding, we now should supply\n``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the\ncriterion, as well as ``criterion__loss_function`` as the loss function\napplied to the meaned predictions.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n    have found to work well for motor decoding, however we strongly\n    encourage you to perform your own hyperparameter optimization using\n    cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\nfrom braindecode.training import CroppedLoss\n\n# These values we found good for shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    cropped=True,\n    criterion=CroppedLoss,\n    criterion__loss_function=torch.nn.functional.nll_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\n_ = clf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n<div class=\"alert alert-info\"><h4>Note</h4><p>Note that we drop further in the classification error and\n    loss as in the trialwise decoding tutorial.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy',\n                   'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,\n    fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(\n    Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(\n    Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Confusion Matrix\n\nGenerate a confusion matrix as in [2]_\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = valid_set.datasets[0].window_kwargs[0][1]['mapping']\n# sort the labels by values (values are integer class labels)\nlabels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/0.8/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb b/0.8/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb
new file mode 100644
index 00000000..ed47d27d
--- /dev/null
+++ b/0.8/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb
@@ -0,0 +1,187 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Process a big data EEG resource (TUH EEG Corpus)\n\nIn this example, we showcase usage of the Temple University Hospital EEG Corpus\n(https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)\nincluding simple preprocessing steps as well as cutting of compute windows.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows)\nfrom numpy import multiply\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating the dataset using TUHMock\n\nSince the data is not available at the time of the creation of this example,\nwe are required to mock some of the dataset functionality. Therefore, if you\nwant to try this code with the actual data, please disconsider this section.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Firstly, we start by creating a TUH mock dataset using braindecode's _TUHMock class.\nThe complete code can be found at :func:`braindecode.datasets.TUH`, but we will give\na small description of how it works.\nThis class is able to read the recordings from TUH_PATH and generate a description\nby parsing information from file paths, such as patient id and recording data.\nTHis description can later be accessed by the object's .description method.\nAfter that, the files are sorted chronologically by year, month, day,\npatient id, recording session and segment, and then use the description corresponding\nto the specified by recording ids.\nFInally, additional information regarding age and gender of the subjects are parsed\ndirectly to the description.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "TUH_PATH = 'please insert actual path to data here'\n# specify the number of jobs for loading and windowing\nN_JOBS = 2\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=None,\n    preload=False,\n    add_physician_reports=False,\n    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can visualize our data's statistics using the class' \"description\" method\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plt_histogram(df_of_ages_genders, alpha=0.5, fs=24, ylim=1.5, show_title=True):\n    # Dafarame containing info about gender and age of subjects\n    df = df_of_ages_genders\n    male_df = df[df[\"gender\"] == 'M']\n    female_df = df[df[\"gender\"] == 'F']\n\n    plt.figure(figsize=(15, 18))\n    if show_title:\n        plt.suptitle(\"Age information\", y=0.95, fontsize=fs + 5)\n\n    # First plot: Male individuals\n    plt.subplot(121)\n    plt.hist(male_df[\"age\"], bins=np.linspace(0, 100, 101),\n             alpha=alpha, color=\"green\", orientation=\"horizontal\")\n    plt.axhline(np.mean(male_df[\"age\"]), color=\"black\",\n                label=f\"mean age {np.mean(male_df['age']):.1f} \"\n                      f\"(\u00b1{np.std(male_df['age']):.1f})\")\n    plt.barh(np.mean(male_df[\"age\"]), height=2 * np.std(male_df[\"age\"]),\n             width=ylim, color=\"black\", alpha=0.25)\n\n    # Legend\n    plt.xlim(0, ylim)\n    plt.legend(fontsize=fs, loc=\"upper left\")\n    plt.title(f\"male ({100 * len(male_df) / len(df):.1f}%)\",\n              fontsize=fs, loc=\"left\", y=1, x=0.05)\n    plt.yticks(color='w')\n    plt.gca().invert_xaxis()\n    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)\n    plt.tick_params(labelsize=fs - 5)\n\n    # First plot: Female individuals\n    plt.subplot(122)\n    plt.hist(female_df[\"age\"], bins=np.linspace(0, 100, 101),\n             alpha=alpha, color=\"orange\", orientation=\"horizontal\")\n    plt.axhline(np.mean(female_df[\"age\"]), color=\"black\", linestyle=\"--\",\n                label=f\"mean age {np.mean(female_df['age']):.1f} (\"\n                      f\"\u00b1{np.std(female_df['age']):.1f})\")\n    plt.barh(np.mean(female_df[\"age\"]), height=2 * np.std(female_df[\"age\"]),\n             width=ylim, color=\"black\", alpha=0.25)\n\n    # Label\n    plt.legend(fontsize=fs, loc=\"upper right\")\n    plt.xlim(0, ylim)\n    plt.title(f\"female ({100 * len(female_df) / len(df):.1f}%)\",\n              fontsize=fs, loc=\"right\", y=1, x=0.95)\n    plt.ylim(0, 100)\n    plt.subplots_adjust(wspace=0, hspace=0)\n    plt.ylabel(\"age [years]\", fontsize=fs)\n    plt.xlabel(\"count\", fontsize=fs, x=1, labelpad=20)\n    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)\n    plt.tick_params(labelsize=fs - 5)\n\n    plt.show()\n\n\ndf = tuh.description\nplt_histogram(df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preprocessing\n\n### Selecting recordings\n\nFirst, we will do some selection of available recordings based on the duration.\nWe will select those recordings that have at least five minutes duration.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def select_by_duration(ds, tmin=0, tmax=None):\n    if tmax is None:\n        tmax = np.inf\n    # determine length of the recordings and select based on tmin and tmax\n    split_ids = []\n    for d_i, d in enumerate(ds.datasets):\n        duration = d.raw.n_times / d.raw.info['sfreq']\n        # select the ones in the required duration range\n        if tmin <= duration <= tmax:\n            split_ids.append(d_i)\n    splits = ds.split(split_ids)\n    split = splits['0']\n    return split\n\n\ntmin = 5 * 60\ntmax = None\ntuh = select_by_duration(tuh, tmin, tmax)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will discard all recordings that have an incomplete channel\nconfiguration on the channels that we are interested. The subdivisions of the\nrecordings into 'le' and 'ar' labels represents the channels for\nthe re-referencing of the signals.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "short_ch_names = sorted([\n    'A1', 'A2',\n    'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',\n    'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])\n\n# TUH data is subdivided into 'le' and 'ar' recordings references\nar_ch_names = sorted([\n    'EEG A1-REF', 'EEG A2-REF',\n    'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',\n    'EEG C4-REF', 'EEG P3-REF', 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF',\n    'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', 'EEG T5-REF',\n    'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF'])\nle_ch_names = sorted([\n    'EEG A1-LE', 'EEG A2-LE',\n    'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE',\n    'EEG C4-LE', 'EEG P3-LE', 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE',\n    'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', 'EEG T5-LE',\n    'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE'])\nassert len(short_ch_names) == len(ar_ch_names) == len(le_ch_names)\nar_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    ar_ch_names, short_ch_names)}\nle_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    le_ch_names, short_ch_names)}\nch_mapping = {'ar': ar_ch_mapping, 'le': le_ch_mapping}\n\n\ndef select_by_channels(ds, ch_mapping):\n    split_ids = []\n    for i, d in enumerate(ds.datasets):\n        ref = 'ar' if d.raw.ch_names[0].endswith('-REF') else 'le'\n        # these are the channels we are looking for\n        seta = set(ch_mapping[ref].keys())\n        # these are the channels of the recoding\n        setb = set(d.raw.ch_names)\n        # if recording contains all channels we are looking for, include it\n        if seta.issubset(setb):\n            split_ids.append(i)\n    return ds.split(split_ids)['0']\n\n\ntuh = select_by_channels(tuh, ch_mapping)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Combining preprocessing steps\n\nNext, we use braindecode's preprocess to combine and execute several preprocessing\nsteps that are executed through 'mne':\n\n- Crop the recordings to a region of interest\n- Re-reference all recordings to 'ar' (requires load)\n- Rename channels to short channel names\n- Pick channels of interest\n- Scale signals to micro volts (requires load)\n- Clip outlier values to +/- 800 micro volts (requires load)\n- Resample recordings to a common frequency (requires load)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def custom_rename_channels(raw, mapping):\n    # rename channels which are dependent on referencing:\n    # le: EEG 01-LE, ar: EEG 01-REF\n    # mne fails if the mapping contains channels as keys that are not present\n    # in the raw\n    reference = raw.ch_names[0].split('-')[-1].lower()\n    assert reference in ['le', 'ref'], 'unexpected referencing'\n    reference = 'le' if reference == 'le' else 'ar'\n    raw.rename_channels(mapping[reference])\n\n\ndef custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):\n    # crop recordings to tmin \u2013 tmax. can be incomplete if recording\n    # has lower duration than tmax\n    # by default mne fails if tmax is bigger than duration\n    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)\n    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)\n\n\ntmin = 1 * 60\ntmax = 6 * 60\nsfreq = 100\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,\n                 apply_on_array=False),\n    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),\n    Preprocessor(custom_rename_channels, mapping=ch_mapping,\n                 apply_on_array=False),\n    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),\n    Preprocessor('resample', sfreq=sfreq),\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we can apply the defined preprocessors on the selected recordings in parallel.\nWe additionally use the serialization functionality of\n:func:`braindecode.preprocessing.preprocess` to limit memory usage during\npreprocessing, as each file must be loaded into memory for some of the\npreprocessing steps to work.\nThis also makes it possible to use the lazy\nloading capabilities of :class:`braindecode.datasets.BaseConcatDataset`, as\nthe preprocessed data is automatically reloaded with ``preload=False``.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Here we use ``n_jobs=2`` as the machines the documentation is build on\n   only have two cores. This number should be modified based on the machine\n   that is available for preprocessing.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "OUT_PATH = tempfile.mkdtemp()  # please insert actual output directory here\ntuh_preproc = preprocess(\n    concat_ds=tuh,\n    preprocessors=preprocessors,\n    n_jobs=N_JOBS,\n    save_dir=OUT_PATH\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\nWe can finally generate compute windows. The resulting dataset is now ready\nto be used for model training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "window_size_samples = 1000\nwindow_stride_samples = 1000\n# Generate compute windows here and store them to disk\ntuh_windows = create_fixed_length_windows(\n    tuh_preproc,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_stride_samples,\n    drop_last_window=False,\n    n_jobs=N_JOBS,\n)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/0.8/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py b/0.8/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py
new file mode 100644
index 00000000..9d58b381
--- /dev/null
+++ b/0.8/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py
@@ -0,0 +1,380 @@
+"""
+Hyperparameter tuning with scikit-learn
+=======================================
+
+The braindecode provides some compatibility with
+`scikit-learn <https://scikit-learn.org/stable/>`__. This allows us
+to use scikit-learn functionality to find the best hyperparameters for our
+model. This is especially useful to tune hyperparameters or
+parameters for one decoding task or a specific dataset.
+
+.. topic:: Why do you need to tune the neural networks model?
+
+    Deep learning models are often sensitive to the choice of hyperparameters
+    and parameters. Hyperparameters are the parameters set before
+    training the model. The hyeperparameters determine (1) the capacity of the model,
+    e.g. its depth (the number of layers) and its width (the number of
+    convolutional kernels, sizes of fully connected layers) and (2) the
+    learning process via the choice of optimizer and its learning rate,
+    the number of epochs, the batch size, the choice of non-linearities,
+    the strategies to initialize the learning weights, etc.
+    On the other hand, parameters are learned during training,
+    such as the neural network weights. The choice of these can have a
+    significant impact on the performance of the model.
+    Therefore, it is important to tune these to maximize the discriminative
+    power of the model, in the case of decoding tasks (classification,
+    regression, etc.), such as sleep staging, BCI, pathology detection, etc.
+    We recommend the Deep Learning Tuning Playbook by Google to learn more
+    about hyperparameters and parameters tuning [1]_.
+
+
+In this tutorial, we will use the standard decoding approach to show the impact
+of the learning rate and dropout probability on the model's performance.
+
+
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+
+
+######################################################################
+# Loading
+# ~~~~~~~
+#
+
+
+######################################################################
+# First, we load the data. In this tutorial, we use the functionality of
+# braindecode to load datasets via
+# `MOABB <https://github.com/NeuroTechX/moabb>`__ [2]_ to load the BCI
+# Competition IV 2a data [3]_.
+#
+# .. note::
+#    To load your own datasets either via mne or from
+#    preprocessed X/y numpy arrays, see `MNE Dataset
+#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
+#    Tutorial <./plot_custom_dataset_example.html>`__.
+#
+
+from braindecode.datasets.moabb import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# In this example, preprocessing includes signal rescaling, the bandpass filtering
+# (low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+# the exponential moving mean and variance.
+# You can either apply functions provided by
+# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
+# `mne.Epochs <https://mne.tools/stable/generated/mne.Epochs.html>`__
+# or apply your own functions, either to the MNE object or the underlying
+# numpy array.
+#
+# .. note::
+#    These prepocessings are now directly applied to the loaded
+#    data, and not on-the-fly applied as transformations in
+#    PyTorch-libraries like
+#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
+#
+
+from braindecode.preprocessing.preprocess import (
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
+    # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
+    # Bandpass filter
+    Preprocessor(exponential_moving_standardize,
+                 # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+# Preprocess the data
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Extraction of the Compute Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Extraction of the Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Extraction of the trials (windows) from the time series is based on the
+# events inside the dataset. One event is the demarcation of the stimulus or
+# the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+# before the corresponding event and the duration of the event itself.
+# #Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the
+# ``trial_stop_offset_seconds`` to 0 [s].
+#
+# We extract from the dataset the sampling frequency, which is the same for
+# all datasets in this case, and we tested it.
+#
+# .. note::
+#    The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are
+#    defined in seconds and need to be converted into samples (multiplication
+#    with the sampling frequency), relative to the event.
+#    This variable is dataset dependent.
+#
+
+from braindecode.preprocessing.windowers import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case ``session`` column. We select
+# ``0train`` for training and ``1test`` for evaluation.
+#
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Create model
+# ------------
+#
+
+
+######################################################################
+# Now we create the deep learning model! Braindecode comes with some
+# predefined convolutional neural network architectures for raw
+# time-domain EEG. Here, we use the ShallowFBCSPNet model from `Deep
+# learning with convolutional neural networks for EEG decoding and
+# visualization <https://arxiv.org/abs/1703.05051>`__ [4]_. These models are
+# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
+# to use your own model, it just has to be a normal PyTorch
+# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
+#
+from functools import partial
+import torch
+from braindecode.util import set_random_seeds
+from braindecode.models import ShallowFBCSPNet
+
+# check if GPU is available, if True chooses to use it
+cuda = torch.cuda.is_available()
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+seed = 20200220  # random seed to make results reproducible
+# Set random seed to be able to reproduce results
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+# Extract number of chans and time steps from dataset
+n_chans = train_set[0][0].shape[0]
+input_window_samples = train_set[0][0].shape[1]
+
+# To analyze the impact of the different parameters inside the torch model, we
+# need to create partial initialisations. This is because the
+# GridSearchCV of scikit-learn will try to initialize the model with the
+# parameters we want to tune. If we do not do this, the GridSearchCV will
+# try to initialize the model with the parameters we want to tune but
+# without the parameters we do not want to tune. This will result in an
+# error.
+model = partial(ShallowFBCSPNet, n_chans, n_classes,
+                input_window_samples=input_window_samples,
+                final_conv_length='auto', )
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# Training
+# --------
+#
+
+
+######################################################################
+# Now we train the network! EEGClassifier is a Braindecode object
+# responsible for managing the training of neural networks. It inherits
+# from `skorch.NeuralNetClassifier <https://skorch.readthedocs.io/
+# en/latest/classifier.html>`__,
+# so the training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+
+from skorch.callbacks import LRScheduler
+from skorch.dataset import ValidSplit
+from braindecode import EEGClassifier
+
+batch_size = 16
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    optimizer__lr=[],  # This will be handled by GridSearchCV
+    batch_size=batch_size,
+    train_split=ValidSplit(0.2, random_state=seed),
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+)
+
+######################################################################
+# We use scikit-learn `GridSearchCV
+# <https://scikit-learn.org/stable/modules/generated/
+# sklearn.model_selection.GridSearchCV.html>`__ to tune hyperparameters.
+# To be able to do this, we slice the braindecode datasets that by default
+# return a 3-tuple to return X and y, respectively.
+#
+
+######################################################################
+# .. note::
+#    The KFold object splits the datasets based on their
+#    length which corresponds to the number of compute windows. In
+#    this (trialwise) example this is fine to do. In a cropped setting
+#    this is not advisable since this might split compute windows
+#    of a single trial into both train and valid set.
+#
+
+from sklearn.model_selection import GridSearchCV, KFold
+from skorch.helper import SliceDataset
+from numpy import array
+import pandas as pd
+
+train_X = SliceDataset(train_set, idx=0)
+train_y = array([y for y in SliceDataset(train_set, idx=1)])
+cv = KFold(n_splits=2, shuffle=True, random_state=42)
+
+learning_rates = [0.00625, 0.0000625]
+drop_probs = [0.2, 0.5, 0.8]
+
+fit_params = {'epochs': n_epochs}
+param_grid = {
+    'optimizer__lr': learning_rates,
+    'module__drop_prob': drop_probs
+}
+
+# By setting n_jobs=-1, grid search is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
+search = GridSearchCV(
+    estimator=clf,
+    param_grid=param_grid,
+    cv=cv,
+    return_train_score=True,
+    scoring='accuracy',
+    refit=True,
+    verbose=1,
+    error_score='raise',
+    n_jobs=1,
+)
+
+search.fit(train_X, train_y, **fit_params)
+
+# Extract the results into a DataFrame
+search_results = pd.DataFrame(search.cv_results_)
+
+######################################################################
+# Plotting the results
+# --------------------
+#
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# Create a pivot table for the heatmap
+pivot_table = search_results.pivot(index='param_optimizer__lr',
+                                   columns='param_module__drop_prob',
+                                   values='mean_test_score')
+# Create the heatmap
+fig, ax = plt.subplots()
+sns.heatmap(pivot_table, annot=True, fmt=".3f",
+            cmap="YlGnBu", cbar=True)
+plt.title('Grid Search Mean Test Scores')
+plt.ylabel('Learning Rate')
+plt.xlabel('Dropout Probability')
+plt.tight_layout()
+plt.show()
+
+###########################################################################
+# Get the best hyperparameters
+# ----------------------------
+#
+best_run = search_results[search_results['rank_test_score'] == 1].squeeze()
+print(
+    f"Best hyperparameters were {best_run['params']} which gave a validation "
+    f"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training "
+    f"accuracy of {best_run['mean_train_score'] * 100:.2f}%).")
+
+eval_X = SliceDataset(eval_set, idx=0)
+eval_y = SliceDataset(eval_set, idx=1)
+score = search.score(eval_X, eval_y)
+print(f"Eval accuracy is {score * 100:.2f}%.")
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,
+#       Zachary Nado (2022). Deep Learning Tuning Playbook.
+#       Github https://github.com/google-research/tuning_playbook
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [4] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/817b00270adee98fe11f869f8c64d9cc/plot_benchmark_preprocessing.py b/0.8/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py
similarity index 99%
rename from stable/_downloads/817b00270adee98fe11f869f8c64d9cc/plot_benchmark_preprocessing.py
rename to 0.8/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py
index 1536c629..9cd08ee9 100644
--- a/stable/_downloads/817b00270adee98fe11f869f8c64d9cc/plot_benchmark_preprocessing.py
+++ b/0.8/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py
@@ -114,7 +114,7 @@ def prepare_data(n_recs, save, preload, n_jobs):
 #   data size and preprocessing operations), we recommend adapting this example
 #   to your use case and running it on your machine.
 
-n_repets = 3  # Number of repetitions
+n_repets = 2  # Number of repetitions
 all_n_recs = 2  # Number of recordings to load and preprocess
 all_n_jobs = [1, 2]  # Number of parallel processes
 
diff --git a/0.8/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py b/0.8/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py
new file mode 100644
index 00000000..e8d3c186
--- /dev/null
+++ b/0.8/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py
@@ -0,0 +1,409 @@
+"""
+Training a Braindecode model in PyTorch
+=======================================
+
+This tutorial shows you how to train a Braindecode model with PyTorch. The data
+preparation and model instantiation steps are identical to that of the tutorial
+`How to train, test and tune your model <./plot_how_train_test_and_tune.html>`__
+
+We will use the BCIC IV 2a dataset as a showcase example.
+
+The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing,
+defining a model, and other details which are not exclusive to this page (compare
+`Cropped Decoding Tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.
+
+The goal of this tutorial is to present braindecode in the PyTorch perceptive.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Why should I care about model evaluation?
+# -----------------------------------------
+# Short answer: To produce reliable results!
+#
+# In machine learning, we usually follow the scheme of splitting the
+# data into two parts, training and testing sets. It sounds like a
+# simple division, right? But the story does not end here.
+#
+# While developing a ML model you usually have to adjust and tune
+# hyperparameters of your model or pipeline (e.g., number of layers,
+# learning rate, number of epochs). Deep learning models usually have
+# many free parameters; they could be considered complex models with
+# many degrees of freedom. If you kept using the test dataset to
+# evaluate your adjustmentyou would run into data leakage.
+#
+# This means that if you use the test set to adjust the hyperparameters
+# of your model, the model implicitly learns or memorizes the test set.
+# Therefore, the trained model is no longer independent of the test set
+# (even though it was never used for training explicitly!).
+# If you perform any hyperparameter tuning, you need a third split,
+# the so-called validation set.
+#
+# This tutorial shows the three basic schemes for training and evaluating
+# the model as well as two methods to tune your hyperparameters.
+#
+
+######################################################################
+# .. warning::
+#    You might recognize that the accuracy gets better throughout
+#    the experiments of this tutorial. The reason behind that is that
+#    we always use the same model with the same parameters in every
+#    segment to keep the tutorial short and readable. If you do your
+#    own experiments you always have to reinitialize the model before
+#    training.
+#
+
+######################################################################
+# Loading, preprocessing, defining a model, etc.
+# ----------------------------------------------
+#
+
+
+######################################################################
+# Loading the Dataset Structure
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Here, we have a data structure with equal behavior to the Pytorch Dataset.
+
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing, the offline transformation of the raw dataset
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+import numpy as np
+
+from braindecode.preprocessing import (
+    exponential_moving_standardize,
+    preprocess,
+    Preprocessor,
+)
+
+low_cut_hz = 4.0  # low cut frequency for filtering
+high_cut_hz = 38.0  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+
+transforms = [
+    Preprocessor("pick_types", eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(
+        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV
+        factor=1e6,
+    ),
+    Preprocessor("filter", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(
+        exponential_moving_standardize,  # Exponential moving standardization
+        factor_new=factor_new,
+        init_block_size=init_block_size,
+    ),
+]
+
+# Transform the data
+preprocess(dataset, transforms, n_jobs=-1)
+
+######################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~~~~~~~
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info["sfreq"]
+assert all([ds.raw.info["sfreq"] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Create Pytorch model
+# ~~~~~~~~~~~~~~~~~~~~
+#
+
+import torch
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = "cuda" if cuda else "cpu"
+if cuda:
+    torch.backends.cudnn.benchmark = True
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans and time steps from dataset
+n_channels = windows_dataset[0][0].shape[0]
+input_window_samples = windows_dataset[0][0].shape[1]
+
+# The ShallowFBCSPNet is a `nn.Sequential` model
+
+model = ShallowFBCSPNet(
+    n_channels,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length="auto",
+)
+
+# Display torchinfo table describing the model
+print(model)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# How to train and evaluate your model
+# ------------------------------------
+#
+
+######################################################################
+# Split dataset into train and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case the ``session`` column. We
+# select ``Train`` for training and ``test`` for testing.
+# For other datasets, you might have to choose another column.
+#
+# .. note::
+#    No matter which of the three schemes you use, this initial
+#    two-fold split into train_set and test_set always remains the same.
+#    Remember that you are not allowed to use the test_set during any
+#    stage of training or tuning.
+#
+
+splitted = windows_dataset.split("session")
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Option 1: Pure PyTorch training loop
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# .. image:: https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png
+#    :alt: Pytorch logo
+
+
+######################################################################
+# `model` is an instance of `torch.nn.Module`, and can as such be trained
+# using PyTorch optimization capabilities.
+# The following training scheme is simple as the dataset is only
+# split into two distinct sets (``train_set`` and ``test_set``).
+# This scheme uses no separate validation split and should only be
+# used for the final evaluation of the (previously!) found
+# hyperparameters configuration.
+#
+# .. warning::
+#    If you make any use of the ``test_set`` during training
+#    (e.g. by using EarlyStopping) there will be data leakage
+#    which will make the reported generalization capability/decoding
+#    performance of your model less credible.
+#
+# .. warning::
+#    The parameter values showcased here for optimizing the network are
+#    chosen to make this tutorial fast to run and build. Real-world values
+#    would be higher, especially when it comes to n_epochs.
+
+from torch.nn import Module
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
+
+######################################################################
+# The following method runs one training epoch over the dataloader for the
+# given model. It needs a loss function, optimization algorithm, and
+# learning rate updating callback.
+from tqdm import tqdm
+# Define a method for training one epoch
+
+
+def train_one_epoch(
+        dataloader: DataLoader, model: Module, loss_fn, optimizer,
+        scheduler: LRScheduler, epoch: int, device, print_batch_stats=True
+):
+    model.train()  # Set the model to training mode
+    train_loss, correct = 0, 0
+
+    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader),
+                        disable=not print_batch_stats)
+
+    for batch_idx, (X, y, _) in progress_bar:
+        X, y = X.to(device), y.to(device)
+        optimizer.zero_grad()
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        loss.backward()
+        optimizer.step()  # update the model weights
+        optimizer.zero_grad()
+
+        train_loss += loss.item()
+        correct += (pred.argmax(1) == y).sum().item()
+
+        if print_batch_stats:
+            progress_bar.set_description(
+                f"Epoch {epoch}/{n_epochs}, "
+                f"Batch {batch_idx + 1}/{len(dataloader)}, "
+                f"Loss: {loss.item():.6f}"
+            )
+
+    # Update the learning rate
+    scheduler.step()
+
+    correct /= len(dataloader.dataset)
+    return train_loss / len(dataloader), correct
+
+######################################################################
+# Very similarly, the evaluation function loops over the entire dataloader
+# and accumulate the metrics, but doesn't update the model weights.
+
+
+@torch.no_grad()
+def test_model(
+    dataloader: DataLoader, model: Module, loss_fn, print_batch_stats=True
+):
+    size = len(dataloader.dataset)
+    n_batches = len(dataloader)
+    model.eval()  # Switch to evaluation mode
+    test_loss, correct = 0, 0
+
+    if print_batch_stats:
+        progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
+    else:
+        progress_bar = enumerate(dataloader)
+
+    for batch_idx, (X, y, _) in progress_bar:
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        batch_loss = loss_fn(pred, y).item()
+
+        test_loss += batch_loss
+        correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+
+        if print_batch_stats:
+            progress_bar.set_description(
+                f"Batch {batch_idx + 1}/{len(dataloader)}, "
+                f"Loss: {batch_loss:.6f}"
+            )
+
+    test_loss /= n_batches
+    correct /= size
+
+    print(
+        f"Test Accuracy: {100 * correct:.1f}%, Test Loss: {test_loss:.6f}\n"
+    )
+    return test_loss, correct
+
+
+# Define the optimization
+optimizer = torch.optim.AdamW(model.parameters(),
+                              lr=lr, weight_decay=weight_decay)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
+                                                       T_max=n_epochs - 1)
+# Define the loss function
+# We used the NNLoss function, which expects log probabilities as input
+# (which is the case for our model output)
+loss_fn = torch.nn.NLLLoss()
+
+# train_set and test_set are instances of torch Datasets, and can seamlessly be
+# wrapped in data loaders.
+train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_set, batch_size=batch_size)
+
+for epoch in range(1, n_epochs + 1):
+    print(f"Epoch {epoch}/{n_epochs}: ", end="")
+
+    train_loss, train_accuracy = train_one_epoch(
+        train_loader, model, loss_fn, optimizer, scheduler, epoch, device,
+    )
+
+    test_loss, test_accuracy = test_model(test_loader, model, loss_fn)
+
+    print(
+        f"Train Accuracy: {100 * train_accuracy:.2f}%, "
+        f"Average Train Loss: {train_loss:.6f}, "
+        f"Test Accuracy: {100 * test_accuracy:.1f}%, "
+        f"Average Test Loss: {test_loss:.6f}\n"
+    )
+
+
+######################################################################
+# Option 2: Train it with PyTorch Lightning
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# .. image:: https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png
+#    :alt: Pytorch Lightning logo
+
+######################################################################
+# Alternatively, lightning provides a nice interface around torch modules
+# which integrates the previous logic.
+
+
+import lightning as L
+from torchmetrics.functional import accuracy
+
+
+class LitModule(L.LightningModule):
+    def __init__(self, module):
+        super().__init__()
+        self.module = module
+        self.loss = torch.nn.NLLLoss()
+
+    def training_step(self, batch, batch_idx):
+        x, y, _ = batch
+        y_hat = self.module(x)
+        loss = self.loss(y_hat, y)
+        self.log("train_loss", loss)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        x, y, _ = batch
+        y_hat = self.module(x)
+        loss = self.loss(y_hat, y)
+        acc = accuracy(y_hat, y, "multiclass", num_classes=4)
+        metrics = {"test_acc": acc, "test_loss": loss}
+        self.log_dict(metrics)
+        return metrics
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(model.parameters(), lr=lr,
+                                      weight_decay=weight_decay)
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
+                                                               T_max=n_epochs - 1)
+        return [optimizer], [scheduler]
+
+
+# Creating the trainer with max_epochs=2 for demonstration purposes
+trainer = L.Trainer(max_epochs=n_epochs)
+# Create and train the LightningModule
+lit_model = LitModule(model)
+trainer.fit(lit_model, train_loader)
+
+# After training, you can test the model using the test DataLoader
+trainer.test(dataloaders=test_loader)
diff --git a/0.8/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/0.8/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
new file mode 100644
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diff --git a/stable/_downloads/07670a81c122dbfad70946c34b056724/plot_how_train_test_and_tune.py b/0.8/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py
similarity index 58%
rename from stable/_downloads/07670a81c122dbfad70946c34b056724/plot_how_train_test_and_tune.py
rename to 0.8/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py
index 92123856..d11375ef 100644
--- a/stable/_downloads/07670a81c122dbfad70946c34b056724/plot_how_train_test_and_tune.py
+++ b/0.8/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py
@@ -1,22 +1,25 @@
 """
-How to train, test and tune your model
-======================================
+How to train, test and tune your model?
+=======================================
 
 This tutorial shows you how to properly train, tune and test your deep learning
-models with Braindecode. We will use the BCIC IV 2a dataset as a showcase example.
+models with Braindecode. We will use the BCIC IV 2a dataset [1]_ as a showcase example.
 
 The methods shown can be applied to any standard supervised trial-based decoding setting.
-This tutorial will include additional parts of code like loading and preprocessing,
-defining a model, and other details which are not exclusive to this page (compare
-`Cropped Decoding Tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__). Therefore we
+This tutorial will include additional parts of code like loading and preprocessing of data,
+defining a model, and other details which are not exclusive to this page (see
+`Cropped Decoding Tutorial <./plot_bcic_iv_2a_moabb_cropped.html>`__). Therefore we
 will not further elaborate on these parts and you can feel free to skip them.
 
-In general we distinguish between "usual" training and evaluation and hyperparameter search.
+In general, we distinguish between "usual" training and evaluation and hyperparameter search.
 The tutorial is therefore split into two parts, one for the three different training schemes
 and one for the two different hyperparameter tuning methods.
 
-"""
+.. contents:: This example covers:
+   :local:
+   :depth: 2
 
+"""
 ######################################################################
 # Why should I care about model evaluation?
 # -----------------------------------------
@@ -26,12 +29,30 @@
 # data into two parts, training and testing sets. It sounds like a
 # simple division, right? But the story does not end here.
 #
+# - What are model's parameters?
+#
+# Model's parameters are learnable weights which are used in the
+# extraction of the relevant features and in performing the final inference.
+# In the context of deep learning, these are usually fully connected weights,
+# convolutional kernels, biases, etc.
+#
+# - What are model's hyperparameters?
+#
+# Model's hyperparameters are used to set the capacity (size) of the model
+# and to guide the parameter learning process.
+# In the context of deep learning, examples of the hyperparameters are the
+# number of convolutional layers and the number of convolutional kernels in
+# each of them, the number and size of the fully connected weights,
+# choice of the optimizer and its learning rate, the number of training epochs,
+# choice of the nonlinearities, etc.
+#
+#
 # While developing a ML model you usually have to adjust and tune
 # hyperparameters of your model or pipeline (e.g., number of layers,
 # learning rate, number of epochs). Deep learning models usually have
-# many free parameters; they could be considered complex models with
+# many free parameters; they could be considered as complex models with
 # many degrees of freedom. If you kept using the test dataset to
-# evaluate your adjustmentyou would run into data leakage.
+# evaluate your adjustment, you would run into data leakage.
 #
 # This means that if you use the test set to adjust the hyperparameters
 # of your model, the model implicitly learns or memorizes the test set.
@@ -48,33 +69,39 @@
 # .. warning::
 #    You might recognize that the accuracy gets better throughout
 #    the experiments of this tutorial. The reason behind that is that
-#    we always use the same model with the same paramters in every
+#    we always use the same model with the same parameters in every
 #    segment to keep the tutorial short and readable. If you do your
 #    own experiments you always have to reinitialize the model before
 #    training.
 #
 
 ######################################################################
-# Loading, preprocessing, defining a model, etc.
-# ----------------------------------------------
+# Loading and preprocessing of data, defining a model, etc.
+# ----------------------------------------------------------
+#
 #
-
 
 ######################################################################
-# Loading
-# ~~~~~~~
+# Loading data
+# ~~~~~~~~~~~~~
+#
+# In this example, we load the BCI Competition IV 2a data [1]_, for one
+# subject (subject id 3), using braindecode's wrapper to load via
+# `MOABB library <https://github.com/NeuroTechX/moabb>`__ [2]_.
 #
-
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
 dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
 
 ######################################################################
-# Preprocessing
-# ~~~~~~~~~~~~~
+# Preprocessing data
+# ~~~~~~~~~~~~~~~~~~
+#
+# In this example, preprocessing includes signal rescaling, the bandpass filtering
+# (low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+# the exponential moving mean and variance.
 #
-
 import numpy as np
 
 from braindecode.preprocessing import (
@@ -103,12 +130,28 @@
     ),
 ]
 
-# Transform the data
-preprocess(dataset, preprocessors)
+# Preprocess the data
+preprocess(dataset, preprocessors, n_jobs=-1)
 
 ######################################################################
-# Cut Compute Windows
-# ~~~~~~~~~~~~~~~~~~~
+# Extraction of the Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Extraction of the trials (windows) from the time series is based on the
+# events inside the dataset. One event is the demarcation of the stimulus or
+# the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+# before the corresponding event and the duration of the event itself.
+# #Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the
+# ``trial_stop_offset_seconds`` to 0 [s].
+#
+# We extract from the dataset the sampling frequency, which is the same for
+# all datasets in this case, and we tested it.
+#
+# .. note::
+#    The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are
+#    defined in seconds and need to be converted into samples (multiplication
+#    with the sampling frequency), relative to the event.
+#    This variable is dataset dependent.
 #
 
 from braindecode.preprocessing import create_windows_from_events
@@ -117,11 +160,11 @@
 # Extract sampling frequency, check that they are same in all datasets
 sfreq = dataset.datasets[0].raw.info["sfreq"]
 assert all([ds.raw.info["sfreq"] == sfreq for ds in dataset.datasets])
-# Calculate the trial start offset in samples.
+# Calculate the window start offset in samples.
 trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
 
-# Create windows using braindecode function for this. It needs parameters to define how
-# trials should be used.
+# Create windows using braindecode function for this. It needs parameters to
+# define how windows should be used.
 windows_dataset = create_windows_from_events(
     dataset,
     trial_start_offset_samples=trial_start_offset_samples,
@@ -129,10 +172,37 @@
     preload=True,
 )
 
+
+######################################################################
+# Split dataset into train and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# We can easily split the dataset BCIC IV 2a dataset using additional
+# info stored in the description attribute, in this case the ``session``
+# column. We select ``0train`` for training and ``0test`` for testing.
+# For other datasets, you might have to choose another column and/or column.
+#
+# .. note::
+#    No matter which of the three schemes you use, this initial
+#    two-fold split into train_set and test_set always remains the same.
+#    Remember that you are not allowed to use the test_set during any
+#    stage of training or tuning.
+#
+
+splitted = windows_dataset.split("session")
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
+
+
 ######################################################################
 # Create model
 # ~~~~~~~~~~~~
 #
+# In this tutorial, ShallowFBCSPNet classifier [3]_ is explored. The model
+# training is performed on GPU if it exists, otherwise on CPU.
+#
 
 import torch
 from braindecode.util import set_random_seeds
@@ -146,6 +216,7 @@
 set_random_seeds(seed=seed, cuda=cuda)
 
 n_classes = 4
+classes = list(range(n_classes))
 # Extract number of chans and time steps from dataset
 n_channels = windows_dataset[0][0].shape[0]
 input_window_samples = windows_dataset[0][0].shape[1]
@@ -157,6 +228,9 @@
     final_conv_length="auto",
 )
 
+# Display torchinfo table describing the model
+print(model)
+
 # Send model to GPU
 if cuda:
     model.cuda()
@@ -166,28 +240,6 @@
 # ------------------------------------
 #
 
-######################################################################
-# Split dataset into train and test
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#
-
-######################################################################
-# We can easily split the dataset using additional info stored in the
-# description attribute, in this case the ``session`` column. We
-# select ``session_T`` for training and ``session_E`` for testing.
-# For other datasets, you might have to choose another column.
-#
-# .. note::
-#    No matter which of the three schemes you use, this initial
-#    two-fold split into train_set and test_set always remains the same.
-#    Remember that you are not allowed to use the test_set during any
-#    stage of training or tuning.
-#
-
-splitted = windows_dataset.split("session")
-train_set = splitted["session_T"]
-test_set = splitted["session_E"]
-
 ######################################################################
 # Option 1: Simple Train-Test Split
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -215,7 +267,7 @@
 lr = 0.0625 * 0.01
 weight_decay = 0
 batch_size = 64
-n_epochs = 4
+n_epochs = 2
 
 clf = EEGClassifier(
     model,
@@ -230,12 +282,14 @@
         ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
+    max_epochs=n_epochs,
 )
 # Model training for a specified number of epochs. `y` is None as it is already supplied
 # in the dataset.
-clf.fit(train_set, y=None, epochs=n_epochs)
+clf.fit(train_set, y=None)
 
-# score the Model after training
+# evaluated the model after training
 y_test = test_set.get_metadata().target
 test_acc = clf.score(test_set, y=y_test)
 print(f"Test acc: {(test_acc * 100):.2f}%")
@@ -243,57 +297,40 @@
 ######################################################################
 # Let's visualize the first option with a util function.
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
+# The following figure illustrates split of entire dataset into the
+# training and testing subsets.
+#
+#
+import seaborn as sns
 import matplotlib.pyplot as plt
 from matplotlib.patches import Patch
 
+sns.set(font_scale=1.5)
+
 
-def plot_simple_train_test(ax, windows_dataset, train_set, test_set):
+def plot_simple_train_test(ax, all_dataset, train_set, test_set):
     """Create a sample plot for training-testing split."""
-    braindecode_cmap = ["#3A6190", "#683E00", "#DDF2FF", "#2196F3"]
-
-    ax.scatter(
-        range(len(windows_dataset)),
-        [3.5] * len(windows_dataset),
-        c=braindecode_cmap[0],
-        marker="_",
-        lw=50,
-    )
-
-    ax.scatter(
-        range(len(train_set) + len(test_set)),
-        [0.5] * len(train_set) + [0.5] * len(test_set),
-        c=[braindecode_cmap[1]] * len(train_set)
-        + [braindecode_cmap[2]] * len(test_set),
-        marker="_",
-        lw=50,
-    )
-
-    ax.set(
-        ylim=[-1, 5],
-        yticks=[0.5, 3.5],
-        yticklabels=["Train-Test\nSplit", "Original\nDataset"],
-        xlabel="Number of samples.",
-        title="Train-Test Split",
-    )
-
-    ax.legend(
-        [
-            Patch(color=braindecode_cmap[0]),
-            Patch(color=braindecode_cmap[1]),
-            Patch(color=braindecode_cmap[2]),
-        ],
-        ["Original set", "Training set", "Testing set"],
-        loc=(1.02, 0.8),
-    )
+    bd_cmap = ["#3A6190", "#683E00", "#DDF2FF", "#2196F3"]
+
+    ax.barh("Original\ndataset", len(all_dataset), left=0,
+            height=0.5, color=bd_cmap[0])
+    ax.barh("Train-Test\nsplit", len(train_set), left=0,
+            height=0.5, color=bd_cmap[1])
+    ax.barh("Train-Test\nsplit", len(test_set), left=len(train_set),
+            height=0.5, color=bd_cmap[2])
+
+    ax.invert_yaxis()
+    ax.set(xlabel="Number of samples.", title="Train-Test split")
+    ax.legend(["Original set", "Training set", "Testing set"], loc='lower center',
+              ncols=4, bbox_to_anchor=(0.5, 0.5))
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
     return ax
 
 
-fig, ax = plt.subplots(figsize=(12, 5))
-plot_simple_train_test(
-    ax=ax, windows_dataset=windows_dataset, train_set=train_set, test_set=test_set
-)
-fig.tight_layout()
+fig, ax = plt.subplots(figsize=(12, 8))
+plot_simple_train_test(ax=ax, all_dataset=windows_dataset,
+                       train_set=train_set, test_set=test_set)
+
 ######################################################################
 # Option 2: Train-Val-Test Split
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -332,7 +369,6 @@ def plot_simple_train_test(ax, windows_dataset, train_set, test_set):
 )
 train_subset = Subset(train_set, train_indices)
 val_subset = Subset(train_set, val_indices)
-
 ######################################################################
 # .. note::
 #    The parameter ``shuffle`` is set to ``False``. For time-series
@@ -354,78 +390,47 @@ def plot_simple_train_test(ax, windows_dataset, train_set, test_set):
         ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
+    max_epochs=n_epochs,
 )
-clf.fit(train_subset, y=None, epochs=n_epochs)
+clf.fit(train_subset, y=None)
 
-# score the Model after training (optional)
+# evaluate the model after training and validation
 y_test = test_set.get_metadata().target
 test_acc = clf.score(test_set, y=y_test)
 print(f"Test acc: {(test_acc * 100):.2f}%")
 
-
 ######################################################################
 # Let's visualize the second option with a util function.
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The following figure illustrates split of entire dataset into the
+# training, validation and testing subsets.
+# ``Making more compact plot_train_valid_test function.``
+#
 
 
-def plot_train_valid_test(ax, windows_dataset, train_subset, val_subset, test_set):
+def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):
     """Create a sample plot for training, validation, testing."""
 
-    braindecode_cmap = [
-        "#3A6190",
-        "#683E00",
-        "#2196F3",
-        "#DDF2FF",
-    ]
-    ax.scatter(
-        range(len(windows_dataset)),
-        [3.5] * len(windows_dataset),
-        c=braindecode_cmap[0],
-        marker="_",
-        lw=50,
-    )
-
-    ax.scatter(
-        range(len(train_subset) + len(val_subset) + len(test_set)),
-        [0.5] * len(train_subset) + [0.5] * len(val_subset) + [0.5] * len(test_set),
-        c=[braindecode_cmap[1]] * len(train_subset)
-        + [braindecode_cmap[2]] * len(val_subset)
-        + [braindecode_cmap[3]] * len(test_set),
-        marker="_",
-        lw=50,
-    )
-
-    ax.set(
-        ylim=[-1, 5],
-        yticks=[0.5, 3.5],
-        yticklabels=["Train-Test\nSplit", "Original\nDataset"],
-        xlabel="Number of samples.",
-        title="Train-Validation-Test Split",
-    )
-
-    ax.legend(
-        [
-            Patch(color=braindecode_cmap[0]),
-            Patch(color=braindecode_cmap[1]),
-            Patch(color=braindecode_cmap[2]),
-            Patch(color=braindecode_cmap[3]),
-        ],
-        ["Original set", "Training set", "Validation set", "Testing set"],
-        loc=(1.02, 0.8),
-    )
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
 
+    n_train, n_val, n_test = len(train_subset), len(val_subset), len(test_set)
+    ax.barh("Original\ndataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
+    ax.barh("Train-Test-Valid\nsplit", n_train, left=0, height=0.5, color=bd_cmap[1])
+    ax.barh("Train-Test-Valid\nsplit", n_val, left=n_train, height=0.5, color=bd_cmap[2])
+    ax.barh("Train-Test-Valid\nsplit", n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])
+
+    ax.invert_yaxis()
+    ax.set(xlabel="Number of samples.", title="Train-Test-Valid split")
+    ax.legend(["Original set", "Training set", "Validation set", "Testing set"],
+              loc="lower center", ncols=2, bbox_to_anchor=(0.5, 0.4))
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
     return ax
 
 
 fig, ax = plt.subplots(figsize=(12, 5))
-plot_train_valid_test(
-    ax=ax,
-    windows_dataset=windows_dataset,
-    train_subset=train_subset,
-    val_subset=val_subset,
-    test_set=test_set,
-)
-fig.tight_layout()
+plot_train_valid_test(ax=ax, all_dataset=windows_dataset,
+                      train_subset=train_subset, val_subset=val_subset, test_set=test_set,)
 
 ######################################################################
 # Option 3: k-Fold Cross Validation
@@ -455,9 +460,17 @@ def plot_train_valid_test(ax, windows_dataset, train_subset, val_subset, test_se
 # The ``train_split`` argument has to be set to ``None``, as sklearn
 # will take care of the splitting.
 #
+from skorch.callbacks import LRScheduler
+
+from braindecode import EEGClassifier
 
 from sklearn.model_selection import KFold, cross_val_score
 
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
 clf = EEGClassifier(
     model,
     criterion=torch.nn.NLLLoss,
@@ -471,12 +484,16 @@ def plot_train_valid_test(ax, windows_dataset, train_subset, val_subset, test_se
         ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
+    max_epochs=n_epochs,
 )
 
 train_val_split = KFold(n_splits=5, shuffle=False)
-fit_params = {"epochs": n_epochs}
+# By setting n_jobs=-1, cross-validation is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
 cv_results = cross_val_score(
-    clf, X_train, y_train, scoring="accuracy", cv=train_val_split, fit_params=fit_params
+    clf, X_train, y_train, scoring="accuracy", cv=train_val_split, n_jobs=1
 )
 print(
     f"Validation accuracy: {np.mean(cv_results * 100):.2f}"
@@ -486,113 +503,40 @@ def plot_train_valid_test(ax, windows_dataset, train_subset, val_subset, test_se
 ######################################################################
 # Let's visualize the third option with a util function.
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-braindecode_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF"]
-
-
-def encode_color(value, br_cmap=braindecode_cmap):
-    # Util to encoder color
-    if value == 0:
-        return br_cmap[1]
-    else:
-        return br_cmap[2]
-
-
-def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
-    braindecode_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF"]
-
-    mosaic = """
-      aa
-      BC
-      """
-
-    axes = plt.figure(figsize=(15, 7), constrained_layout=True).subplot_mosaic(
-        mosaic,
-        gridspec_kw={"height_ratios": [1.5, 5], "width_ratios": [3.5, 3.5]},
-    )
-
-    # Generate the training/testing visualizations for each CV split
-    for ii, (tr, tt) in enumerate(cv.split(X=X_train, y=y_train)):
-        # Fill in indices with the training/test groups
-
-        axes["a"].scatter(
-            range(len(windows_dataset)),
-            [3.5] * len(windows_dataset),
-            c=braindecode_cmap[0],
-            marker="_",
-            lw=20,
-        )
-        indices = np.array([np.nan] * len(X_train))
-        indices[tt] = 1
-        indices[tr] = 0
-
-        color_indices = list(map(encode_color, indices))
-
-        # Visualize the results
-        axes["B"].scatter(
-            range(len(indices)),
-            [ii + 0.5] * len(indices),
-            c=color_indices,
-            marker="_",
-            lw=10,
-            vmin=-0.2,
-            vmax=1.2,
-        )
-
-        axes["C"].scatter(
-            range(len(test_set)),
-            [ii + 0.5] * len(test_set),
-            c=braindecode_cmap[3],
-            marker="_",
-            lw=10,
-        )
-
-    axes["a"].set(
-        yticklabels=[""],
-        xlim=[0, len(windows_dataset) + 1],
-        ylabel="Original\nData",
-        ylim=[3.4, 3.6],
-    )
-    axes["a"].yaxis.get_label().set_fontsize(16)
-
-    axes["C"].set(
-        yticks=np.arange(5) + 0.5, yticklabels=[""] * 5, xlim=[0, 300], ylim=[5, -0.2],
-    )
-
-    # Formatting
-    yticklabels = list(range(5))
-
-    axes["B"].set(
-        yticks=np.arange(5) + 0.5,
-        yticklabels=yticklabels,
-        ylabel="CV iteration",
-        ylim=[5, -0.2],
-        xlim=[0, 300],
-    )
-
-    axes["B"].yaxis.get_label().set_fontsize(16)
-
-    axes["a"].set_title("Training, testing with k-Fold Cross Validation", fontsize=15)
-
-    plt.legend(
-        [
-            Patch(color=braindecode_cmap[0]),
-            Patch(color=braindecode_cmap[1]),
-            Patch(color=braindecode_cmap[2]),
-            Patch(color=braindecode_cmap[3]),
-        ],
-        ["Original set", "Training set", "Validation set", "Testing set"],
-        loc=(1.02, 0),
-    )
-    plt.subplots_adjust(wspace=0.075)
-
-
-plot_k_fold(
-    cv=train_val_split,
-    windows_dataset=windows_dataset,
-    X_train=X_train,
-    y_train=y_train,
-    test_set=test_set,
-)
+
+
+def plot_k_fold(ax, cv, all_dataset, X_train, y_train, test_set):
+    """Create a sample plot for training, validation, testing."""
+
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
+
+    ax.barh("Original\nDataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
+
+    # Generate the training/validation/testing data fraction visualizations for each CV split
+    for ii, (tr_idx, val_idx) in enumerate(cv.split(X=X_train, y=y_train)):
+        n_train, n_val, n_test = len(tr_idx), len(val_idx), len(test_set)
+        n_train2 = n_train + n_val - max(val_idx) - 1
+        ax.barh("cv" + str(ii + 1), min(val_idx), left=0, height=0.5, color=bd_cmap[1])
+        ax.barh("cv" + str(ii + 1), n_val, left=min(val_idx), height=0.5, color=bd_cmap[2])
+        ax.barh("cv" + str(ii + 1), n_train2, left=max(val_idx) + 1, height=0.5, color=bd_cmap[1])
+        ax.barh("cv" + str(ii + 1), n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])
+
+    ax.invert_yaxis()
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
+    ax.set(xlabel="Number of samples.", title="KFold Train-Test-Valid split")
+    ax.legend([Patch(color=bd_cmap[i]) for i in range(4)],
+              ["Original set", "Training set", "Validation set", "Testing set"],
+              loc="lower center", ncols=2)
+    ax.text(-0.07, 0.45, 'Train-Valid-Test split', rotation=90,
+            verticalalignment='center', horizontalalignment='left', transform=ax.transAxes)
+    return ax
+
+
+fig, ax = plt.subplots(figsize=(15, 7))
+plot_k_fold(ax, cv=train_val_split, all_dataset=windows_dataset,
+            X_train=X_train, y_train=y_train, test_set=test_set,)
+
+
 ######################################################################
 # How to tune your hyperparameters
 # --------------------------------
@@ -601,12 +545,12 @@ def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
 ######################################################################
 # One way to do hyperparameter tuning is to run each configuration
 # manually (via Option 2 or 3 from above) and compare the validation
-# performance afterwards. In the early stages of your developement
+# performance afterwards. In the early stages of your development
 # process this might be sufficient to get a rough understanding of
 # how your hyperparameter should look like for your model to converge.
 # However, this manual tuning process quickly becomes messy as the
 # number of hyperparameters you want to (jointly) tune increases.
-# Therefore you sould automate this process. We will present two
+# Therefore you should, automate this process. We will present two
 # different options, analogous to Option 2 and 3 from above.
 #
 
@@ -616,13 +560,14 @@ def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
 #
 
 ######################################################################
-# We will again make use of the sklearn library to do the hyperparameter
-# search. `GridSearchCV <https://scikit-learn.org/stable/modules/
-# generated/sklearn.model_selection.GridSearchCV.html>`__ will perform
-# a Grid Search over the parameters specified in ``param_grid``.
-# We use grid search as a simple example, but you can use `any strategy
-# you want <https://scikit-learn.org/stable/modules/classes.html#
-# module-sklearn.model_selection>`__).
+# We will again make use of the `sklearn <https://scikit-learn.org/stable/>`__
+# library to do the hyperparameter search. `GridSearchCV
+# <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>`__
+# will perform a Grid Search over the parameters specified in ``param_grid``.
+# We use grid search for the model selection as a simple example, but you can use other strategies
+# as well.
+# (`List of the sklearn classes for model selection
+# <https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection>`__.)
 #
 
 import pandas as pd
@@ -635,6 +580,10 @@ def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
 param_grid = {
     "optimizer__lr": [0.00625, 0.000625],
 }
+
+# By setting n_jobs=-1, grid search is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
 search = GridSearchCV(
     estimator=clf,
     param_grid=param_grid,
@@ -644,17 +593,16 @@ def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
     refit=True,
     verbose=1,
     error_score="raise",
+    n_jobs=1,
 )
 
-search.fit(X_train, y_train, **fit_params)
+search.fit(X_train, y_train)
 search_results = pd.DataFrame(search.cv_results_)
 
 best_run = search_results[search_results["rank_test_score"] == 1].squeeze()
-print(
-    f"Best hyperparameters were {best_run['params']} which gave a validation "
-    f"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training "
-    f"accuracy of {best_run['mean_train_score'] * 100:.2f}%)."
-)
+
+best_parameters = best_run["params"]
+
 
 ######################################################################
 # Option 2: k-Fold Cross Validation
@@ -663,6 +611,25 @@ def plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):
 
 ######################################################################
 # To perform a full k-Fold CV just replace ``train_val_split`` from
-# above with the ``KFold`` cross-validator from sklearn.
+# above with the `KFold <https://scikit-learn.org/stable/modules/generated/
+# sklearn.model_selection.KFold.html>`__ cross-validator from sklearn.
 
 train_val_split = KFold(n_splits=5, shuffle=False)
+
+#######################################################################
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/0.8/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb b/0.8/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb
new file mode 100644
index 00000000..954b66b8
--- /dev/null
+++ b/0.8/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb
@@ -0,0 +1,212 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Convolutional neural network regression model on fake data.\n\nThis example shows how to create a CNN regressor from a CNN classifier by removing `softmax`\nfunction from the classifier's output layer and how to train it on a fake regression dataset.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#          Sara Sedlar <sara.sedlar@gmail.com>\n# License: BSD-3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fake regression data\nFunction for generation of the fake regression dataset generates `n_fake_recs` recordings,\neach containing sinusoidal signals with Gaussian noise. Each fake recording signal has\n`n_fake_chs` channels, it lasts `fake_duration` [s] and it is sampled with `fake_sfreq` [Hz].\nThe recordings are split into train, validation and testing sessions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport pandas as pd\n\nfrom braindecode.datasets import BaseConcatDataset, BaseDataset\nfrom braindecode.util import create_mne_dummy_raw"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Function for generating fake regression data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq,\n                            fake_duration, n_fake_targets,\n                            fake_data_split=[0.6, 0.2, 0.2]):\n    \"\"\"Generate a fake regression dataset.\n\n    Parameters\n    ----------\n    n_fake_recs : int\n        Number of fake recordings.\n    n_fake_chs : int\n        Number of fake EEG channels.\n    fake_sfreq : float\n        Fake sampling frequency in Hz.\n    fake_duration : float\n        Fake recording duration in seconds.\n    n_fake_targets : int\n        Number of targets.\n    fake_data_split : list\n        List of train/valid/test subset fractions.\n\n    Returns\n    -------\n    dataset : BaseConcatDataset object\n        The generated dataset object.\n    \"\"\"\n\n    datasets = []\n    for i in range(n_fake_recs):\n        if i < int(fake_data_split[0] * n_fake_recs):\n            target_subset = \"train\"\n        elif i < int((1 - fake_data_split[2]) * n_fake_recs):\n            target_subset = \"valid\"\n        else:\n            target_subset = \"test\"\n        raw, _ = create_mne_dummy_raw(n_channels=n_fake_chs,\n                                      n_times=fake_duration * fake_sfreq,\n                                      sfreq=fake_sfreq)\n\n        target = np.random.randint(0, 10, n_fake_targets)\n        for j in range(n_fake_targets):\n            x = np.sin(2 * np.pi * target[j] * raw.times)\n            raw._data += np.expand_dims(x, axis=0)\n\n        if n_fake_targets == 1:\n            target = target[0]\n        fake_description = pd.Series(data=[target, target_subset],\n                                     index=[\"target\", \"session\"])\n        datasets.append(\n            BaseDataset(raw, fake_description, target_name=\"target\"))\n\n    return BaseConcatDataset(datasets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generating fake regression dataset\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_fake_rec = 20\nn_fake_chans = 21\nfake_sfreq = 100\nfake_duration = 30\nn_fake_targets = 1\ndataset = fake_regression_dataset(n_fake_recs=n_fake_rec,\n                                  n_fake_chs=n_fake_chans,\n                                  fake_sfreq=fake_sfreq,\n                                  fake_duration=fake_duration,\n                                  n_fake_targets=n_fake_targets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Defining a CNN regression model\n\nChoosing and defining a CNN classifier, `ShallowFBCSPNet` or `Deep4Net`, introduced in [1]_.\nTo convert a classifier to a regressor, `softmax` function is removed from its output layer.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.util import set_random_seeds\nfrom braindecode.models import Deep4Net\nfrom braindecode.models import ShallowFBCSPNet\nimport torch\n\n# Choosing a CNN model\nmodel_name = \"shallow\"  # 'shallow' or 'deep'\n\n# Defining a CNN model\nif model_name in [\"shallow\", \"Shallow\", \"ShallowConvNet\"]:\n    model = ShallowFBCSPNet(in_chans=n_fake_chans,\n                            n_classes=n_fake_targets,\n                            input_window_samples=fake_sfreq * fake_duration,\n                            n_filters_time=40, n_filters_spat=40,\n                            final_conv_length=35,\n                            add_log_softmax=False,)\nelif model_name in [\"deep\", \"Deep\", \"DeepConvNet\"]:\n    model = Deep4Net(in_chans=n_fake_chans, n_classes=n_fake_targets,\n                     input_window_samples=fake_sfreq * fake_duration,\n                     n_filters_time=25, n_filters_spat=25,\n                     stride_before_pool=True,\n                     n_filters_2=n_fake_chans * 2,\n                     n_filters_3=n_fake_chans * 4,\n                     n_filters_4=n_fake_chans * 8,\n                     final_conv_length=1,\n                     add_log_softmax=False, )\nelse:\n    raise ValueError(f'{model_name} unknown')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choosing between GPU and CPU processors\nBy default, model's training and evaluation take place at GPU if it exists, otherwise on CPU.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cuda = torch.cuda.is_available()\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Setting a random seed\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data windowing\nWindowing data with a sliding window into the epochs of the size `window_size_samples`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models.util import to_dense_prediction_model, get_output_shape\nfrom braindecode.preprocessing import create_fixed_length_windows\n\nwindow_size_samples = fake_sfreq * fake_duration // 3\nto_dense_prediction_model(model)\nn_preds_per_input = get_output_shape(model, n_fake_chans, window_size_samples)[\n    2]\nwindows_dataset = create_fixed_length_windows(dataset,\n                                              start_offset_samples=0,\n                                              stop_offset_samples=0,\n                                              window_size_samples=window_size_samples,\n                                              window_stride_samples=n_preds_per_input,\n                                              drop_last_window=False,\n                                              preload=True)\n\n# Splitting windowed data into train, valid and test subsets.\nsplits = windows_dataset.split(\"session\")\ntrain_set = splits[\"train\"]\nvalid_set = splits[\"valid\"]\ntest_set = splits[\"test\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model training\nModel is trained by minimizing MSE loss between ground truth and estimated value averaged over\na period of time using AdamW optimizer [2]_, [3]_. Learning rate is managed by CosineAnnealingLR\nlearning rate scheduler.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode import EEGRegressor\nfrom braindecode.training.losses import CroppedLoss\nfrom skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nbatch_size = 4\nn_epochs = 3\noptimizer_lr = 0.001\noptimizer_weight_decay = 0.0\nregressor = EEGRegressor(model, cropped=True,\n                         criterion=CroppedLoss,\n                         criterion__loss_function=torch.nn.functional.mse_loss,\n                         optimizer=torch.optim.AdamW,\n                         optimizer__lr=optimizer_lr,\n                         optimizer__weight_decay=optimizer_weight_decay,\n                         train_split=predefined_split(valid_set),\n                         iterator_train__shuffle=True,\n                         batch_size=batch_size,\n                         callbacks=[\"neg_root_mean_squared_error\",\n                                    (\"lr_scheduler\",\n                                     LRScheduler('CosineAnnealingLR',\n                                                 T_max=n_epochs - 1))],\n                         device=device, )\nregressor.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model evaluation\nPlotting training and validation losses and negative root mean square error\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 5))\naxes[0].set_title(\"Train and valid losses\")\naxes[0].plot(regressor.history[:, \"train_loss\"])\naxes[0].plot(regressor.history[:, \"valid_loss\"])\naxes[0].set_xlabel(\"Epochs\")\naxes[0].set_ylabel(\"Cropped MSE loss\")\naxes[0].legend([\"Train\", \"Valid\"])\n\naxes[1].set_title(\"Train and valid errors\")\naxes[1].plot(regressor.history[:, \"train_neg_root_mean_squared_error\"])\naxes[1].plot(regressor.history[:, \"valid_neg_root_mean_squared_error\"])\naxes[1].set_xlabel(\"Epochs\")\naxes[1].set_ylabel(\"Negative RMSE\")\naxes[1].legend([\"Train\", \"Valid\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model testing\nPlotting a scatter plot of estimated versus target values and corresponding trend line.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 1, figsize=(5, 5))\ny_estim = np.ravel(regressor.predict(test_set))\ny_gt = test_set.get_metadata()[\"target\"].to_numpy()\n\n_ = axes.scatter(y_gt, y_estim)\n_ = axes.set_ylabel(\"Estimated targets.\")\n_ = axes.set_xlabel(\"Ground truth targets.\")\n\nz = np.polyfit(y_gt, y_estim, 1)\np = np.poly1d(z)\nplt.plot(y_gt, p(y_gt), \"r--\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Human brain mapping, 38(11), 5391-5420.\n\n.. [2] Kingma, Diederik P., and Jimmy Ba.\n       \"Adam: A method for stochastic optimization.\" arXiv preprint arXiv:1412.6980 (2014).\n\n.. [3] Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.\n       \"On the convergence of adam and beyond.\" arXiv preprint arXiv:1904.09237 (2019).\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/09bd6a6e5f249aa69470feea45e3b5b0/plot_load_save_datasets.py b/0.8/_downloads/76b6af988880f614494e6948dcfe4128/plot_load_save_datasets.py
similarity index 100%
rename from stable/_downloads/09bd6a6e5f249aa69470feea45e3b5b0/plot_load_save_datasets.py
rename to 0.8/_downloads/76b6af988880f614494e6948dcfe4128/plot_load_save_datasets.py
diff --git a/stable/_downloads/7132d1f42727a7844635064390fdd264/plot_sleep_staging_eldele2021.ipynb b/0.8/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb
similarity index 78%
rename from stable/_downloads/7132d1f42727a7844635064390fdd264/plot_sleep_staging_eldele2021.ipynb
rename to 0.8/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb
index 93a2e314..87941c52 100644
--- a/stable/_downloads/7132d1f42727a7844635064390fdd264/plot_sleep_staging_eldele2021.ipynb
+++ b/0.8/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sleep staging on the Sleep Physionet dataset using Eldele2021\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We use the attention-based model from [1]_ with the time distributed approach of [2]_\nto learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset [3]_ [4]_.\n\n## References\n.. [1] E. Eldele et al., \"An Attention-Based Deep Learning Approach for Sleep Stage\n        Classification With Single-Channel EEG,\" in IEEE Transactions on Neural Systems and\n        Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.\n\n.. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n"
+        "\n# Sleep staging on the Sleep Physionet dataset using Eldele2021\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We use the attention-based model from [1]_ with the time distributed approach of [2]_\nto learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset [3]_ [4]_.\n"
       ]
     },
     {
@@ -62,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {
@@ -134,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
+        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
       ]
     },
     {
@@ -188,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerEldele2021, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerEldele2021(\n    sfreq,\n    n_classes=n_classes,\n    input_size_s=input_size_samples / sfreq,\n    return_feats=True)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerEldele2021, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerEldele2021(\n    sfreq,\n    n_outputs=n_classes,\n    n_times=input_size_samples,\n    return_feats=True,\n)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
       ]
     },
     {
@@ -206,7 +195,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=np.unique(y_train)\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
       ]
     },
     {
@@ -242,7 +231,25 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import confusion_matrix, classification_report\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nprint(confusion_matrix(y_true, y_pred))\nprint(classification_report(y_true, y_pred))"
+        "from sklearn.metrics import confusion_matrix, classification_report\nfrom braindecode.visualization import plot_confusion_matrix\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\nplot_confusion_matrix(confusion_mat=confusion_mat)\n#                      class_names=['Wake', 'N1', 'N2', 'N3', 'N4', 'REM'])\n\nprint(classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true, color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
       ]
     },
     {
@@ -251,6 +258,13 @@
       "source": [
         "The model was able to learn despite the low amount of data that was available\n(only two recordings in this example) and reached a balanced accuracy of\nabout 43% in a 5-class classification task (chance-level = 20%) on held-out\ndata over 10 epochs.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To further improve performance, the number of epochs should be increased.\n   It has been reduced here for faster run-time in document generation. In\n   testing, 10 epochs provided reasonable performance with around 89% balanced\n   accuracy on training data and around 43% on held out validation data.\n   Increasing the number of training recordings and optimizing the hyperparameters\n   will also help increase performance</p></div>\n\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] E. Eldele et al., \"An Attention-Based Deep Learning Approach for Sleep Stage\n        Classification With Single-Channel EEG,\" in IEEE Transactions on Neural Systems and\n        Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.\n\n.. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -269,7 +283,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/698f5f9879e8de86ce2b89d52213c004/plot_tuh_discrete_multitarget.ipynb b/0.8/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb
similarity index 63%
rename from stable/_downloads/698f5f9879e8de86ce2b89d52213c004/plot_tuh_discrete_multitarget.ipynb
rename to 0.8/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb
index 9a174581..9fdec908 100644
--- a/stable/_downloads/698f5f9879e8de86ce2b89d52213c004/plot_tuh_discrete_multitarget.ipynb
+++ b/0.8/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Multiple discrete targets with the TUH EEG Corpus\n\nIn this example, we showcase usage of multiple discrete targets per recording\nwith the TUH EEG Corpus.\n"
+        "\n# Multiple discrete targets with the TUH EEG Corpus\n\nWelcome to this tutorial where we demonstrate how to work with multiple discrete\n targets for each recording in the TUH EEG Corpus. We'll guide you through the\n process step by step.\n"
       ]
     },
     {
@@ -26,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom torch.utils.data import DataLoader\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import create_fixed_length_windows\n\nmne.set_log_level('ERROR')  # avoid messages everytime a window is extracted"
+        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom torch.utils.data import DataLoader\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import create_fixed_length_windows\n\n# Setting Logging Level\n# ----------------------\n#\n# We'll set the logging level to 'ERROR' to avoid excessive messages when\n# extracting windows:\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
       ]
     },
     {
@@ -51,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We start by creating a TUH dataset. Instead of just a str, we give it\nmultiple strings as target names. Each of the strings has to exist as a\ncolumn in the description DataFrame.\n\n"
+        "## Creating Temple University Hospital (TUH) EEG Corpus Dataset\n\nWe start by creating a TUH dataset. Instead of just a `str, we give it\nmultiple strings as target names. Each of the strings has to exist as a\ncolumn in the description DataFrame.\n\n"
       ]
     },
     {
@@ -62,14 +51,14 @@
       },
       "outputs": [],
       "source": [
-        "TUH_PATH = 'please insert actual path to data here'\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=('age', 'gender'),  # use both age and gender as decoding target\n    preload=False,\n    add_physician_reports=False,\n)\ntuh.description"
+        "TUH_PATH = 'please insert actual path to data here'\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=('age', 'gender'),  # use both age and gender as decoding target\n    preload=False,\n    add_physician_reports=False,\n)\nprint(tuh.description)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Iterating through the dataset gives x as ndarray(n_channels x 1) as well as\nthe target as [age of the subject, gender of the subject]. Let's look at the last example\nas it has more interesting age/gender labels (compare to the last row of the dataframe above).\n\n"
+        "## Exploring Data\n\nIterating through the dataset gives `x` as an ndarray with shape\n`(n_channels x 1)` and `y` as a list containing `[age of the subject, gender\nof the subject]`.\nLet's look at the last example as it has more interesting age/gender labels\n(compare to the last row of the dataframe above).\n\n"
       ]
     },
     {
@@ -80,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "x, y = tuh[-1]\nprint('x:', x)\nprint('y:', y)"
+        "x, y = tuh[-1]\n\nprint(f'{x=}\\n{y=}')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We will skip preprocessing steps for now, since it is not the aim of this\nexample. Instead, we will directly create compute windows. We specify a\nmapping from genders 'M' and 'F' to integers, since this is required for\ndecoding.\n\n"
+        "## Creating Windows\n\nWe will skip preprocessing steps for now, since it is not the aim of this\nexample. Instead, we will directly create compute windows. We specify a\nmapping from genders 'M' and 'F' to integers, since this is required for\ndecoding.\n\n"
       ]
     },
     {
@@ -105,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Iterating through the dataset gives x as ndarray(n_channels x 1000), y as\n[age, gender], and ind. Let's look at the last example again.\n\n"
+        "## Exploring Windows\n\nIterating through the dataset gives `x` as an ndarray with shape\n`(n_channels x 1000)`, `y` as `[age, gender]`, and `ind`.\nLet's look at the last example again.\n\n"
       ]
     },
     {
@@ -116,14 +105,14 @@
       },
       "outputs": [],
       "source": [
-        "x, y, ind = tuh_windows[-1]\nprint('x:', x)\nprint('y:', y)\nprint('ind:', ind)"
+        "x, y, ind = tuh_windows[-1]\nprint(f'{x=}\\n{y=}\\n{ind=}')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We give the dataset to a pytorch DataLoader, such that it can be used for\nmodel training.\n\n"
+        "## DataLoader for Model Training\n\nWe give the dataset to a pytorch DataLoader, such that it can be used for\nmodel training.\n\n"
       ]
     },
     {
@@ -141,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Iterating through the DataLoader gives batch_X as tensor(4 x n_channels x\n1000), batch_y as [tensor([4 x age of subject]), tensor([4 x gender of\nsubject])], and batch_ind. We will iterate to the end to look at the last example\nagain.\n\n"
+        "## Exploring DataLoader\n\nWhen iterating through the DataLoader, we get `batch_X` as a tensor with shape\n`(4 x n_channels x 1000)`, `batch_y` as `[tensor([4 x age of subject]),\ntensor([4 x gender of subject])]`, and `batch_ind`. To view the last example,\nsimply iterate through the DataLoader:\n\n"
       ]
     },
     {
@@ -152,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "for batch_X, batch_y, batch_ind in dl:\n    pass\nprint('batch_X:', batch_X)\nprint('batch_y:', batch_y)\nprint('batch_ind:', batch_ind)"
+        "for batch_X, batch_y, batch_ind in dl:\n    pass\n\nprint(f'{batch_X=}\\n{batch_y=}\\n{batch_ind=}')"
       ]
     }
   ],
@@ -172,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/865f9cabb97769a6672829cdd4848751/plot_mne_dataset_example.py b/0.8/_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py
similarity index 100%
rename from stable/_downloads/865f9cabb97769a6672829cdd4848751/plot_mne_dataset_example.py
rename to 0.8/_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py
diff --git a/stable/_downloads/93aa63aa0ed0b3ee87caaf393a756a8c/plot_sleep_staging_usleep.ipynb b/0.8/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb
similarity index 74%
rename from stable/_downloads/93aa63aa0ed0b3ee87caaf393a756a8c/plot_sleep_staging_usleep.ipynb
rename to 0.8/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb
index 7495e335..d381112d 100644
--- a/stable/_downloads/93aa63aa0ed0b3ee87caaf393a756a8c/plot_sleep_staging_usleep.ipynb
+++ b/0.8/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Sleep staging on the Sleep Physionet dataset using U-Sleep network\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Please take a look at the simpler sleep staging example\n    `here <sphx_glr_auto_examples_plot_sleep_staging.py>`\n    before going through this example. The current example uses a more complex\n    architecture and a sequence-to-sequence (seq2seq) approach.</p></div>\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the U-Sleep approach of [1]_ to learn on sequences of EEG\nwindows using the openly accessible Sleep Physionet dataset [2]_ [3]_.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>The example is written to have a very short excecution time.\n    This number of epochs is here too small and very few recordings are used.\n    To obtain competitive results you need to use more data and more epochs.</p></div>\n\n## References\n.. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.\n       U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).\n       https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n"
+        "\n# Sleep staging on the Sleep Physionet dataset using U-Sleep network\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Please take a look at the simpler sleep staging example\n    `here <sphx_glr_auto_examples_plot_sleep_staging.py>`\n    before going through this example. The current example uses a more complex\n    architecture and a sequence-to-sequence (seq2seq) approach.</p></div>\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the U-Sleep approach of [1]_ to learn on sequences of EEG\nwindows using the openly accessible Sleep Physionet dataset [2]_ [3]_.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>The example is written to have a very short execution time.\n    This number of epochs is here too small and very few recordings are used.\n    To obtain competitive results you need to use more data and more epochs.</p></div>\n"
       ]
     },
     {
@@ -80,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import create_windows_from_events\n\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4,\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping,\n)"
+        "from braindecode.preprocessing import create_windows_from_events\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4,\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping,\n)"
       ]
     },
     {
@@ -116,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows; originally 35 in paper\nn_windows_stride = 3  # Non-overlapping sequences\n\ntrain_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint(len(train_sampler))\nprint(len(valid_sampler))"
+        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows; originally 35 in paper\nn_windows_stride = 3  # Non-overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint(len(train_sampler))\nprint(len(valid_sampler))"
       ]
     },
     {
@@ -152,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import USleep\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nin_chans, input_size_samples = train_set[0][0].shape\n\nmodel = USleep(\n    in_chans=in_chans,\n    sfreq=sfreq,\n    depth=12,\n    with_skip_connection=True,\n    n_classes=n_classes,\n    input_size_s=input_size_samples / sfreq,\n    apply_softmax=False\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import USleep\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\nclasses = list(range(n_classes))\n# Extract number of channels and time steps from dataset\nin_chans, input_size_samples = train_set[0][0].shape\nmodel = USleep(\n    n_chans=in_chans,\n    sfreq=sfreq,\n    depth=12,\n    with_skip_connection=True,\n    n_outputs=n_classes,\n    n_times=input_size_samples\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
       ]
     },
     {
@@ -170,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\nfrom sklearn.metrics import balanced_accuracy_score\n\n\ndef balanced_accuracy_multi(model, X, y):\n    y_pred = model.predict(X)\n    return balanced_accuracy_score(y.flatten(), y_pred.flatten())\n\n\ntrain_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=True,\n    name='train_bal_acc',\n    lower_is_better=False,\n)\nvalid_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=False,\n    name='valid_bal_acc',\n    lower_is_better=False,\n)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\nfrom sklearn.metrics import balanced_accuracy_score\n\n\ndef balanced_accuracy_multi(model, X, y):\n    y_pred = model.predict(X)\n    return balanced_accuracy_score(y.flatten(), y_pred.flatten())\n\n\ntrain_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=True,\n    name='train_bal_acc',\n    lower_is_better=False,\n)\nvalid_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=False,\n    name='valid_bal_acc',\n    lower_is_better=False,\n)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=classes,\n)\n# Deactivate the default valid_acc callback:\nclf.set_params(callbacks__valid_acc=None)\n\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
       ]
     },
     {
@@ -206,7 +195,25 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.metrics import confusion_matrix, classification_report\n\ny_true = np.array([valid_set[i][1] for i in valid_sampler])\ny_pred = clf.predict(valid_set)\n\nprint(confusion_matrix(y_true.flatten(), y_pred.flatten()))\nprint(classification_report(y_true.flatten(), y_pred.flatten()))"
+        "from braindecode.visualization import plot_confusion_matrix\nfrom sklearn.metrics import confusion_matrix, classification_report\n\ny_true = np.array([valid_set[i][1] for i in valid_sampler])\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true.flatten(), y_pred.flatten())\n\nplot_confusion_matrix(confusion_mat=confusion_mat,\n                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])\n\nprint(classification_report(y_true.flatten(), y_pred.flatten()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true.flatten(), color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
       ]
     },
     {
@@ -215,6 +222,13 @@
       "source": [
         "Our model was able to learn, as shown by the decreasing training and\nvalidation loss values, despite the low amount of data that was available\n(only two recordings in this example). To further improve performance, more\nrecordings should be included in the training set, the model should be\ntrained for more epochs and hyperparameters should be optimized.\n\n"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.\n       U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).\n       https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
     }
   ],
   "metadata": {
@@ -233,7 +247,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py b/0.8/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py
new file mode 100644
index 00000000..0318ffd1
--- /dev/null
+++ b/0.8/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py
@@ -0,0 +1,116 @@
+"""
+Split Dataset Example
+=====================
+
+In this example, we aim to show multiple ways of how you can split your datasets for
+training, testing, and evaluating your models.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import create_windows_from_events
+
+###############################################################################
+# Loading the dataset
+# -------------------------------------
+#
+# Firstly, we create a dataset using the braindecode class <MOABBDataset> to load
+# it fetched from MOABB. In this example, we're using Dataset 2a from BCI
+# Competition IV.
+
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+###############################################################################
+# Splitting
+# -------------------------------------
+#
+# By description information
+# ~~~~~~~~~~~~~
+#
+# The class <MOABBDataset> has a pandas DataFrame containing additional
+# description of its internal datasets, which can be used to help splitting the data
+# based on recording information, such as subject, session, and run of each trial.
+
+dataset.description
+
+###############################################################################
+# Here, we're splitting the data based on different runs. The method split returns
+# a dictionary with string keys corresponding to unique entries in the description
+# DataFrame column.
+
+splits = dataset.split("run")
+print(splits)
+splits["4"].description
+
+###############################################################################
+# By row index
+# ~~~~~~~~~~~~~
+#
+# Another way we can split the dataset is based on a list of integers corresponding to
+# rows in the description. In this case, the returned dictionary will have
+# '0' as the only key.
+
+splits = dataset.split([0, 1, 5])
+print(splits)
+splits["0"].description
+
+###############################################################################
+# However, if we want multiple splits based on indices, we can also define a list
+# containing lists of integers. In this case, the dictionary will have string keys
+# representing the index of the dataset split in the order of the given list of
+# integers.
+
+splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
+print(splits)
+splits["2"].description
+
+###############################################################################
+# You can also name each split in the output dictionary by specifying the keys
+# of each list of indexes in the input dictionary:
+
+splits = dataset.split(
+    {"train": [0, 1, 5], "valid": [2, 3, 4], "test": [6, 7, 8, 9, 10, 11]}
+)
+print(splits)
+splits["test"].description
+
+###############################################################################
+# Observation
+# -------------------------------------
+#
+# Similarly, we can split datasets after creating windows using the same methods.
+
+windows = create_windows_from_events(
+    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)
+
+###############################################################################
+
+# Splitting by different runs
+print("Using description info")
+splits = windows.split("run")
+print(splits)
+print()
+
+# Splitting by row index
+print("Splitting by row index")
+splits = windows.split([4, 8])
+print(splits)
+print()
+
+print("Multiple row index split")
+splits = windows.split([[4, 8], [5, 9, 11]])
+print(splits)
+print()
+
+# Specifying output's keys
+print("Specifying keys")
+splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))
+print(splits)
diff --git a/stable/_downloads/9dd4851efc4f3e6584054e947df0f447/plot_bcic_iv_4_ecog_trial.py b/0.8/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py
similarity index 96%
rename from stable/_downloads/9dd4851efc4f3e6584054e947df0f447/plot_bcic_iv_4_ecog_trial.py
rename to 0.8/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py
index f2cf848b..6e1aeb8c 100644
--- a/stable/_downloads/9dd4851efc4f3e6584054e947df0f447/plot_bcic_iv_4_ecog_trial.py
+++ b/0.8/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py
@@ -78,8 +78,9 @@
 #
 
 
-from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor)
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
 
 low_cut_hz = 1.  # low cut frequency for filtering
 high_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG
@@ -193,8 +194,8 @@
 # `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
 
 
-from braindecode.util import set_random_seeds
 from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
 
 cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
 device = 'cuda' if cuda else 'cpu'
@@ -219,16 +220,9 @@
     n_out_chans,
     input_window_samples=input_window_samples,
     final_conv_length='auto',
+    add_log_softmax=False,
 )
 
-# We are removing the softmax layer to make it a regression model
-new_model = torch.nn.Sequential()
-for name, module_ in model.named_children():
-    if "softmax" in name:
-        continue
-    new_model.add_module(name, module_)
-model = new_model
-
 # Send model to GPU
 if cuda:
     model.cuda()
@@ -252,7 +246,7 @@
 #    encourage you to perform your own hyperparameter and preprocessing optimization using
 #    cross validation on your training data.
 #
-from skorch.callbacks import LRScheduler, EpochScoring
+from skorch.callbacks import EpochScoring, LRScheduler
 from skorch.helper import predefined_split
 from mne import set_log_level
 
@@ -262,7 +256,7 @@
 lr = 0.0625 * 0.01
 weight_decay = 0
 batch_size = 64
-n_epochs = 8
+n_epochs = 2
 
 
 # Function to compute Pearson correlation coefficient
@@ -314,19 +308,18 @@ def pearson_r_score(net, dataset, y):
 
 
 ######################################################################
-# We plot target and predicted finger flexion on training, validation, adn test sets.
+# We plot target and predicted finger flexion on training, validation, and test sets.
 #
 # .. note::
-#    The model is trained and validated on limited dataset (to decrease the time neded to run
+#    The model is trained and validated on limited dataset (to decrease the time needed to run
 #    this example) which does not contain diverse dataset in terms of fingers flexions and may
 #    cause overfitting. To obtain better results use whole dataset as well as improve the decoding
 #    pipeline which may be not optimal for ECoG.
 #
 import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
 import pandas as pd
+from matplotlib.lines import Line2D
 
-plt.style.use('seaborn')
 fig, axes = plt.subplots(3, 1, figsize=(8, 9))
 
 axes[0].set_title('Training dataset')
diff --git a/stable/_downloads/e42b5bd97033be02521040b0d5ee21c3/plot_mne_dataset_example.ipynb b/0.8/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb
similarity index 93%
rename from stable/_downloads/e42b5bd97033be02521040b0d5ee21c3/plot_mne_dataset_example.ipynb
rename to 0.8/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb
index a2722182..85429d25 100644
--- a/stable/_downloads/e42b5bd97033be02521040b0d5ee21c3/plot_mne_dataset_example.ipynb
+++ b/0.8/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -107,7 +96,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb b/0.8/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb
new file mode 100644
index 00000000..baeaad66
--- /dev/null
+++ b/0.8/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb
@@ -0,0 +1,253 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Basic Brain Decoding on EEG Data\n\nThis tutorial shows you how to train and test deep learning models with\nBraindecode in a classical EEG setting: you have trials of data with\nlabels (e.g., Right Hand, Left Hand, etc.).\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preparing the data\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading the dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load the data. In this tutorial, we load the BCI Competition\nIV 2a data [1]_ using braindecode's wrapper to load via\n[MOABB library](https://github.com/NeuroTechX/moabb)_ [2]_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Generally, braindecode prepocessing is directly applied to the loaded\n   data, and not applied on-the-fly as transformations, such as in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from numpy import multiply\n\nfrom braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extracting Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we extract compute windows from the signals, these will be the inputs\nto the deep networks during training. In the case of trialwise\ndecoding, we just have to decide if we want to include some part\nbefore and/or after the trial. For our work with this dataset,\nit was often beneficial to also include the 500 ms before the trial.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Splitting the dataset into training and validation sets\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``T`` for training and ``test`` for validation.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating a model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [3]_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model = model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model Training\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we will train the network! ``EEGClassifier`` is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom skorch [NeuralNetClassifier](https://skorch.readthedocs.io/en/stable/classifier.html#)_,\nso the training logic is the same as in [Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n   have found to work well for motor decoding, however we strongly\n   encourage you to perform your own hyperparameter optimization using\n   cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\n\n# We found these values to be good for the shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\", (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for the specified number of epochs. `y` is None as it is\n# already supplied in the dataset.\n_ = clf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting Results\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we use the history stored by Skorch throughout training to plot\naccuracy and loss curves.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting a  Confusion Matrix\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we generate a confusion matrix as in [3]_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = windows_dataset.datasets[0].window_kwargs[0][1]['mapping']\n# sort the labels by values (values are integer class labels)\nlabels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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new file mode 100644
index 00000000..074a3e38
--- /dev/null
+++ b/0.8/_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb
@@ -0,0 +1,257 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Hyperparameter tuning with scikit-learn\n\nThe braindecode provides some compatibility with\n[scikit-learn](https://scikit-learn.org/stable/)_. This allows us\nto use scikit-learn functionality to find the best hyperparameters for our\nmodel. This is especially useful to tune hyperparameters or\nparameters for one decoding task or a specific dataset.\n\n.. topic:: Why do you need to tune the neural networks model?\n\n    Deep learning models are often sensitive to the choice of hyperparameters\n    and parameters. Hyperparameters are the parameters set before\n    training the model. The hyeperparameters determine (1) the capacity of the model,\n    e.g. its depth (the number of layers) and its width (the number of\n    convolutional kernels, sizes of fully connected layers) and (2) the\n    learning process via the choice of optimizer and its learning rate,\n    the number of epochs, the batch size, the choice of non-linearities,\n    the strategies to initialize the learning weights, etc.\n    On the other hand, parameters are learned during training,\n    such as the neural network weights. The choice of these can have a\n    significant impact on the performance of the model.\n    Therefore, it is important to tune these to maximize the discriminative\n    power of the model, in the case of decoding tasks (classification,\n    regression, etc.), such as sleep staging, BCI, pathology detection, etc.\n    We recommend the Deep Learning Tuning Playbook by Google to learn more\n    about hyperparameters and parameters tuning [1]_.\n\n\nIn this tutorial, we will use the standard decoding approach to show the impact\nof the learning rate and dropout probability on the model's performance.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load the data. In this tutorial, we use the functionality of\nbraindecode to load datasets via\n[MOABB](https://github.com/NeuroTechX/moabb)_ [2]_ to load the BCI\nCompetition IV 2a data [3]_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.moabb import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, preprocessing includes signal rescaling, the bandpass filtering\n(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using\nthe exponential moving mean and variance.\nYou can either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/stable/generated/mne.Epochs.html)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.preprocess import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),\n    # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),\n    # Bandpass filter\n    Preprocessor(exponential_moving_standardize,\n                 # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Preprocess the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Windows\n\nExtraction of the trials (windows) from the time series is based on the\nevents inside the dataset. One event is the demarcation of the stimulus or\nthe beginning of the trial. In this example, we want to analyse 0.5 [s] long\nbefore the corresponding event and the duration of the event itself.\n#Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the\n``trial_stop_offset_seconds`` to 0 [s].\n\nWe extract from the dataset the sampling frequency, which is the same for\nall datasets in this case, and we tested it.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are\n   defined in seconds and need to be converted into samples (multiplication\n   with the sampling frequency), relative to the event.\n   This variable is dataset dependent.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.windowers import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``0train`` for training and ``1test`` for evaluation.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\neval_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the ShallowFBCSPNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_ [4]_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from functools import partial\nimport torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\n# check if GPU is available, if True chooses to use it\ncuda = torch.cuda.is_available()\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220  # random seed to make results reproducible\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\n# To analyze the impact of the different parameters inside the torch model, we\n# need to create partial initialisations. This is because the\n# GridSearchCV of scikit-learn will try to initialize the model with the\n# parameters we want to tune. If we do not do this, the GridSearchCV will\n# try to initialize the model with the parameters we want to tune but\n# without the parameters we do not want to tune. This will result in an\n# error.\nmodel = partial(ShallowFBCSPNet, n_chans, n_classes,\n                input_window_samples=input_window_samples,\n                final_conv_length='auto', )\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we train the network! EEGClassifier is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom [skorch.NeuralNetClassifier](https://skorch.readthedocs.io/\nen/latest/classifier.html)_,\nso the training logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.dataset import ValidSplit\nfrom braindecode import EEGClassifier\n\nbatch_size = 16\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    optimizer__lr=[],  # This will be handled by GridSearchCV\n    batch_size=batch_size,\n    train_split=ValidSplit(0.2, random_state=seed),\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We use scikit-learn [GridSearchCV](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.GridSearchCV.html)_ to tune hyperparameters.\nTo be able to do this, we slice the braindecode datasets that by default\nreturn a 3-tuple to return X and y, respectively.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>The KFold object splits the datasets based on their\n   length which corresponds to the number of compute windows. In\n   this (trialwise) example this is fine to do. In a cropped setting\n   this is not advisable since this might split compute windows\n   of a single trial into both train and valid set.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV, KFold\nfrom skorch.helper import SliceDataset\nfrom numpy import array\nimport pandas as pd\n\ntrain_X = SliceDataset(train_set, idx=0)\ntrain_y = array([y for y in SliceDataset(train_set, idx=1)])\ncv = KFold(n_splits=2, shuffle=True, random_state=42)\n\nlearning_rates = [0.00625, 0.0000625]\ndrop_probs = [0.2, 0.5, 0.8]\n\nfit_params = {'epochs': n_epochs}\nparam_grid = {\n    'optimizer__lr': learning_rates,\n    'module__drop_prob': drop_probs\n}\n\n# By setting n_jobs=-1, grid search is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=cv,\n    return_train_score=True,\n    scoring='accuracy',\n    refit=True,\n    verbose=1,\n    error_score='raise',\n    n_jobs=1,\n)\n\nsearch.fit(train_X, train_y, **fit_params)\n\n# Extract the results into a DataFrame\nsearch_results = pd.DataFrame(search.cv_results_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting the results\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# Create a pivot table for the heatmap\npivot_table = search_results.pivot(index='param_optimizer__lr',\n                                   columns='param_module__drop_prob',\n                                   values='mean_test_score')\n# Create the heatmap\nfig, ax = plt.subplots()\nsns.heatmap(pivot_table, annot=True, fmt=\".3f\",\n            cmap=\"YlGnBu\", cbar=True)\nplt.title('Grid Search Mean Test Scores')\nplt.ylabel('Learning Rate')\nplt.xlabel('Dropout Probability')\nplt.tight_layout()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get the best hyperparameters\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "best_run = search_results[search_results['rank_test_score'] == 1].squeeze()\nprint(\n    f\"Best hyperparameters were {best_run['params']} which gave a validation \"\n    f\"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training \"\n    f\"accuracy of {best_run['mean_train_score'] * 100:.2f}%).\")\n\neval_X = SliceDataset(eval_set, idx=0)\neval_y = SliceDataset(eval_set, idx=1)\nscore = search.score(eval_X, eval_y)\nprint(f\"Eval accuracy is {score * 100:.2f}%.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,\n      Zachary Nado (2022). Deep Learning Tuning Playbook.\n      Github https://github.com/google-research/tuning_playbook\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [4] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/0.8/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb b/0.8/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb
new file mode 100644
index 00000000..045dd361
--- /dev/null
+++ b/0.8/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb
@@ -0,0 +1,140 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MOABB Dataset Example\n\nIn this example, we show how to fetch and prepare a MOABB dataset for usage\nwith Braindecode.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#          Hubert Banville <hubert.jbanville@gmail.com>\n#          Simon Brandt <simonbrandt@protonmail.com>\n#          Daniel Wilson <dan.c.wil@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom braindecode.datasets import MOABBDataset\nfrom braindecode.preprocessing import preprocess, Preprocessor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we create a dataset based on BCIC IV 2a fetched with MOABB,\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The dataset has a pandas DataFrame with additional description of its internal datasets\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset.description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can iterate through dataset which yields one time point of a continuous signal x,\nand a target y (which can be None if targets are not defined for the entire\ncontinuous signal).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for x, y in dataset:\n    print(x.shape, y)\n    break"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can apply preprocessing transforms that are defined in mne and work\nin-place, such as resampling, bandpass filtering, or electrode selection.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=True),\n    Preprocessor('resample', sfreq=100)\n]\nprint(dataset.datasets[0].raw.info[\"sfreq\"])\npreprocess(dataset, preprocessors)\nprint(dataset.datasets[0].raw.info[\"sfreq\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset based on a criteria applied to the description\nDataFrame:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subsets = dataset.split(\"session\")\nprint({subset_name: len(subset) for subset_name, subset in subsets.items()})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "See our [Trialwise Decoding](../model_building/plot_bcic_iv_2a_moabb_trial.html)_ and\n[Cropped Decoding](../model_building/plot_bcic_iv_2a_moabb_cropped.html)_ examples for\ntraining with this dataset.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/e98035eece5207cc7fa43203bc5a6fe5/plot_bcic_iv_4_ecog_trial.ipynb b/0.8/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb
similarity index 86%
rename from stable/_downloads/e98035eece5207cc7fa43203bc5a6fe5/plot_bcic_iv_4_ecog_trial.ipynb
rename to 0.8/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb
index 3337ac68..d5da2e31 100644
--- a/stable/_downloads/e98035eece5207cc7fa43203bc5a6fe5/plot_bcic_iv_4_ecog_trial.ipynb
+++ b/0.8/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -83,7 +72,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
+        "from braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
       ]
     },
     {
@@ -230,7 +219,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_out_chans = train_set[0][1].shape[0]\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = 1000  # 1 second long windows\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_out_chans,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)\n\n# We are removing the softmax layer to make it a regression model\nnew_model = torch.nn.Sequential()\nfor name, module_ in model.named_children():\n    if \"softmax\" in name:\n        continue\n    new_model.add_module(name, module_)\nmodel = new_model\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+        "from braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_out_chans = train_set[0][1].shape[0]\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = 1000  # 1 second long windows\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_out_chans,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n    add_log_softmax=False,\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
       ]
     },
     {
@@ -255,7 +244,7 @@
       },
       "outputs": [],
       "source": [
-        "from skorch.callbacks import LRScheduler, EpochScoring\nfrom skorch.helper import predefined_split\nfrom mne import set_log_level\n\nfrom braindecode import EEGRegressor\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 8\n\n\n# Function to compute Pearson correlation coefficient\ndef pearson_r_score(net, dataset, y):\n    preds = net.predict(dataset)\n    corr_coeffs = []\n    for i in range(y.shape[1]):\n        corr_coeffs.append(np.corrcoef(y[:, i], preds[:, i])[0, 1])\n    return np.mean(corr_coeffs)\n\n\nregressor = EEGRegressor(\n    model,\n    criterion=torch.nn.MSELoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'r2',\n        ('valid_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=False,\n                                         name='valid_pearson_r')),\n        ('train_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=True,\n                                         name='train_pearson_r')),\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
+        "from skorch.callbacks import EpochScoring, LRScheduler\nfrom skorch.helper import predefined_split\nfrom mne import set_log_level\n\nfrom braindecode import EEGRegressor\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\n\n# Function to compute Pearson correlation coefficient\ndef pearson_r_score(net, dataset, y):\n    preds = net.predict(dataset)\n    corr_coeffs = []\n    for i in range(y.shape[1]):\n        corr_coeffs.append(np.corrcoef(y[:, i], preds[:, i])[0, 1])\n    return np.mean(corr_coeffs)\n\n\nregressor = EEGRegressor(\n    model,\n    criterion=torch.nn.MSELoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'r2',\n        ('valid_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=False,\n                                         name='valid_pearson_r')),\n        ('train_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=True,\n                                         name='train_pearson_r')),\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
       ]
     },
     {
@@ -305,7 +294,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We plot target and predicted finger flexion on training, validation, adn test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time neded to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
+        "We plot target and predicted finger flexion on training, validation, and test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time needed to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
       ]
     },
     {
@@ -316,7 +305,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport pandas as pd\n\nplt.style.use('seaborn')\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(0, y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(0, preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(0, y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(0, preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(0, y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(0, preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(0, y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(0, preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(0, y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(0, preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(0, y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(0, preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
       ]
     },
     {
@@ -372,7 +361,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb b/0.8/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb
new file mode 100644
index 00000000..870b102c
--- /dev/null
+++ b/0.8/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb
@@ -0,0 +1,243 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Training a Braindecode model in PyTorch\n\nThis tutorial shows you how to train a Braindecode model with PyTorch. The data\npreparation and model instantiation steps are identical to that of the tutorial\n[How to train, test and tune your model](./plot_how_train_test_and_tune.html)_\n\nWe will use the BCIC IV 2a dataset as a showcase example.\n\nThe methods shown can be applied to any standard supervised trial-based decoding setting.\nThis tutorial will include additional parts of code like loading and preprocessing,\ndefining a model, and other details which are not exclusive to this page (compare\n[Cropped Decoding Tutorial](./plot_bcic_iv_2a_moabb_trial.html)_). Therefore we\nwill not further elaborate on these parts and you can feel free to skip them.\n\nThe goal of this tutorial is to present braindecode in the PyTorch perceptive.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Why should I care about model evaluation?\nShort answer: To produce reliable results!\n\nIn machine learning, we usually follow the scheme of splitting the\ndata into two parts, training and testing sets. It sounds like a\nsimple division, right? But the story does not end here.\n\nWhile developing a ML model you usually have to adjust and tune\nhyperparameters of your model or pipeline (e.g., number of layers,\nlearning rate, number of epochs). Deep learning models usually have\nmany free parameters; they could be considered complex models with\nmany degrees of freedom. If you kept using the test dataset to\nevaluate your adjustmentyou would run into data leakage.\n\nThis means that if you use the test set to adjust the hyperparameters\nof your model, the model implicitly learns or memorizes the test set.\nTherefore, the trained model is no longer independent of the test set\n(even though it was never used for training explicitly!).\nIf you perform any hyperparameter tuning, you need a third split,\nthe so-called validation set.\n\nThis tutorial shows the three basic schemes for training and evaluating\nthe model as well as two methods to tune your hyperparameters.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>You might recognize that the accuracy gets better throughout\n   the experiments of this tutorial. The reason behind that is that\n   we always use the same model with the same parameters in every\n   segment to keep the tutorial short and readable. If you do your\n   own experiments you always have to reinitialize the model before\n   training.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading, preprocessing, defining a model, etc.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading the Dataset Structure\nHere, we have a data structure with equal behavior to the Pytorch Dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing, the offline transformation of the raw dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize,\n    preprocess,\n    Preprocessor,\n)\n\nlow_cut_hz = 4.0  # low cut frequency for filtering\nhigh_cut_hz = 38.0  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\ntransforms = [\n    Preprocessor(\"pick_types\", eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(\n        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV\n        factor=1e6,\n    ),\n    Preprocessor(\"filter\", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(\n        exponential_moving_standardize,  # Exponential moving standardization\n        factor_new=factor_new,\n        init_block_size=init_block_size,\n    ),\n]\n\n# Transform the data\npreprocess(dataset, transforms, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info[\"sfreq\"]\nassert all([ds.raw.info[\"sfreq\"] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Pytorch model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = \"cuda\" if cuda else \"cpu\"\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = windows_dataset[0][0].shape[0]\ninput_window_samples = windows_dataset[0][0].shape[1]\n\n# The ShallowFBCSPNet is a `nn.Sequential` model\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=\"auto\",\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to train and evaluate your model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and test\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case the ``session`` column. We\nselect ``Train`` for training and ``test`` for testing.\nFor other datasets, you might have to choose another column.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>No matter which of the three schemes you use, this initial\n   two-fold split into train_set and test_set always remains the same.\n   Remember that you are not allowed to use the test_set during any\n   stage of training or tuning.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split(\"session\")\ntrain_set = splitted['0train']  # Session train\ntest_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Pure PyTorch training loop\n\n<img src=\"https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png\" alt=\"Pytorch logo\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "`model` is an instance of `torch.nn.Module`, and can as such be trained\nusing PyTorch optimization capabilities.\nThe following training scheme is simple as the dataset is only\nsplit into two distinct sets (``train_set`` and ``test_set``).\nThis scheme uses no separate validation split and should only be\nused for the final evaluation of the (previously!) found\nhyperparameters configuration.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>If you make any use of the ``test_set`` during training\n   (e.g. by using EarlyStopping) there will be data leakage\n   which will make the reported generalization capability/decoding\n   performance of your model less credible.</p></div>\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>The parameter values showcased here for optimizing the network are\n   chosen to make this tutorial fast to run and build. Real-world values\n   would be higher, especially when it comes to n_epochs.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from torch.nn import Module\nfrom torch.optim.lr_scheduler import LRScheduler\nfrom torch.utils.data import DataLoader\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following method runs one training epoch over the dataloader for the\ngiven model. It needs a loss function, optimization algorithm, and\nlearning rate updating callback.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm import tqdm\n# Define a method for training one epoch\n\n\ndef train_one_epoch(\n        dataloader: DataLoader, model: Module, loss_fn, optimizer,\n        scheduler: LRScheduler, epoch: int, device, print_batch_stats=True\n):\n    model.train()  # Set the model to training mode\n    train_loss, correct = 0, 0\n\n    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader),\n                        disable=not print_batch_stats)\n\n    for batch_idx, (X, y, _) in progress_bar:\n        X, y = X.to(device), y.to(device)\n        optimizer.zero_grad()\n        pred = model(X)\n        loss = loss_fn(pred, y)\n        loss.backward()\n        optimizer.step()  # update the model weights\n        optimizer.zero_grad()\n\n        train_loss += loss.item()\n        correct += (pred.argmax(1) == y).sum().item()\n\n        if print_batch_stats:\n            progress_bar.set_description(\n                f\"Epoch {epoch}/{n_epochs}, \"\n                f\"Batch {batch_idx + 1}/{len(dataloader)}, \"\n                f\"Loss: {loss.item():.6f}\"\n            )\n\n    # Update the learning rate\n    scheduler.step()\n\n    correct /= len(dataloader.dataset)\n    return train_loss / len(dataloader), correct"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Very similarly, the evaluation function loops over the entire dataloader\nand accumulate the metrics, but doesn't update the model weights.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "@torch.no_grad()\ndef test_model(\n    dataloader: DataLoader, model: Module, loss_fn, print_batch_stats=True\n):\n    size = len(dataloader.dataset)\n    n_batches = len(dataloader)\n    model.eval()  # Switch to evaluation mode\n    test_loss, correct = 0, 0\n\n    if print_batch_stats:\n        progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))\n    else:\n        progress_bar = enumerate(dataloader)\n\n    for batch_idx, (X, y, _) in progress_bar:\n        X, y = X.to(device), y.to(device)\n        pred = model(X)\n        batch_loss = loss_fn(pred, y).item()\n\n        test_loss += batch_loss\n        correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n\n        if print_batch_stats:\n            progress_bar.set_description(\n                f\"Batch {batch_idx + 1}/{len(dataloader)}, \"\n                f\"Loss: {batch_loss:.6f}\"\n            )\n\n    test_loss /= n_batches\n    correct /= size\n\n    print(\n        f\"Test Accuracy: {100 * correct:.1f}%, Test Loss: {test_loss:.6f}\\n\"\n    )\n    return test_loss, correct\n\n\n# Define the optimization\noptimizer = torch.optim.AdamW(model.parameters(),\n                              lr=lr, weight_decay=weight_decay)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                       T_max=n_epochs - 1)\n# Define the loss function\n# We used the NNLoss function, which expects log probabilities as input\n# (which is the case for our model output)\nloss_fn = torch.nn.NLLLoss()\n\n# train_set and test_set are instances of torch Datasets, and can seamlessly be\n# wrapped in data loaders.\ntrain_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\ntest_loader = DataLoader(test_set, batch_size=batch_size)\n\nfor epoch in range(1, n_epochs + 1):\n    print(f\"Epoch {epoch}/{n_epochs}: \", end=\"\")\n\n    train_loss, train_accuracy = train_one_epoch(\n        train_loader, model, loss_fn, optimizer, scheduler, epoch, device,\n    )\n\n    test_loss, test_accuracy = test_model(test_loader, model, loss_fn)\n\n    print(\n        f\"Train Accuracy: {100 * train_accuracy:.2f}%, \"\n        f\"Average Train Loss: {train_loss:.6f}, \"\n        f\"Test Accuracy: {100 * test_accuracy:.1f}%, \"\n        f\"Average Test Loss: {test_loss:.6f}\\n\"\n    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: Train it with PyTorch Lightning\n<img src=\"https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png\" alt=\"Pytorch Lightning logo\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, lightning provides a nice interface around torch modules\nwhich integrates the previous logic.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import lightning as L\nfrom torchmetrics.functional import accuracy\n\n\nclass LitModule(L.LightningModule):\n    def __init__(self, module):\n        super().__init__()\n        self.module = module\n        self.loss = torch.nn.NLLLoss()\n\n    def training_step(self, batch, batch_idx):\n        x, y, _ = batch\n        y_hat = self.module(x)\n        loss = self.loss(y_hat, y)\n        self.log(\"train_loss\", loss)\n        return loss\n\n    def test_step(self, batch, batch_idx):\n        x, y, _ = batch\n        y_hat = self.module(x)\n        loss = self.loss(y_hat, y)\n        acc = accuracy(y_hat, y, \"multiclass\", num_classes=4)\n        metrics = {\"test_acc\": acc, \"test_loss\": loss}\n        self.log_dict(metrics)\n        return metrics\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.AdamW(model.parameters(), lr=lr,\n                                      weight_decay=weight_decay)\n        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                               T_max=n_epochs - 1)\n        return [optimizer], [scheduler]\n\n\n# Creating the trainer with max_epochs=2 for demonstration purposes\ntrainer = L.Trainer(max_epochs=n_epochs)\n# Create and train the LightningModule\nlit_model = LitModule(model)\ntrainer.fit(lit_model, train_loader)\n\n# After training, you can test the model using the test DataLoader\ntrainer.test(dataloaders=test_loader)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/3e0455c88adcaa2571230abb981020db/plot_relative_positioning.ipynb b/0.8/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb
similarity index 78%
rename from stable/_downloads/3e0455c88adcaa2571230abb981020db/plot_relative_positioning.ipynb
rename to 0.8/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb
index 32341720..ba243dde 100644
--- a/stable/_downloads/3e0455c88adcaa2571230abb981020db/plot_relative_positioning.ipynb
+++ b/0.8/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Self-supervised learning on EEG with relative positioning\n\nThis example shows how to train a neural network with self-supervision on sleep\nEEG data. We follow the relative positioning approach of [1]_ on the openly\naccessible Sleep Physionet dataset [2]_ [3]_.\n\n.. topic:: Self-supervised learning\n\n    Self-supervised learning (SSL) is a learning paradigm that leverages\n    unlabelled data to train neural networks. First, neural networks are\n    trained on a \"pretext task\" which uses unlabelled data only. The pretext\n    task is designed based on a prior understanding of the data under study\n    (e.g., EEG has an underlying autocorrelation struture) and such that the\n    processing required to perform well on this pretext task is related to the\n    processing required to perform well on another task of interest.\n    Once trained, these neural networks can be reused as feature extractors or\n    weight initialization in a \"downstream task\", which is the task that we are\n    actually interested in (e.g., sleep staging). The pretext task step can\n    help reduce the quantity of labelled data needed to perform well on the\n    downstream task and/or improve downstream performance as compared to a\n    strictly supervised approach [1]_.\n\nHere, we use relative positioning (RP) as our pretext task, and perform sleep\nstaging as our downstream task. RP is a simple SSL task, in which a neural\nnetwork is trained to predict whether two randomly sampled EEG windows are\nclose or far apart in time. This method was shown to yield physiologically- and\nclinically-relevant features and to boost classification performance in\nlow-labels data regimes [1]_.\n   :depth: 2\n"
+        "\n# Self-supervised learning on EEG with relative positioning\n\nThis example shows how to train a neural network with self-supervision on sleep\nEEG data. We follow the relative positioning approach of [1]_ on the openly\naccessible Sleep Physionet dataset [2]_ [3]_.\n\n.. topic:: Self-supervised learning\n\n    Self-supervised learning (SSL) is a learning paradigm that leverages\n    unlabelled data to train neural networks. First, neural networks are\n    trained on a \"pretext task\" which uses unlabelled data only. The pretext\n    task is designed based on a prior understanding of the data under study\n    (e.g., EEG has an underlying autocorrelation structure) and such that the\n    processing required to perform well on this pretext task is related to the\n    processing required to perform well on another task of interest.\n    Once trained, these neural networks can be reused as feature extractors or\n    weight initialization in a \"downstream task\", which is the task that we are\n    actually interested in (e.g., sleep staging). The pretext task step can\n    help reduce the quantity of labelled data needed to perform well on the\n    downstream task and/or improve downstream performance as compared to a\n    strictly supervised approach [1]_.\n\nHere, we use relative positioning (RP) as our pretext task, and perform sleep\nstaging as our downstream task. RP is a simple SSL task, in which a neural\nnetwork is trained to predict whether two randomly sampled EEG windows are\nclose or far apart in time. This method was shown to yield physiologically- and\nclinically-relevant features and to boost classification performance in\nlow-labels data regimes [1]_.\n   :depth: 2\n"
       ]
     },
     {
@@ -116,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom braindecode.datasets import BaseConcatDataset\n\nsubjects = np.unique(windows_dataset.description['subject'])\nsubj_train, subj_test = train_test_split(\n    subjects, test_size=0.4, random_state=random_state)\nsubj_valid, subj_test = train_test_split(\n    subj_test, test_size=0.5, random_state=random_state)\n\n\nclass RelativePositioningDataset(BaseConcatDataset):\n    \"\"\"BaseConcatDataset with __getitem__ that expects 2 indices and a target.\n    \"\"\"\n    def __init__(self, list_of_ds):\n        super().__init__(list_of_ds)\n        self.return_pair = True\n\n    def __getitem__(self, index):\n        if self.return_pair:\n            ind1, ind2, y = index\n            return (super().__getitem__(ind1)[0],\n                    super().__getitem__(ind2)[0]), y\n        else:\n            return super().__getitem__(index)\n\n    @property\n    def return_pair(self):\n        return self._return_pair\n\n    @return_pair.setter\n    def return_pair(self, value):\n        self._return_pair = value\n\n\nsplit_ids = {'train': subj_train, 'valid': subj_valid, 'test': subj_test}\nsplitted = dict()\nfor name, values in split_ids.items():\n    splitted[name] = RelativePositioningDataset(\n        [ds for ds in windows_dataset.datasets\n         if ds.description['subject'] in values])"
+        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom braindecode.datasets import BaseConcatDataset\n\nsubjects = np.unique(windows_dataset.description['subject'])\nsubj_train, subj_test = train_test_split(\n    subjects, test_size=0.4, random_state=random_state)\nsubj_valid, subj_test = train_test_split(\n    subj_test, test_size=0.5, random_state=random_state)\n\n\nclass RelativePositioningDataset(BaseConcatDataset):\n    \"\"\"BaseConcatDataset with __getitem__ that expects 2 indices and a target.\n    \"\"\"\n\n    def __init__(self, list_of_ds):\n        super().__init__(list_of_ds)\n        self.return_pair = True\n\n    def __getitem__(self, index):\n        if self.return_pair:\n            ind1, ind2, y = index\n            return (super().__getitem__(ind1)[0],\n                    super().__getitem__(ind2)[0]), y\n        else:\n            return super().__getitem__(index)\n\n    @property\n    def return_pair(self):\n        return self._return_pair\n\n    @return_pair.setter\n    def return_pair(self, value):\n        self._return_pair = value\n\n\nsplit_ids = {'train': subj_train, 'valid': subj_valid, 'test': subj_test}\nsplitted = dict()\nfor name, values in split_ids.items():\n    splitted[name] = RelativePositioningDataset(\n        [ds for ds in windows_dataset.datasets\n         if ds.description['subject'] in values])"
       ]
     },
     {
@@ -152,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif device == 'cuda':\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=random_state, cuda=device == 'cuda')\n\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = windows_dataset[0][0].shape\nemb_size = 100\n\nemb = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_classes=emb_size,\n    n_conv_chs=16,\n    input_size_s=input_size_samples / sfreq,\n    dropout=0,\n    apply_batch_norm=True\n)\n\n\nclass ContrastiveNet(nn.Module):\n    \"\"\"Contrastive module with linear layer on top of siamese embedder.\n\n    Parameters\n    ----------\n    emb : nn.Module\n        Embedder architecture.\n    emb_size : int\n        Output size of the embedder.\n    dropout : float\n        Dropout rate applied to the linear layer of the contrastive module.\n    \"\"\"\n    def __init__(self, emb, emb_size, dropout=0.5):\n        super().__init__()\n        self.emb = emb\n        self.clf = nn.Sequential(\n            nn.Dropout(dropout),\n            nn.Linear(emb_size, 1)\n        )\n\n    def forward(self, x):\n        x1, x2 = x\n        z1, z2 = self.emb(x1), self.emb(x2)\n        return self.clf(torch.abs(z1 - z2)).flatten()\n\n\nmodel = ContrastiveNet(emb, emb_size).to(device)"
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif device == 'cuda':\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=random_state, cuda=device == 'cuda')\n\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = windows_dataset[0][0].shape\nemb_size = 100\nclasses = list(range(5))\n\nemb = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_outputs=emb_size,\n    n_conv_chs=16,\n    n_times=input_size_samples,\n    dropout=0,\n    apply_batch_norm=True,\n)\n\n\nclass ContrastiveNet(nn.Module):\n    \"\"\"Contrastive module with linear layer on top of siamese embedder.\n\n    Parameters\n    ----------\n    emb : nn.Module\n        Embedder architecture.\n    emb_size : int\n        Output size of the embedder.\n    dropout : float\n        Dropout rate applied to the linear layer of the contrastive module.\n    \"\"\"\n\n    def __init__(self, emb, emb_size, dropout=0.5):\n        super().__init__()\n        self.emb = emb\n        self.clf = nn.Sequential(\n            nn.Dropout(dropout),\n            nn.Linear(emb_size, 1)\n        )\n\n    def forward(self, x):\n        x1, x2 = x\n        z1, z2 = self.emb(x1), self.emb(x2)\n        return self.clf(torch.abs(z1 - z2)).flatten()\n\n\nmodel = ContrastiveNet(emb, emb_size).to(device)"
       ]
     },
     {
@@ -170,7 +159,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\n\nfrom skorch.helper import predefined_split\nfrom skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 5e-3\nbatch_size = 128  # 512 if data large enough\nn_epochs = 25\nnum_workers = 0 if n_jobs <= 1 else n_jobs\n\ncp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)\nearly_stopping = EarlyStopping(patience=10)\ntrain_acc = EpochScoring(\n    scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)\nvalid_acc = EpochScoring(\n    scoring='accuracy', on_train=False, name='valid_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('cp', cp),\n    ('patience', early_stopping),\n    ('train_acc', train_acc),\n    ('valid_acc', valid_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.BCEWithLogitsLoss,\n    optimizer=torch.optim.Adam,\n    max_epochs=n_epochs,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    iterator_train__num_workers=num_workers,\n    iterator_valid__num_workers=num_workers,\n    train_split=predefined_split(splitted['valid']),\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(splitted['train'], y=None)\nclf.load_params(checkpoint=cp)  # Load the model with the lowest valid_loss\n\nos.remove('./params.pt')  # Delete parameters file"
+        "import os\n\nfrom skorch.helper import predefined_split\nfrom skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 5e-3\nbatch_size = 128  # 512 if data large enough\nn_epochs = 25\nnum_workers = 0 if n_jobs <= 1 else n_jobs\n\ncp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)\nearly_stopping = EarlyStopping(patience=10)\ntrain_acc = EpochScoring(\n    scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)\n\ncallbacks = [\n    ('cp', cp),\n    ('patience', early_stopping),\n    ('train_acc', train_acc),\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.BCEWithLogitsLoss,\n    optimizer=torch.optim.Adam,\n    max_epochs=n_epochs,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    iterator_train__num_workers=num_workers,\n    iterator_valid__num_workers=num_workers,\n    train_split=predefined_split(splitted['valid']),\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(splitted['train'], y=None)\nclf.load_params(checkpoint=cp)  # Load the model with the lowest valid_loss\n\nos.remove('./params.pt')  # Delete parameters file"
       ]
     },
     {
@@ -188,7 +177,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\n\ndf['train_acc'] *= 100\ndf['valid_acc'] *= 100\n\nys1 = ['train_loss', 'valid_loss']\nys2 = ['train_acc', 'valid_acc']\nstyles = ['-', ':']\nmarkers = ['.', '.']\n\nplt.style.use('seaborn-talk')\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\nax2 = ax1.twinx()\nfor y1, y2, style, marker in zip(ys1, ys2, styles, markers):\n    ax1.plot(df['epoch'], df[y1], ls=style, marker=marker, ms=7,\n             c='tab:blue', label=y1)\n    ax2.plot(df['epoch'], df[y2], ls=style, marker=marker, ms=7,\n             c='tab:orange', label=y2)\n\nax1.tick_params(axis='y', labelcolor='tab:blue')\nax1.set_ylabel('Loss', color='tab:blue')\nax2.tick_params(axis='y', labelcolor='tab:orange')\nax2.set_ylabel('Accuracy [%]', color='tab:orange')\nax1.set_xlabel('Epoch')\n\nlines1, labels1 = ax1.get_legend_handles_labels()\nlines2, labels2 = ax2.get_legend_handles_labels()\nax2.legend(lines1 + lines2, labels1 + labels2)\n\nplt.tight_layout()"
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\n\ndf['train_acc'] *= 100\ndf['valid_acc'] *= 100\n\nys1 = ['train_loss', 'valid_loss']\nys2 = ['train_acc', 'valid_acc']\nstyles = ['-', ':']\nmarkers = ['.', '.']\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\nax2 = ax1.twinx()\nfor y1, y2, style, marker in zip(ys1, ys2, styles, markers):\n    ax1.plot(df['epoch'], df[y1], ls=style, marker=marker, ms=7,\n             c='tab:blue', label=y1)\n    ax2.plot(df['epoch'], df[y2], ls=style, marker=marker, ms=7,\n             c='tab:orange', label=y2)\n\nax1.tick_params(axis='y', labelcolor='tab:blue')\nax1.set_ylabel('Loss', color='tab:blue')\nax2.tick_params(axis='y', labelcolor='tab:orange')\nax2.set_ylabel('Accuracy [%]', color='tab:orange')\nax1.set_xlabel('Epoch')\n\nlines1, labels1 = ax1.get_legend_handles_labels()\nlines2, labels2 = ax2.get_legend_handles_labels()\nax2.legend(lines1 + lines2, labels1 + labels2)\n\nplt.tight_layout()"
       ]
     },
     {
@@ -269,7 +258,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/039c2490138a56f628199b79e22e54fd/plot_custom_dataset_example.py b/0.8/_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py
similarity index 100%
rename from stable/_downloads/039c2490138a56f628199b79e22e54fd/plot_custom_dataset_example.py
rename to 0.8/_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py
diff --git a/stable/_downloads/4855a497085ac8dc60a648bf4a72d647/plot_load_save_datasets.ipynb b/0.8/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb
similarity index 96%
rename from stable/_downloads/4855a497085ac8dc60a648bf4a72d647/plot_load_save_datasets.ipynb
rename to 0.8/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb
index 7a75dca8..6a3fb935 100644
--- a/stable/_downloads/4855a497085ac8dc60a648bf4a72d647/plot_load_save_datasets.ipynb
+++ b/0.8/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -172,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/e21937efe066fc624a4b536dced68cd1/plot_relative_positioning.py b/0.8/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py
similarity index 98%
rename from stable/_downloads/e21937efe066fc624a4b536dced68cd1/plot_relative_positioning.py
rename to 0.8/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py
index df81948f..7331f09d 100644
--- a/stable/_downloads/e21937efe066fc624a4b536dced68cd1/plot_relative_positioning.py
+++ b/0.8/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py
@@ -12,7 +12,7 @@
     unlabelled data to train neural networks. First, neural networks are
     trained on a "pretext task" which uses unlabelled data only. The pretext
     task is designed based on a prior understanding of the data under study
-    (e.g., EEG has an underlying autocorrelation struture) and such that the
+    (e.g., EEG has an underlying autocorrelation structure) and such that the
     processing required to perform well on this pretext task is related to the
     processing required to perform well on another task of interest.
     Once trained, these neural networks can be reused as feature extractors or
@@ -59,7 +59,6 @@
 dataset = SleepPhysionet(
     subject_ids=[0, 1, 2], recording_ids=[1], crop_wake_mins=30)
 
-
 ######################################################################
 # Preprocessing
 # ~~~~~~~~~~~~~
@@ -83,7 +82,6 @@
 # Transform the data
 preprocess(dataset, preprocessors)
 
-
 ######################################################################
 # Extracting windows
 # ~~~~~~~~~~~~~~~~~~
@@ -115,7 +113,6 @@
     window_size_samples=window_size_samples,
     window_stride_samples=window_size_samples, preload=True, mapping=mapping)
 
-
 ######################################################################
 # Preprocessing windows
 # ~~~~~~~~~~~~~~~~~~~~~
@@ -126,7 +123,6 @@
 
 preprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])
 
-
 ######################################################################
 # Splitting dataset into train, valid and test sets
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -150,6 +146,7 @@
 class RelativePositioningDataset(BaseConcatDataset):
     """BaseConcatDataset with __getitem__ that expects 2 indices and a target.
     """
+
     def __init__(self, list_of_ds):
         super().__init__(list_of_ds)
         self.return_pair = True
@@ -178,7 +175,6 @@ def return_pair(self, value):
         [ds for ds in windows_dataset.datasets
          if ds.description['subject'] in values])
 
-
 ######################################################################
 # Creating samplers
 # ~~~~~~~~~~~~~~~~~
@@ -218,7 +214,6 @@ def return_pair(self, value):
     n_examples=n_examples_test, same_rec_neg=True,
     random_state=random_state).presample()
 
-
 ######################################################################
 # Creating the model
 # ------------------
@@ -253,15 +248,16 @@ def return_pair(self, value):
 # Extract number of channels and time steps from dataset
 n_channels, input_size_samples = windows_dataset[0][0].shape
 emb_size = 100
+classes = list(range(5))
 
 emb = SleepStagerChambon2018(
     n_channels,
     sfreq,
-    n_classes=emb_size,
+    n_outputs=emb_size,
     n_conv_chs=16,
-    input_size_s=input_size_samples / sfreq,
+    n_times=input_size_samples,
     dropout=0,
-    apply_batch_norm=True
+    apply_batch_norm=True,
 )
 
 
@@ -277,6 +273,7 @@ class ContrastiveNet(nn.Module):
     dropout : float
         Dropout rate applied to the linear layer of the contrastive module.
     """
+
     def __init__(self, emb, emb_size, dropout=0.5):
         super().__init__()
         self.emb = emb
@@ -293,7 +290,6 @@ def forward(self, x):
 
 model = ContrastiveNet(emb, emb_size).to(device)
 
-
 ######################################################################
 # Training
 # ---------
@@ -318,14 +314,11 @@ def forward(self, x):
 early_stopping = EarlyStopping(patience=10)
 train_acc = EpochScoring(
     scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)
-valid_acc = EpochScoring(
-    scoring='accuracy', on_train=False, name='valid_acc',
-    lower_is_better=False)
+
 callbacks = [
     ('cp', cp),
     ('patience', early_stopping),
     ('train_acc', train_acc),
-    ('valid_acc', valid_acc)
 ]
 
 clf = EEGClassifier(
@@ -342,7 +335,8 @@ def forward(self, x):
     optimizer__lr=lr,
     batch_size=batch_size,
     callbacks=callbacks,
-    device=device
+    device=device,
+    classes=classes,
 )
 # Model training for a specified number of epochs. `y` is None as it is already
 # supplied in the dataset.
@@ -351,7 +345,6 @@ def forward(self, x):
 
 os.remove('./params.pt')  # Delete parameters file
 
-
 ######################################################################
 # Visualizing the results
 # -----------------------
@@ -376,8 +369,6 @@ def forward(self, x):
 styles = ['-', ':']
 markers = ['.', '.']
 
-plt.style.use('seaborn-talk')
-
 fig, ax1 = plt.subplots(figsize=(8, 3))
 ax2 = ax1.twinx()
 for y1, y2, style, marker in zip(ys1, ys2, styles, markers):
@@ -398,7 +389,6 @@ def forward(self, x):
 
 plt.tight_layout()
 
-
 ######################################################################
 # We also display the confusion matrix and classification report for the
 # pretext task:
@@ -414,7 +404,6 @@ def forward(self, x):
 print(confusion_matrix(y_true, y_pred))
 print(classification_report(y_true, y_pred))
 
-
 ######################################################################
 # Using the learned representation for sleep staging
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -487,7 +476,6 @@ def forward(self, x):
                color=colors[i], label=stage)
 ax.legend()
 
-
 ######################################################################
 # We see that there is sleep stage-related structure in the embedding. A
 # nonlinear projection method (e.g., tSNE, UMAP) might yield more insightful
diff --git a/stable/_downloads/7072ea9a517ea63ad1a0cf3ca2153f10/plot_tuh_discrete_multitarget.py b/0.8/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py
similarity index 58%
rename from stable/_downloads/7072ea9a517ea63ad1a0cf3ca2153f10/plot_tuh_discrete_multitarget.py
rename to 0.8/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py
index 2e4ca1bc..a1fa4a57 100644
--- a/stable/_downloads/7072ea9a517ea63ad1a0cf3ca2153f10/plot_tuh_discrete_multitarget.py
+++ b/0.8/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py
@@ -2,8 +2,10 @@
 Multiple discrete targets with the TUH EEG Corpus
 =================================================
 
-In this example, we showcase usage of multiple discrete targets per recording
-with the TUH EEG Corpus.
+Welcome to this tutorial where we demonstrate how to work with multiple discrete
+ targets for each recording in the TUH EEG Corpus. We'll guide you through the
+ process step by step.
+
 """
 
 # Author: Lukas Gemein <l.gemein@gmail.com>
@@ -16,7 +18,13 @@
 from braindecode.datasets import TUH
 from braindecode.preprocessing import create_fixed_length_windows
 
-mne.set_log_level('ERROR')  # avoid messages everytime a window is extracted
+# Setting Logging Level
+# ----------------------
+#
+# We'll set the logging level to 'ERROR' to avoid excessive messages when
+# extracting windows:
+
+mne.set_log_level('ERROR')  # avoid messages every time a window is extracted
 
 
 ###############################################################################
@@ -27,7 +35,10 @@
 
 
 ###############################################################################
-# We start by creating a TUH dataset. Instead of just a str, we give it
+# Creating Temple University Hospital (TUH) EEG Corpus Dataset
+# ------------------------------------------------------------
+#
+# We start by creating a TUH dataset. Instead of just a `str, we give it
 # multiple strings as target names. Each of the strings has to exist as a
 # column in the description DataFrame.
 
@@ -39,19 +50,26 @@
     preload=False,
     add_physician_reports=False,
 )
-tuh.description
-
+print(tuh.description)
 
 ###############################################################################
-# Iterating through the dataset gives x as ndarray(n_channels x 1) as well as
-# the target as [age of the subject, gender of the subject]. Let's look at the last example
-# as it has more interesting age/gender labels (compare to the last row of the dataframe above).
+# Exploring Data
+# --------------
+#
+# Iterating through the dataset gives `x` as an ndarray with shape
+# `(n_channels x 1)` and `y` as a list containing `[age of the subject, gender
+# of the subject]`.
+# Let's look at the last example as it has more interesting age/gender labels
+# (compare to the last row of the dataframe above).
 x, y = tuh[-1]
-print('x:', x)
-print('y:', y)
+
+print(f'{x=}\n{y=}')
 
 
 ###############################################################################
+# Creating Windows
+# ----------------
+#
 # We will skip preprocessing steps for now, since it is not the aim of this
 # example. Instead, we will directly create compute windows. We specify a
 # mapping from genders 'M' and 'F' to integers, since this is required for
@@ -72,15 +90,20 @@
 
 
 ###############################################################################
-# Iterating through the dataset gives x as ndarray(n_channels x 1000), y as
-# [age, gender], and ind. Let's look at the last example again.
+# Exploring Windows
+# -----------------
+#
+# Iterating through the dataset gives `x` as an ndarray with shape
+# `(n_channels x 1000)`, `y` as `[age, gender]`, and `ind`.
+# Let's look at the last example again.
 x, y, ind = tuh_windows[-1]
-print('x:', x)
-print('y:', y)
-print('ind:', ind)
+print(f'{x=}\n{y=}\n{ind=}')
 
 
 ###############################################################################
+# DataLoader for Model Training
+# -----------------------------
+#
 # We give the dataset to a pytorch DataLoader, such that it can be used for
 # model training.
 dl = DataLoader(
@@ -90,12 +113,15 @@
 
 
 ###############################################################################
-# Iterating through the DataLoader gives batch_X as tensor(4 x n_channels x
-# 1000), batch_y as [tensor([4 x age of subject]), tensor([4 x gender of
-# subject])], and batch_ind. We will iterate to the end to look at the last example
-# again.
+# Exploring DataLoader
+# --------------------
+#
+# When iterating through the DataLoader, we get `batch_X` as a tensor with shape
+# `(4 x n_channels x 1000)`, `batch_y` as `[tensor([4 x age of subject]),
+# tensor([4 x gender of subject])]`, and `batch_ind`. To view the last example,
+# simply iterate through the DataLoader:
+
 for batch_X, batch_y, batch_ind in dl:
     pass
-print('batch_X:', batch_X)
-print('batch_y:', batch_y)
-print('batch_ind:', batch_ind)
+
+print(f'{batch_X=}\n{batch_y=}\n{batch_ind=}')
diff --git a/stable/_downloads/2a53ed6bee60a29af3f760043d35ed09/benchmark_lazy_eager_loading.py b/0.8/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py
similarity index 98%
rename from stable/_downloads/2a53ed6bee60a29af3f760043d35ed09/benchmark_lazy_eager_loading.py
rename to 0.8/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py
index 22e9bd12..2aaf1b05 100644
--- a/stable/_downloads/2a53ed6bee60a29af3f760043d35ed09/benchmark_lazy_eager_loading.py
+++ b/0.8/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py
@@ -18,7 +18,7 @@
 when they are required. This means large datasets can be used for training,
 however this introduces some file-reading overhead every time an example must
 be extracted. Some preprocessing steps that require continuous data also have to
-be implemented differently to accomodate the nature of windowed data. Overall
+be implemented differently to accommodate the nature of windowed data. Overall
 though, we can reduce the impact of lazy loading by using the `num_workers`
 parameter of pytorch's `Dataloader` class, which dispatches the data loading to
 multiple processes.
@@ -49,7 +49,7 @@
 from braindecode.models import ShallowFBCSPNet, Deep4Net
 
 
-mne.set_log_level('WARNING')  # avoid messages everytime a window is extracted
+mne.set_log_level('WARNING')  # avoid messages every time a window is extracted
 
 ###############################################################################
 # We start by setting two pytorch internal parameters that can affect the
@@ -106,7 +106,7 @@ def load_example_data(preload, window_len_s, n_recordings=10):
         ds, start_offset_samples=0, stop_offset_samples=None,
         window_size_samples=window_len_samples,
         window_stride_samples=window_stride_samples, drop_last_window=True,
-        preload=preload, drop_bad_windows=True)
+        preload=preload)
 
     # Drop bad epochs
     # XXX: This could be parallelized.
diff --git a/stable/_downloads/dce0c540b9a673ff87c724934b4a0581/plot_bcic_iv_4_ecog_cropped.py b/0.8/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py
similarity index 95%
rename from stable/_downloads/dce0c540b9a673ff87c724934b4a0581/plot_bcic_iv_4_ecog_cropped.py
rename to 0.8/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py
index acbbd441..e33346f3 100644
--- a/stable/_downloads/dce0c540b9a673ff87c724934b4a0581/plot_bcic_iv_4_ecog_cropped.py
+++ b/0.8/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py
@@ -82,8 +82,9 @@
 #
 
 
-from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor)
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
 
 low_cut_hz = 1.  # low cut frequency for filtering
 high_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG
@@ -95,9 +96,10 @@
 # We select only first 30 seconds from the training dataset to limit time and memory
 # to run this example. We split training dataset into train and validation (only 6 seconds).
 # To obtain full results whole datasets should be used.
-valid_set = preprocess(copy.deepcopy(train_set), [Preprocessor('crop', tmin=24, tmax=30)])
-preprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)])
-preprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)])
+valid_set = preprocess(copy.deepcopy(train_set),
+                       [Preprocessor('crop', tmin=24, tmax=30)], n_jobs=-1)
+preprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)
+preprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)
 
 
 ######################################################################
@@ -156,8 +158,9 @@
 #
 
 import torch
-from braindecode.util import set_random_seeds
+
 from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
 
 cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
 device = 'cuda' if cuda else 'cpu'
@@ -180,31 +183,23 @@
     n_chans,
     n_classes,
     final_conv_length=2,
+    add_log_softmax=False,
 )
-# We are removing the softmax layer to make it a regression model
-new_model = torch.nn.Sequential()
-for name, module_ in model.named_children():
-    if "softmax" in name:
-        continue
-    new_model.add_module(name, module_)
-model = new_model
 
 # Send model to GPU
 if cuda:
     model.cuda()
 
-from braindecode.models import to_dense_prediction_model, get_output_shape
+from braindecode.models import get_output_shape, to_dense_prediction_model
 
 to_dense_prediction_model(model)
 
-
 ######################################################################
 # To know the models’ receptive field, we calculate the shape of model
 # output for a dummy input.
 
 n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
 
-
 ######################################################################
 # Cut Compute Windows
 # ~~~~~~~~~~~~~~~~~~~
@@ -280,9 +275,8 @@
 from skorch.callbacks import LRScheduler
 from skorch.helper import predefined_split
 
-from braindecode.training import TimeSeriesLoss
 from braindecode import EEGRegressor
-from braindecode.training import CroppedTimeSeriesEpochScoring
+from braindecode.training import CroppedTimeSeriesEpochScoring, TimeSeriesLoss
 
 # These values we found good for shallow network for EEG MI decoding:
 lr = 0.0625 * 0.01
@@ -355,19 +349,18 @@ def pad_and_select_predictions(preds, y):
 
 
 ######################################################################
-# We plot target and predicted finger flexion on training, validation, adn test sets.
+# We plot target and predicted finger flexion on training, validation, and test sets.
 #
 # .. note::
-#    The model is trained and validated on limited dataset (to decrease the time neded to run
+#    The model is trained and validated on limited dataset (to decrease the time needed to run
 #    this example) which does not contain diverse dataset in terms of fingers flexions and may
 #    cause overfitting. To obtain better results use whole dataset as well as improve the decoding
 #    pipeline which may be not optimal for ECoG.
 #
 import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
 import pandas as pd
+from matplotlib.lines import Line2D
 
-plt.style.use('seaborn')
 fig, axes = plt.subplots(3, 1, figsize=(8, 9))
 
 axes[0].set_title('Training dataset')
diff --git a/stable/_downloads/692af8474f3e890e34bcf84a5b5b929c/plot_sleep_staging_usleep.py b/0.8/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py
similarity index 82%
rename from stable/_downloads/692af8474f3e890e34bcf84a5b5b929c/plot_sleep_staging_usleep.py
rename to 0.8/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py
index 8ecadc0f..b501d78c 100644
--- a/stable/_downloads/692af8474f3e890e34bcf84a5b5b929c/plot_sleep_staging_usleep.py
+++ b/0.8/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py
@@ -13,25 +13,10 @@
 windows using the openly accessible Sleep Physionet dataset [2]_ [3]_.
 
 .. warning::
-    The example is written to have a very short excecution time.
+    The example is written to have a very short execution time.
     This number of epochs is here too small and very few recordings are used.
     To obtain competitive results you need to use more data and more epochs.
 
-References
-----------
-.. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
-       U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
-       https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py
-
-.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-       a sleep-dependent neuronal feedback loop: the slow-wave
-       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
-
-.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-       Research Resource for Complex Physiologic Signals.
-       Circulation 101(23):e215-e220
 """
 # Authors: Theo Gnassounou <theo.gnassounou@inria.fr>
 #          Omar Chehab <l-emir-omar.chehab@inria.fr>
@@ -63,7 +48,6 @@
     subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30,
     crop=crop)
 
-
 ######################################################################
 # Preprocessing
 # ~~~~~~~~~~~~~
@@ -81,7 +65,6 @@
 # Transform the data
 preprocess(dataset, preprocessors)
 
-
 ######################################################################
 # Extract windows
 # ~~~~~~~~~~~~~~~
@@ -90,7 +73,6 @@
 
 from braindecode.preprocessing import create_windows_from_events
 
-
 mapping = {  # We merge stages 3 and 4 following AASM standards.
     'Sleep stage W': 0,
     'Sleep stage 1': 1,
@@ -141,14 +123,15 @@
 n_windows = 3  # Sequences of 3 consecutive windows; originally 35 in paper
 n_windows_stride = 3  # Non-overlapping sequences
 
-train_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
 valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
 
 # Print number of examples per class
 print(len(train_sampler))
 print(len(valid_sampler))
 
-
 ######################################################################
 # Finally, since some sleep stages appear a lot more often than others (e.g.
 # most of the night is spent in the N2 stage), the classes are imbalanced. To
@@ -161,7 +144,6 @@
 y_train = [train_set[idx][1][1] for idx in train_sampler]
 class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
 
-
 ######################################################################
 # Create model
 # ------------
@@ -187,24 +169,21 @@
 set_random_seeds(seed=31, cuda=cuda)
 
 n_classes = 5
+classes = list(range(n_classes))
 # Extract number of channels and time steps from dataset
 in_chans, input_size_samples = train_set[0][0].shape
-
 model = USleep(
-    in_chans=in_chans,
+    n_chans=in_chans,
     sfreq=sfreq,
     depth=12,
     with_skip_connection=True,
-    n_classes=n_classes,
-    input_size_s=input_size_samples / sfreq,
-    apply_softmax=False
+    n_outputs=n_classes,
+    n_times=input_size_samples
 )
 
 # Send model to GPU
 if cuda:
     model.cuda()
-
-
 ######################################################################
 # Training
 # --------
@@ -268,7 +247,11 @@ def balanced_accuracy_multi(model, X, y):
     batch_size=batch_size,
     callbacks=callbacks,
     device=device,
+    classes=classes,
 )
+# Deactivate the default valid_acc callback:
+clf.set_params(callbacks__valid_acc=None)
+
 # Model training for a specified number of epochs. `y` is None as it is already
 # supplied in the dataset.
 clf.fit(train_set, y=None, epochs=n_epochs)
@@ -299,18 +282,54 @@ def balanced_accuracy_multi(model, X, y):
 
 ######################################################################
 # Finally, we also display the confusion matrix and classification report:
-
+from braindecode.visualization import plot_confusion_matrix
 from sklearn.metrics import confusion_matrix, classification_report
 
 y_true = np.array([valid_set[i][1] for i in valid_sampler])
 y_pred = clf.predict(valid_set)
 
-print(confusion_matrix(y_true.flatten(), y_pred.flatten()))
+confusion_mat = confusion_matrix(y_true.flatten(), y_pred.flatten())
+
+plot_confusion_matrix(confusion_mat=confusion_mat,
+                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])
+
 print(classification_report(y_true.flatten(), y_pred.flatten()))
 
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true.flatten(), color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
+
 ######################################################################
 # Our model was able to learn, as shown by the decreasing training and
 # validation loss values, despite the low amount of data that was available
 # (only two recordings in this example). To further improve performance, more
 # recordings should be included in the training set, the model should be
 # trained for more epochs and hyperparameters should be optimized.
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+#        U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+#        https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py
+#
+# .. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/995f58ef1b3d29dce64caee24e5bf457/plot_bcic_iv_2a_moabb_cropped.py b/0.8/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py
similarity index 73%
rename from stable/_downloads/995f58ef1b3d29dce64caee24e5bf457/plot_bcic_iv_2a_moabb_cropped.py
rename to 0.8/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py
index dc095ec2..2916d832 100644
--- a/stable/_downloads/995f58ef1b3d29dce64caee24e5bf457/plot_bcic_iv_2a_moabb_cropped.py
+++ b/0.8/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py
@@ -3,23 +3,24 @@
 Cropped Decoding on BCIC IV 2a Dataset
 ======================================
 
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
 """
 
 ######################################################################
-# Building on the `Trialwise decoding
-# tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__, we now do more
-# data-efficient cropped decoding!
+# Building on the `Trialwise decoding <./plot_bcic_iv_2a_moabb_trial.html>`__,
+# we now do more data-efficient cropped decoding!
 #
-
-######################################################################
-
 # In Braindecode, there are two supported configurations created for
 # training models: trialwise decoding and cropped decoding. We will
 # explain this visually by comparing trialwise to cropped decoding.
 #
-# .. image:: ../_static/trialwise_explanation.png
-# .. image:: ../_static/cropped_explanation.png
-#
+# .. image:: https://braindecode.org/stable/_static/trialwise_explanation.png
+#    :alt: Trialwise decoding
+# .. image:: https://braindecode.org/stable/_static/cropped_explanation.png
+#    :alt: Cropped decoding
 # On the left, you see trialwise decoding:
 #
 # 1. A complete trial is pushed through the network.
@@ -35,9 +36,18 @@
 #    called compute windows)
 # 3. Therefore, the network produces multiple predictions (one per crop in
 #    the window)
-# 4. The individual crop predictions are averaged before computing the
+# 4. The individual crop predictions are *AVERAGED* before computing the
 #    loss function
 #
+# This averaging of predictions of small sub-windows is the key difference
+# between trialwise and cropped decoding. It was introduced in [1]_ and it impact
+# on the parameters of the network.
+#
+# It is important to note that the averaging of predictions is only done
+# during training. During testing, the network is still applied to crops
+# and the predictions are averaged afterwards.
+#
+#
 # .. note::
 #
 #     -  The network architecture implicitly defines the crop size (it is the
@@ -50,26 +60,25 @@
 #     -  Crop size and window size together define how many predictions the
 #        network makes per window: ``#window − #crop + 1 = #predictions``
 #
-
-######################################################################
-
 # .. note::
 #
 #     For cropped decoding, the above training setup is mathematically
-#     identical to sampling crops in your dataset, pushing them through the
-#     network and training directly on the individual crops. At the same time,
-#     the above training setup is much faster as it avoids redundant
-#     computations by using dilated convolutions, see our paper
-#     `Deep learning with convolutional neural networks for EEG decoding and visualization <https://arxiv.org/abs/1703.05051>`_. # noqa: E501
-#     However, the two setups are only mathematically identical in case (1)
+#     similar to sampling crops in your dataset, pushing them through the
+#     network and training directly on the individual crops. However, the
+#     if their position would be randomly selected, the crops would be less
+#     correlated in contrast to the neighbourhood crops selected from a window.
+#     At the same time, the above training setup is much faster as it avoids
+#     redundant computations by using dilated convolutions, see [2]_.
+#     However, the two setups are only mathematically related in case (1)
 #     your network does not use any padding or only left padding and
 #     (2) your loss function leads
 #     to the same gradients when using the averaged output. The first is true
 #     for our shallow and deep ConvNet models and the second is true for the
 #     log-softmax outputs and negative log likelihood loss that is typically
 #     used for classification in PyTorch.
-#
 
+
+######################################################################
 # Loading and preprocessing the dataset
 # -------------------------------------
 #
@@ -79,12 +88,16 @@
 from braindecode.datasets import MOABBDataset
 
 subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
 
-from braindecode.preprocessing import (
-    exponential_moving_standardize, preprocess, Preprocessor)
 from numpy import multiply
 
+from braindecode.preprocessing import (
+    Preprocessor,
+    exponential_moving_standardize,
+    preprocess,
+)
+
 low_cut_hz = 4.  # low cut frequency for filtering
 high_cut_hz = 38.  # high cut frequency for filtering
 # Parameters for exponential moving standardization
@@ -94,51 +107,48 @@
 factor = 1e6
 
 preprocessors = [
-    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
+    # Keep EEG sensors
     Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
-    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
-    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
+    # Bandpass filter
+    Preprocessor(exponential_moving_standardize,
+                 # Exponential moving standardization
                  factor_new=factor_new, init_block_size=init_block_size)
 ]
 
 # Transform the data
-preprocess(dataset, preprocessors)
-
+preprocess(dataset, preprocessors, n_jobs=-1)
 
 ######################################################################
 # Create model and compute windowing parameters
 # ---------------------------------------------
-#
 # In contrast to trialwise decoding, we first have to create the model
 # before we can cut the dataset into windows. This is because we need to
-# know the receptive field of the network to know how large the window
+# know the neural network parameters to know how large the sub-window
 # stride should be.
 #
-
-######################################################################
-
 # We first choose the compute/input window size that will be fed to the
-# network during training This has to be larger than the networks
-# receptive field size and can otherwise be chosen for computational
+# network during training. This has to be larger than the networks
+# the number of timesteps size and can otherwise be chosen for computational
 # efficiency (see explanations in the beginning of this tutorial). Here we
 # choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.
 #
 
 input_window_samples = 1000
 
-
 ######################################################################
 # Now we create the model. To enable it to be used in cropped decoding
 # efficiently, we manually set the length of the final convolution layer
-# to some length that makes the receptive field of the ConvNet smaller
+# to some length that makes the number of timesteps of the ConvNet smaller
 # than ``input_window_samples`` (see ``final_conv_length=30`` in the model
 # definition).
 #
 
 import torch
-from braindecode.util import set_random_seeds
-from braindecode.models import ShallowFBCSPNet
 
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
 
 cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
 device = 'cuda' if cuda else 'cpu'
@@ -154,6 +164,7 @@
 set_random_seeds(seed=seed, cuda=cuda)
 
 n_classes = 4
+classes = list(range(n_classes))
 # Extract number of chans from dataset
 n_chans = dataset[0][0].shape[0]
 
@@ -164,29 +175,26 @@
     final_conv_length=30,
 )
 
+# Display torchinfo table describing the model
+print(model)
+
 # Send model to GPU
 if cuda:
-    model.cuda()
-
+    _ = model.cuda()
 
 ######################################################################
 # And now we transform model with strides to a model that outputs dense
 # prediction, so we can use it to obtain predictions for all
 # crops.
 #
-
-from braindecode.models import to_dense_prediction_model, get_output_shape
-
-to_dense_prediction_model(model)
-
+model.to_dense_prediction_model()
 
 ######################################################################
-# To know the models’ receptive field, we calculate the shape of model
-# output for a dummy input.
+# To know the models’ output shape without the last layer, we calculate the
+# shape of model output for a dummy input.
 #
 
-n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
-
+n_preds_per_input = model.get_output_shape()[2]
 
 ######################################################################
 # Cut the data into windows
@@ -218,7 +226,6 @@
     preload=True
 )
 
-
 ######################################################################
 # Split the dataset
 # -----------------
@@ -227,9 +234,8 @@
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
-
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 
 ######################################################################
 # Training
@@ -239,8 +245,6 @@
 # criterion, as well as ``criterion__loss_function`` as the loss function
 # applied to the meaned predictions.
 #
-######################################################################
-
 # .. note::
 #
 #     In this tutorial, we use some default parameters that we
@@ -264,7 +268,7 @@
 # weight_decay = 0.5 * 0.001
 
 batch_size = 64
-n_epochs = 4
+n_epochs = 2
 
 clf = EEGClassifier(
     model,
@@ -278,20 +282,19 @@
     iterator_train__shuffle=True,
     batch_size=batch_size,
     callbacks=[
-        "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
 )
 # Model training for a specified number of epochs. `y` is None as it is already supplied
 # in the dataset.
-clf.fit(train_set, y=None, epochs=n_epochs)
-
+_ = clf.fit(train_set, y=None, epochs=n_epochs)
 
 ######################################################################
 # Plot Results
 # ----------------
-# This is again the same code as in trialwise decoding.
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # .. note::
 #
 #     Note that we drop further in the classification error and
@@ -299,11 +302,12 @@
 #
 
 import matplotlib.pyplot as plt
-from matplotlib.lines import Line2D
 import pandas as pd
+from matplotlib.lines import Line2D
 
 # Extract loss and accuracy values for plotting from history object
-results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
+results_columns = ['train_loss', 'valid_loss', 'train_accuracy',
+                   'valid_accuracy']
 df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
                   index=clf.history[:, 'epoch'])
 
@@ -311,10 +315,10 @@
 df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
                valid_misclass=100 - 100 * df.valid_accuracy)
 
-plt.style.use('seaborn')
 fig, ax1 = plt.subplots(figsize=(8, 3))
 df.loc[:, ['train_loss', 'valid_loss']].plot(
-    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,
+    fontsize=14)
 
 ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
 ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
@@ -330,20 +334,22 @@
 
 # where some data has already been plotted to ax
 handles = []
-handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
-handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
 plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
 plt.tight_layout()
 
-
 ######################################################################
 # Plot Confusion Matrix
 # ---------------------
 #
-# Generate a confusion matrix as in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730
+# Generate a confusion matrix as in [2]_
 #
 
 from sklearn.metrics import confusion_matrix
+
 from braindecode.visualization import plot_confusion_matrix
 
 # generate confusion matrices
@@ -356,9 +362,25 @@
 
 # add class labels
 # label_dict is class_name : str -> i_class : int
-label_dict = valid_set.datasets[0].windows.event_id.items()
+label_dict = valid_set.datasets[0].window_kwargs[0][1]['mapping']
 # sort the labels by values (values are integer class labels)
-labels = list(dict(sorted(list(label_dict), key=lambda kv: kv[1])).keys())
+labels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]
 
 # plot the basic conf. matrix
 plot_confusion_matrix(confusion_mat, class_names=labels)
+
+##########################################################################
+#
+#
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/fc5680253dfb92d25d5d415bcdc10089/plot_sleep_staging_chambon2018.py b/0.8/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py
similarity index 83%
rename from stable/_downloads/fc5680253dfb92d25d5d415bcdc10089/plot_sleep_staging_chambon2018.py
rename to 0.8/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py
index 0b5fec35..c7ff530b 100644
--- a/stable/_downloads/fc5680253dfb92d25d5d415bcdc10089/plot_sleep_staging_chambon2018.py
+++ b/0.8/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py
@@ -7,23 +7,6 @@
 sequences of EEG windows using the openly accessible Sleep Physionet dataset
 [2]_ [3]_.
 
-References
-----------
-.. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
-      (2018)A Deep Learning Architecture for Temporal Sleep Stage
-      Classification Using Multivariate and Multimodal Time Series.
-      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
-      (758-769)
-
-.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-       a sleep-dependent neuronal feedback loop: the slow-wave
-       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
-
-.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-       Research Resource for Complex Physiologic Signals.
-       Circulation 101(23):e215-e220
 """
 # Authors: Hubert Banville <hubert.jbanville@gmail.com>
 #
@@ -53,7 +36,6 @@
 dataset = SleepPhysionet(
     subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30)
 
-
 ######################################################################
 # Preprocessing
 # ~~~~~~~~~~~~~
@@ -62,19 +44,20 @@
 # a lowpass filter. We omit the downsampling step of [1]_ as the Sleep
 # Physionet data is already sampled at a lower 100 Hz.
 
-from braindecode.preprocessing import preprocess, Preprocessor, scale
+from braindecode.preprocessing import preprocess, Preprocessor
+from numpy import multiply
 
 high_cut_hz = 30
+factor = 1e6
 
 preprocessors = [
-    Preprocessor(scale, factor=1e6, apply_on_array=True),
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
 ]
 
 # Transform the data
 preprocess(dataset, preprocessors)
 
-
 ######################################################################
 # Extract windows
 # ~~~~~~~~~~~~~~~
@@ -83,7 +66,6 @@
 
 from braindecode.preprocessing import create_windows_from_events
 
-
 mapping = {  # We merge stages 3 and 4 following AASM standards.
     'Sleep stage W': 0,
     'Sleep stage 1': 1,
@@ -107,7 +89,6 @@
     mapping=mapping
 )
 
-
 ######################################################################
 # Window preprocessing
 # ~~~~~~~~~~~~~~~~~~~~
@@ -119,7 +100,6 @@
 
 preprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])
 
-
 ######################################################################
 # Split dataset into train and valid
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -152,13 +132,16 @@
 n_windows = 3  # Sequences of 3 consecutive windows
 n_windows_stride = 3  # Maximally overlapping sequences
 
-train_sampler = SequenceSampler(train_set.get_metadata(), n_windows, n_windows_stride)
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
 valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
 
 # Print number of examples per class
 print('Training examples: ', len(train_sampler))
 print('Validation examples: ', len(valid_sampler))
 
+
 ######################################################################
 # We also implement a transform to extract the label of the center window of a
 # sequence to use it as target.
@@ -185,7 +168,6 @@ def get_center_label(x):
 y_train = [train_set[idx][1] for idx in train_sampler]
 class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
 
-
 ######################################################################
 # Create model
 # ------------
@@ -221,8 +203,8 @@ def get_center_label(x):
 feat_extractor = SleepStagerChambon2018(
     n_channels,
     sfreq,
-    n_classes=n_classes,
-    input_size_s=input_size_samples / sfreq,
+    n_outputs=n_classes,
+    n_times=input_size_samples,
     return_feats=True
 )
 
@@ -239,7 +221,6 @@ def get_center_label(x):
 if cuda:
     model.cuda()
 
-
 ######################################################################
 # Training
 # --------
@@ -289,13 +270,13 @@ def get_center_label(x):
     optimizer__lr=lr,
     batch_size=batch_size,
     callbacks=callbacks,
-    device=device
+    device=device,
+    classes=np.unique(y_train),
 )
 # Model training for a specified number of epochs. `y` is None as it is already
 # supplied in the dataset.
 clf.fit(train_set, y=None, epochs=n_epochs)
 
-
 ######################################################################
 # Plot results
 # ------------
@@ -325,13 +306,31 @@ def get_center_label(x):
 #
 
 from sklearn.metrics import confusion_matrix, classification_report
+from braindecode.visualization import plot_confusion_matrix
 
 y_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]
 y_pred = clf.predict(valid_set)
 
-print(confusion_matrix(y_true, y_pred))
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+plot_confusion_matrix(confusion_mat=confusion_mat,
+                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])
+
 print(classification_report(y_true, y_pred))
 
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true, color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
 
 ######################################################################
 # Our model was able to learn despite the low amount of data that was available
@@ -344,3 +343,23 @@ def get_center_label(x):
 #    training set, and hyperparameters should be selected accordingly.
 #    Increasing the sequence length was also shown in [1]_ to help improve
 #    performance, especially when few EEG channels are available.
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
+#       Classification Using Multivariate and Multimodal Time Series.
+#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+#       (758-769)
+#
+# .. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/35f097f0d5a2df80f3fc3d21411435e1/plot_data_augmentation.py b/0.8/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py
similarity index 97%
rename from stable/_downloads/35f097f0d5a2df80f3fc3d21411435e1/plot_data_augmentation.py
rename to 0.8/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py
index d2704f45..36da3656 100644
--- a/stable/_downloads/35f097f0d5a2df80f3fc3d21411435e1/plot_data_augmentation.py
+++ b/0.8/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py
@@ -58,7 +58,7 @@
                  factor_new=factor_new, init_block_size=init_block_size)
 ]
 
-preprocess(dataset, preprocessors)
+preprocess(dataset, preprocessors, n_jobs=-1)
 
 ######################################################################
 # Extracting windows
@@ -89,9 +89,8 @@
 #
 
 splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-valid_set = splitted['session_E']
-
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
 ######################################################################
 # Defining a Transform
 # --------------------
@@ -120,9 +119,9 @@
 # data of an mne Epoch is used here to make usage of mne functions.
 
 import torch
+import numpy as np
 
-epochs = train_set.datasets[0].windows  # original epochs
-X = epochs.get_data()
+X = np.stack([X for X, y, i in train_set.datasets[0]])
 # This allows to apply the transform with a fixed shift (10 Hz) for
 # visualization instead of sampling the shift randomly between -2 and 2 Hz
 X_tr, _ = transform.operation(torch.as_tensor(X).float(), None, 10., sfreq)
@@ -133,7 +132,6 @@
 
 import mne
 import matplotlib.pyplot as plt
-import numpy as np
 
 
 def plot_psd(data, axis, label, color):
@@ -184,6 +182,7 @@ def plot_psd(data, axis, label, color):
 set_random_seeds(seed=seed, cuda=cuda)
 
 n_classes = 4
+classes = list(range(n_classes))
 
 # Extract number of chans and time steps from dataset
 n_channels = train_set[0][0].shape[0]
@@ -249,6 +248,7 @@ def plot_psd(data, axis, label, color):
         ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
     ],
     device=device,
+    classes=classes,
 )
 # Model training for a specified number of epochs. `y` is None as it is already
 # supplied in the dataset.
diff --git a/0.8/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py b/0.8/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py
new file mode 100644
index 00000000..9f53c631
--- /dev/null
+++ b/0.8/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py
@@ -0,0 +1,177 @@
+"""
+Simple training on MNE epochs
+=============================
+
+The braindecode library gives you access to a large number of neural network
+architectures that were developed for EEG data decoding. This tutorial will
+show you how you can easily use any of these models to decode your own data.
+In particular, we assume that have your data in an MNE format and want to
+train one of the Braindecode models on it.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>
+#
+# License: BSD (3-clause)
+
+######################################################################
+# Finding the model you want
+# --------------------------
+#
+# Exploring the braindecode online documentation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Let's suppose you recently stumbled upon the Schirrmeister 2017 article [1]_.
+# In this article, the authors mention that their novel architecture ShallowConvNet
+# is performing well on the BCI Competition IV 2a dataset and you would like to use
+# it on your own data. Fortunately, the authors also mentioned they published their
+# architecture on Braindecode!
+#
+# In order to use this architecture, you first need to find what is its exact
+# name in Braindecode. To do so, you can visit the Braindecode online documentation
+# which lists all the available models.
+#
+# Models list: https://braindecode.org/stable/api.html#models
+#
+# Alternatively, the API also provide a dictionary with all available models:
+
+from braindecode.models.util import models_dict
+
+print(f'All the Braindecode models:\n{list(models_dict.keys())}')
+
+######################################################################
+# After your investigation, you found out that the model you are looking for is
+# ``ShallowFBCSPNet``. You can now import it from Braindecode:
+
+from braindecode.models import ShallowFBCSPNet
+
+######################################################################
+# Examining the model
+# ~~~~~~~~~~~~~~~~~~~
+#
+# Now that you found your model, you must check which parameters it expects.
+# You can find this information either in the online documentation here:
+# :class:`braindecode.models.ShallowFBCSPNet` or directly in the module's docstring:
+
+print(ShallowFBCSPNet.__doc__)
+
+######################################################################
+# Additionally, you might be interested in visualizing the model's architecture.
+# This can be done by initializing the model and calling its ``__str__()`` method.
+# To initialize it, we need to specify some parameters that we set at random
+# values for now:
+
+model = ShallowFBCSPNet(
+    n_chans=32,
+    n_times=1000,
+    n_outputs=2,
+    final_conv_length='auto',
+)
+print(model)
+
+######################################################################
+# Loading your own data with MNE
+# ------------------------------
+#
+# In this tutorial, we demonstrate how to train the model on MNE data.
+# MNE is quite a popular library for EEG data analysis as it provides methods
+# to load data from many different file formats and a large collection of algorithms
+# to preprocess it.
+# However, Braindecode is not limited to MNE and can be used with numpy arrays or
+# PyTorch tensors/datasets.
+#
+# For this example, we generate some random data containing 100 examples with each
+# 3 channels and 1024 time points. We also generate some random labels for our data
+# that simulate a 4-class classification problem.
+
+import mne
+import numpy as np
+
+info = mne.create_info(ch_names=['C3', 'C4', 'Cz'], sfreq=256., ch_types='eeg')
+X = np.random.randn(100, 3, 1024)  # 100 epochs, 3 channels, 4 seconds (@256Hz)
+epochs = mne.EpochsArray(X, info=info)
+y = np.random.randint(0, 4, size=100)  # 4 classes
+print(epochs)
+
+######################################################################
+# Training your model (scikit-learn compatible)
+# ---------------------------------------------
+#
+# Now that you know which model you want to use, you know how to instantiate it,
+# and that we have some fake data, it is time to train the model!
+#
+# .. note::
+#    `Skorch <https://skorch.readthedocs.io>`_  is a library that allows you to wrap
+#    any PyTorch module into a scikit-learn-compatible classifier or regressor.
+#    Braindecode provides wrappers that inherit form the original Skorch ones and simply
+#    implement a few additional features that facilitate the use of Braindecode models.
+#
+# To train a Braindecode model, the easiest way is by using braindecode's
+# Skorch wrappers. These wrappers are :class:`braindecode.EEGClassifier` and
+# :class:`braindecode.EEGRegressor`. As our fake data is a classification task,
+# we will use the former.
+#
+# The wrapper :class:`braindecode.EEGClassifier` expects a model class as its first argument but
+# to facilitate the usage, you can also simply pass the name of any braindecode model as a string.
+# The wrapper automatically finds and instantiates the model for you.
+# If you want to pass parameters to your model, you can give them to the wrapper
+# with the prefix ``module__``.
+#
+from skorch.dataset import ValidSplit
+from braindecode import EEGClassifier
+
+net = EEGClassifier(
+    'ShallowFBCSPNet',
+    module__final_conv_length='auto',
+    train_split=ValidSplit(0.2),
+    # To train a neural network you need validation split, here, we use 20%.
+)
+
+######################################################################
+# In this example, we passed one additional parameter to the wrapper: ``module__final_conv_length``
+# that will be forwarded to the model (without the prefix ``module__``).
+#
+# We also note that the parameters ``n_chans``, ``n_times`` and ``n_outputs`` were not specified
+# even if :class:`braindecode.ShallowFBCSPNet` needs them to be initialized. This is because the
+# wrapper will automatically infer them, along with some other signal-related parameters,
+# from the input data at training time.
+#
+# Now that we have our model wrapped in a scikit-learn-compatible classifier,
+# we can train it by simply calling the ``fit`` method:
+
+net.fit(epochs, y)
+
+######################################################################
+# The pre-trained model is accessible via the ``module_`` attribute:
+
+print(net.module_)
+
+######################################################################
+# And we can see that all the following parameters were automatically inferred
+# from the training data:
+
+print(f'{net.module_.n_chans=}\n{net.module_.n_times=}\n{net.module_.n_outputs=}'
+      f'\n{net.module_.input_window_seconds=}\n{net.module_.sfreq=}\n{net.module_.chs_info=}')
+
+######################################################################
+# Depending on the type of data used for training, some parameters might not be
+# possible to infer. For example if you pass a numpy array or a
+# :class:`braindecode.dataset.WindowsDataset` with ``target_from="metadata"``,
+#  then only ``n_chans``, ``n_times`` and ``n_outputs`` will be inferred.
+# And if you pass other types of datasets, only ``n_chans`` and ``n_times`` will be inferred.
+# In these case, you will have to pass the missing parameters manually
+# (with the prefix ``module__``).
+
+######################################################################
+# References
+# ----------
+#
+# .. [1] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter,
+#        M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T.(2017).
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Human Brain Mapping, Aug. 2017.
+#        Online: http://dx.doi.org/10.1002/hbm.23730
diff --git a/stable/_downloads/e0deebe7e74ab8d42ee491e92f30e023/plot_tuh_eeg_corpus.py b/0.8/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py
similarity index 54%
rename from stable/_downloads/e0deebe7e74ab8d42ee491e92f30e023/plot_tuh_eeg_corpus.py
rename to 0.8/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py
index ea2ed013..ad6daf06 100644
--- a/stable/_downloads/e0deebe7e74ab8d42ee491e92f30e023/plot_tuh_eeg_corpus.py
+++ b/0.8/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py
@@ -5,6 +5,12 @@
 In this example, we showcase usage of the Temple University Hospital EEG Corpus
 (https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)
 including simple preprocessing steps as well as cutting of compute windows.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+
 """
 
 # Author: Lukas Gemein <l.gemein@gmail.com>
@@ -19,72 +25,119 @@
 
 from braindecode.datasets import TUH
 from braindecode.preprocessing import (
-    preprocess, Preprocessor, create_fixed_length_windows, scale as multiply)
-
-
-plt.style.use('seaborn')
-mne.set_log_level('ERROR')  # avoid messages everytime a window is extracted
+    preprocess, Preprocessor, create_fixed_length_windows)
+from numpy import multiply
 
+mne.set_log_level('ERROR')  # avoid messages every time a window is extracted
 
 ###############################################################################
-# If you want to try this code with the actual data, please delete the next
-# section. We are required to mock some dataset functionality, since the data
-# is not available at creation time of this example.
-from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811
+# Creating the dataset using TUHMock
+# -------------------------------------
+#
+# Since the data is not available at the time of the creation of this example,
+# we are required to mock some of the dataset functionality. Therefore, if you
+# want to try this code with the actual data, please disconsider this section.
 
+from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811
 
 ###############################################################################
-# We start by creating a TUH dataset. First, the class generates a description
-# of the recordings in `TUH_PATH` (which is later accessible as
-# `tuh.description`) without actually touching the files. This will parse
-# information from file paths such as patient id, recording data, etc and should
-# be really fast. Afterwards, the files are sorted chronologically by year,
-# month, day, patient id, recording session and segment.
-# In the following, a subset of the description corresponding to `recording_ids`
-# is used.
-# Afterwards, the files will be iterated a second time, slower than before.
-# The files are now actually touched. Additional information about subjects
-# like age and gender are parsed directly from the EDF file header. If existent,
-# the physician report is added to the description. Furthermore, the recordings
-# are read with `mne.io.read_raw_edf` with `preload=False`. Finally, we will get
-# a `BaseConcatDataset` of `BaseDatasets` each holding a single
-# `nme.io.Raw` which is fully compatible with other braindecode functionalities.
+# Firstly, we start by creating a TUH mock dataset using braindecode's _TUHMock class.
+# The complete code can be found at :func:`braindecode.datasets.TUH`, but we will give
+# a small description of how it works.
+# This class is able to read the recordings from TUH_PATH and generate a description
+# by parsing information from file paths, such as patient id and recording data.
+# THis description can later be accessed by the object's .description method.
+# After that, the files are sorted chronologically by year, month, day,
+# patient id, recording session and segment, and then use the description corresponding
+# to the specified by recording ids.
+# FInally, additional information regarding age and gender of the subjects are parsed
+# directly to the description.
 
 TUH_PATH = 'please insert actual path to data here'
-N_JOBS = 2  # specify the number of jobs for loading and windowing
+# specify the number of jobs for loading and windowing
+N_JOBS = 2
 tuh = TUH(
     path=TUH_PATH,
     recording_ids=None,
     target_name=None,
     preload=False,
     add_physician_reports=False,
-    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS,  # Mock dataset can't
-    # be loaded in parallel
+    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS
 )
 
 
 ###############################################################################
-# We can easily create descriptive statistics using the description `DataFrame`,
-# for example an age histogram split by gender of patients.
-
-fig, ax = plt.subplots(1, 1, figsize=(15, 5))
-genders = tuh.description.gender.unique()
-x = [tuh.description.age[tuh.description.gender == g] for g in genders]
-ax.hist(
-    x=x,
-    stacked=True,
-    bins=np.arange(100, dtype=int),
-    alpha=.5,
-)
-ax.legend(genders)
-ax.set_xlabel('Age [years]')
-ax.set_ylabel('Count')
+# We can visualize our data's statistics using the class' "description" method
+
+def plt_histogram(df_of_ages_genders, alpha=0.5, fs=24, ylim=1.5, show_title=True):
+    # Dafarame containing info about gender and age of subjects
+    df = df_of_ages_genders
+    male_df = df[df["gender"] == 'M']
+    female_df = df[df["gender"] == 'F']
+
+    plt.figure(figsize=(15, 18))
+    if show_title:
+        plt.suptitle("Age information", y=0.95, fontsize=fs + 5)
+
+    # First plot: Male individuals
+    plt.subplot(121)
+    plt.hist(male_df["age"], bins=np.linspace(0, 100, 101),
+             alpha=alpha, color="green", orientation="horizontal")
+    plt.axhline(np.mean(male_df["age"]), color="black",
+                label=f"mean age {np.mean(male_df['age']):.1f} "
+                      f"(±{np.std(male_df['age']):.1f})")
+    plt.barh(np.mean(male_df["age"]), height=2 * np.std(male_df["age"]),
+             width=ylim, color="black", alpha=0.25)
+
+    # Legend
+    plt.xlim(0, ylim)
+    plt.legend(fontsize=fs, loc="upper left")
+    plt.title(f"male ({100 * len(male_df) / len(df):.1f}%)",
+              fontsize=fs, loc="left", y=1, x=0.05)
+    plt.yticks(color='w')
+    plt.gca().invert_xaxis()
+    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)
+    plt.tick_params(labelsize=fs - 5)
+
+    # First plot: Female individuals
+    plt.subplot(122)
+    plt.hist(female_df["age"], bins=np.linspace(0, 100, 101),
+             alpha=alpha, color="orange", orientation="horizontal")
+    plt.axhline(np.mean(female_df["age"]), color="black", linestyle="--",
+                label=f"mean age {np.mean(female_df['age']):.1f} ("
+                      f"±{np.std(female_df['age']):.1f})")
+    plt.barh(np.mean(female_df["age"]), height=2 * np.std(female_df["age"]),
+             width=ylim, color="black", alpha=0.25)
+
+    # Label
+    plt.legend(fontsize=fs, loc="upper right")
+    plt.xlim(0, ylim)
+    plt.title(f"female ({100 * len(female_df) / len(df):.1f}%)",
+              fontsize=fs, loc="right", y=1, x=0.95)
+    plt.ylim(0, 100)
+    plt.subplots_adjust(wspace=0, hspace=0)
+    plt.ylabel("age [years]", fontsize=fs)
+    plt.xlabel("count", fontsize=fs, x=1, labelpad=20)
+    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)
+    plt.tick_params(labelsize=fs - 5)
+
+    plt.show()
+
+
+df = tuh.description
+plt_histogram(df)
 
 
 ###############################################################################
-# Next, we will perform some preprocessing steps. First, we will do some
-# selection of available recordings based on the duration. We will select those
-# recordings, that have at least five minutes duration. Data is not loaded here.
+# Preprocessing
+# -------------------------------------
+#
+# Selecting recordings
+# ~~~~~~~~~~~~~
+#
+# First, we will do some selection of available recordings based on the duration.
+# We will select those recordings that have at least five minutes duration.
+#
 
 def select_by_duration(ds, tmin=0, tmax=None):
     if tmax is None:
@@ -93,6 +146,7 @@ def select_by_duration(ds, tmin=0, tmax=None):
     split_ids = []
     for d_i, d in enumerate(ds.datasets):
         duration = d.raw.n_times / d.raw.info['sfreq']
+        # select the ones in the required duration range
         if tmin <= duration <= tmax:
             split_ids.append(d_i)
     splits = ds.split(split_ids)
@@ -104,18 +158,18 @@ def select_by_duration(ds, tmin=0, tmax=None):
 tmax = None
 tuh = select_by_duration(tuh, tmin, tmax)
 
-
 ###############################################################################
 # Next, we will discard all recordings that have an incomplete channel
-# configuration (wrt the channels that we are interested in, i.e. the 21
-# channels of the international 10-20-placement). The dataset is subdivided into
-# recordings with 'le' and 'ar' reference which we will have to consider. Data
-# is not loaded here.
+# configuration on the channels that we are interested. The subdivisions of the
+# recordings into 'le' and 'ar' labels represents the channels for
+# the re-referencing of the signals.
 
 short_ch_names = sorted([
     'A1', 'A2',
     'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',
     'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])
+
+# TUH data is subdivided into 'le' and 'ar' recordings references
 ar_ch_names = sorted([
     'EEG A1-REF', 'EEG A2-REF',
     'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',
@@ -154,17 +208,19 @@ def select_by_channels(ds, ch_mapping):
 
 
 ###############################################################################
-# Next, we will chain several preprocessing steps that are realized through
-# `mne`. Data will be loaded by the first preprocessor that has a mention of it
-# in brackets:
+# Combining preprocessing steps
+# ~~~~~~~~~~~~~
+#
+# Next, we use braindecode's preprocess to combine and execute several preprocessing
+# steps that are executed through 'mne':
 #
-# #. crop the recordings to a region of interest
-# #. re-reference all recordings to 'ar' (requires load)
-# #. rename channels to short channel names
-# #. pick channels of interest
-# #. scale signals to micro volts (requires load)
-# #. clip outlier values to +/- 800 micro volts (requires load)
-# #. resample recordings to a common frequency (requires load)
+# - Crop the recordings to a region of interest
+# - Re-reference all recordings to 'ar' (requires load)
+# - Rename channels to short channel names
+# - Pick channels of interest
+# - Scale signals to micro volts (requires load)
+# - Clip outlier values to +/- 800 micro volts (requires load)
+# - Resample recordings to a common frequency (requires load)
 
 def custom_rename_channels(raw, mapping):
     # rename channels which are dependent on referencing:
@@ -188,6 +244,7 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
 tmin = 1 * 60
 tmax = 6 * 60
 sfreq = 100
+factor = 1e6
 
 preprocessors = [
     Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,
@@ -196,18 +253,18 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
     Preprocessor(custom_rename_channels, mapping=ch_mapping,
                  apply_on_array=False),
     Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
-    Preprocessor(multiply, factor=1e6, apply_on_array=True),
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),
     Preprocessor('resample', sfreq=sfreq),
 ]
 
-
 ###############################################################################
-# Next, we apply the preprocessors on the selected recordings in parallel.
+# Next, we can apply the defined preprocessors on the selected recordings in parallel.
 # We additionally use the serialization functionality of
 # :func:`braindecode.preprocessing.preprocess` to limit memory usage during
-# preprocessing (as each file must be loaded into memory for some of the
-# preprocessing steps to work). This also makes it possible to use the lazy
+# preprocessing, as each file must be loaded into memory for some of the
+# preprocessing steps to work.
+# This also makes it possible to use the lazy
 # loading capabilities of :class:`braindecode.datasets.BaseConcatDataset`, as
 # the preprocessed data is automatically reloaded with ``preload=False``.
 #
@@ -225,12 +282,14 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
 )
 
 ###############################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~
 # We can finally generate compute windows. The resulting dataset is now ready
 # to be used for model training.
 
 window_size_samples = 1000
 window_stride_samples = 1000
-# generate compute windows here and store them to disk
+# Generate compute windows here and store them to disk
 tuh_windows = create_fixed_length_windows(
     tuh_preproc,
     window_size_samples=window_size_samples,
@@ -238,6 +297,3 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
     drop_last_window=False,
     n_jobs=N_JOBS,
 )
-
-for x, y, ind in tuh_windows:
-    break
diff --git a/stable/_downloads/b38e40c9ee338673865922575e84fd7f/plot_split_dataset.ipynb b/0.8/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb
similarity index 61%
rename from stable/_downloads/b38e40c9ee338673865922575e84fd7f/plot_split_dataset.ipynb
rename to 0.8/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb
index a57fc8a2..909cad32 100644
--- a/stable/_downloads/b38e40c9ee338673865922575e84fd7f/plot_split_dataset.ipynb
+++ b/0.8/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb
@@ -1,21 +1,10 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Split Dataset Example\n\nIn this example, we show multiple ways of how to split datasets.\n"
+        "\n# Split Dataset Example\n\nIn this example, we aim to show multiple ways of how you can split your datasets for\ntraining, testing, and evaluating your models.\n   :depth: 2\n"
       ]
     },
     {
@@ -33,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "First, we create a dataset based on BCIC IV 2a fetched with MOABB,\n\n"
+        "## Loading the dataset\n\nFirstly, we create a dataset using the braindecode class <MOABBDataset> to load\nit fetched from MOABB. In this example, we're using Dataset 2a from BCI\nCompetition IV.\n\n"
       ]
     },
     {
@@ -51,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "ds has a pandas DataFrame with additional description of its internal datasets\n\n"
+        "## Splitting\n\n### By description information\n\nThe class <MOABBDataset> has a pandas DataFrame containing additional\ndescription of its internal datasets, which can be used to help splitting the data\nbased on recording information, such as subject, session, and run of each trial.\n\n"
       ]
     },
     {
@@ -69,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We can split the dataset based on the info in the description, for example\nbased on different runs. The returned dictionary will have string keys\ncorresponding to unique entries in the description DataFrame column\n\n"
+        "Here, we're splitting the data based on different runs. The method split returns\na dictionary with string keys corresponding to unique entries in the description\nDataFrame column.\n\n"
       ]
     },
     {
@@ -80,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "splits = dataset.split(\"run\")\nprint(splits)\nsplits[\"run_4\"].description"
+        "splits = dataset.split(\"run\")\nprint(splits)\nsplits[\"4\"].description"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We can also split the dataset based on a list of integers corresponding to\nrows in the description. In this case, the returned dictionary will have\n'0' as the only key\n\n"
+        "### By row index\n\nAnother way we can split the dataset is based on a list of integers corresponding to\nrows in the description. In this case, the returned dictionary will have\n'0' as the only key.\n\n"
       ]
     },
     {
@@ -105,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If we want multiple splits based on indices, we can also specify a list of\nlist of integers. In this case, the dictionary will have string keys\nrepresenting the id of the dataset split in the order of the given list of\nintegers\n\n"
+        "However, if we want multiple splits based on indices, we can also define a list\ncontaining lists of integers. In this case, the dictionary will have string keys\nrepresenting the index of the dataset split in the order of the given list of\nintegers.\n\n"
       ]
     },
     {
@@ -123,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If we want to split based on a list of indices but you want to specify\nthe keys in the output dictionary you can pass a dict as:\n\n"
+        "You can also name each split in the output dictionary by specifying the keys\nof each list of indexes in the input dictionary:\n\n"
       ]
     },
     {
@@ -141,29 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Similarly, we can split datasets after creating windows\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "windows = create_windows_from_events(\n    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)\nsplits = windows.split(\"run\")\nsplits"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "splits = windows.split([4, 8])\nsplits"
+        "## Observation\n\nSimilarly, we can split datasets after creating windows using the same methods.\n\n"
       ]
     },
     {
@@ -174,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "splits = windows.split([[4, 8], [5, 9, 11]])\nsplits"
+        "windows = create_windows_from_events(\n    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)"
       ]
     },
     {
@@ -185,7 +152,7 @@
       },
       "outputs": [],
       "source": [
-        "splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))\nsplits"
+        "# Splitting by different runs\nprint(\"Using description info\")\nsplits = windows.split(\"run\")\nprint(splits)\nprint()\n\n# Splitting by row index\nprint(\"Splitting by row index\")\nsplits = windows.split([4, 8])\nprint(splits)\nprint()\n\nprint(\"Multiple row index split\")\nsplits = windows.split([[4, 8], [5, 9, 11]])\nprint(splits)\nprint()\n\n# Specifying output's keys\nprint(\"Specifying keys\")\nsplits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))\nprint(splits)"
       ]
     }
   ],
@@ -205,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/stable/_downloads/49b1f19a86467aeaf762a1550583a845/plot_custom_dataset_example.ipynb b/0.8/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb
similarity index 94%
rename from stable/_downloads/49b1f19a86467aeaf762a1550583a845/plot_custom_dataset_example.ipynb
rename to 0.8/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb
index fc20a9bc..9531ac62 100644
--- a/stable/_downloads/49b1f19a86467aeaf762a1550583a845/plot_custom_dataset_example.ipynb
+++ b/0.8/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb
@@ -1,16 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -136,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.12"
+      "version": "3.9.5"
     }
   },
   "nbformat": 4,
diff --git a/0.8/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py b/0.8/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py
new file mode 100644
index 00000000..3d148717
--- /dev/null
+++ b/0.8/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py
@@ -0,0 +1,54 @@
+"""MOABB Dataset Example
+========================
+
+In this example, we show how to fetch and prepare a MOABB dataset for usage
+with Braindecode.
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#          Hubert Banville <hubert.jbanville@gmail.com>
+#          Simon Brandt <simonbrandt@protonmail.com>
+#          Daniel Wilson <dan.c.wil@gmail.com>
+#
+# License: BSD (3-clause)
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import preprocess, Preprocessor
+
+###############################################################################
+# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+###############################################################################
+# The dataset has a pandas DataFrame with additional description of its internal datasets
+dataset.description
+
+##############################################################################
+# We can iterate through dataset which yields one time point of a continuous signal x,
+# and a target y (which can be None if targets are not defined for the entire
+# continuous signal).
+for x, y in dataset:
+    print(x.shape, y)
+    break
+
+##############################################################################
+# We can apply preprocessing transforms that are defined in mne and work
+# in-place, such as resampling, bandpass filtering, or electrode selection.
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=True),
+    Preprocessor('resample', sfreq=100)
+]
+print(dataset.datasets[0].raw.info["sfreq"])
+preprocess(dataset, preprocessors)
+print(dataset.datasets[0].raw.info["sfreq"])
+
+###############################################################################
+# We can easily split the dataset based on a criteria applied to the description
+# DataFrame:
+subsets = dataset.split("session")
+print({subset_name: len(subset) for subset_name, subset in subsets.items()})
+
+##############################################################################
+# See our `Trialwise Decoding <../model_building/plot_bcic_iv_2a_moabb_trial.html>`__ and
+# `Cropped Decoding <../model_building/plot_bcic_iv_2a_moabb_cropped.html>`__ examples for
+# training with this dataset.
diff --git a/0.8/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb b/0.8/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb
new file mode 100644
index 00000000..bdaf46e4
--- /dev/null
+++ b/0.8/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb
@@ -0,0 +1,219 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Simple training on MNE epochs\n\nThe braindecode library gives you access to a large number of neural network\narchitectures that were developed for EEG data decoding. This tutorial will\nshow you how you can easily use any of these models to decode your own data.\nIn particular, we assume that have your data in an MNE format and want to\ntrain one of the Braindecode models on it.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Finding the model you want\n\n### Exploring the braindecode online documentation\n\nLet's suppose you recently stumbled upon the Schirrmeister 2017 article [1]_.\nIn this article, the authors mention that their novel architecture ShallowConvNet\nis performing well on the BCI Competition IV 2a dataset and you would like to use\nit on your own data. Fortunately, the authors also mentioned they published their\narchitecture on Braindecode!\n\nIn order to use this architecture, you first need to find what is its exact\nname in Braindecode. To do so, you can visit the Braindecode online documentation\nwhich lists all the available models.\n\nModels list: https://braindecode.org/stable/api.html#models\n\nAlternatively, the API also provide a dictionary with all available models:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models.util import models_dict\n\nprint(f'All the Braindecode models:\\n{list(models_dict.keys())}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After your investigation, you found out that the model you are looking for is\n``ShallowFBCSPNet``. You can now import it from Braindecode:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models import ShallowFBCSPNet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Examining the model\n\nNow that you found your model, you must check which parameters it expects.\nYou can find this information either in the online documentation here:\n:class:`braindecode.models.ShallowFBCSPNet` or directly in the module's docstring:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(ShallowFBCSPNet.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Additionally, you might be interested in visualizing the model's architecture.\nThis can be done by initializing the model and calling its ``__str__()`` method.\nTo initialize it, we need to specify some parameters that we set at random\nvalues for now:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model = ShallowFBCSPNet(\n    n_chans=32,\n    n_times=1000,\n    n_outputs=2,\n    final_conv_length='auto',\n)\nprint(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading your own data with MNE\n\nIn this tutorial, we demonstrate how to train the model on MNE data.\nMNE is quite a popular library for EEG data analysis as it provides methods\nto load data from many different file formats and a large collection of algorithms\nto preprocess it.\nHowever, Braindecode is not limited to MNE and can be used with numpy arrays or\nPyTorch tensors/datasets.\n\nFor this example, we generate some random data containing 100 examples with each\n3 channels and 1024 time points. We also generate some random labels for our data\nthat simulate a 4-class classification problem.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import mne\nimport numpy as np\n\ninfo = mne.create_info(ch_names=['C3', 'C4', 'Cz'], sfreq=256., ch_types='eeg')\nX = np.random.randn(100, 3, 1024)  # 100 epochs, 3 channels, 4 seconds (@256Hz)\nepochs = mne.EpochsArray(X, info=info)\ny = np.random.randint(0, 4, size=100)  # 4 classes\nprint(epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training your model (scikit-learn compatible)\n\nNow that you know which model you want to use, you know how to instantiate it,\nand that we have some fake data, it is time to train the model!\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>[Skorch](https://skorch.readthedocs.io)  is a library that allows you to wrap\n   any PyTorch module into a scikit-learn-compatible classifier or regressor.\n   Braindecode provides wrappers that inherit form the original Skorch ones and simply\n   implement a few additional features that facilitate the use of Braindecode models.</p></div>\n\nTo train a Braindecode model, the easiest way is by using braindecode's\nSkorch wrappers. These wrappers are :class:`braindecode.EEGClassifier` and\n:class:`braindecode.EEGRegressor`. As our fake data is a classification task,\nwe will use the former.\n\nThe wrapper :class:`braindecode.EEGClassifier` expects a model class as its first argument but\nto facilitate the usage, you can also simply pass the name of any braindecode model as a string.\nThe wrapper automatically finds and instantiates the model for you.\nIf you want to pass parameters to your model, you can give them to the wrapper\nwith the prefix ``module__``.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.dataset import ValidSplit\nfrom braindecode import EEGClassifier\n\nnet = EEGClassifier(\n    'ShallowFBCSPNet',\n    module__final_conv_length='auto',\n    train_split=ValidSplit(0.2),\n    # To train a neural network you need validation split, here, we use 20%.\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we passed one additional parameter to the wrapper: ``module__final_conv_length``\nthat will be forwarded to the model (without the prefix ``module__``).\n\nWe also note that the parameters ``n_chans``, ``n_times`` and ``n_outputs`` were not specified\neven if :class:`braindecode.ShallowFBCSPNet` needs them to be initialized. This is because the\nwrapper will automatically infer them, along with some other signal-related parameters,\nfrom the input data at training time.\n\nNow that we have our model wrapped in a scikit-learn-compatible classifier,\nwe can train it by simply calling the ``fit`` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "net.fit(epochs, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The pre-trained model is accessible via the ``module_`` attribute:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(net.module_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And we can see that all the following parameters were automatically inferred\nfrom the training data:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f'{net.module_.n_chans=}\\n{net.module_.n_times=}\\n{net.module_.n_outputs=}'\n      f'\\n{net.module_.input_window_seconds=}\\n{net.module_.sfreq=}\\n{net.module_.chs_info=}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Depending on the type of data used for training, some parameters might not be\npossible to infer. For example if you pass a numpy array or a\n:class:`braindecode.dataset.WindowsDataset` with ``target_from=\"metadata\"``,\n then only ``n_chans``, ``n_times`` and ``n_outputs`` will be inferred.\nAnd if you pass other types of datasets, only ``n_chans`` and ``n_times`` will be inferred.\nIn these case, you will have to pass the missing parameters manually\n(with the prefix ``module__``).\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter,\n       M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T.(2017).\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Human Brain Mapping, Aug. 2017.\n       Online: http://dx.doi.org/10.1002/hbm.23730\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
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new file mode 100644
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diff --git a/0.8/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/0.8/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
index 00000000..eb19f698
--- /dev/null
+++ b/0.8/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
@@ -0,0 +1 @@
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diff --git a/0.8/_sphinx_design_static/design-tabs.js b/0.8/_sphinx_design_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/0.8/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/0.8/_static/basic.css b/0.8/_static/basic.css
new file mode 100644
index 00000000..e760386b
--- /dev/null
+++ b/0.8/_static/basic.css
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 270px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
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+    background-color: transparent;
+    font-weight: bold;
+}
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+	font-size: 1.1em;
+}
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+    font-size: 1.2em;
+}
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+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
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+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
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+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
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+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/0.8/_static/binder_badge_logo.svg b/0.8/_static/binder_badge_logo.svg
new file mode 100644
index 00000000..327f6b63
--- /dev/null
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diff --git a/0.8/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/0.8/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
index 00000000..eb19f698
--- /dev/null
+++ b/0.8/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
@@ -0,0 +1 @@
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diff --git a/0.8/_static/design-tabs.js b/0.8/_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/0.8/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/0.8/_static/doctools.js b/0.8/_static/doctools.js
new file mode 100644
index 00000000..d06a71d7
--- /dev/null
+++ b/0.8/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/0.8/_static/documentation_options.js b/0.8/_static/documentation_options.js
new file mode 100644
index 00000000..d40ea2b7
--- /dev/null
+++ b/0.8/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.8',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: false,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
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diff --git a/0.8/_static/language_data.js b/0.8/_static/language_data.js
new file mode 100644
index 00000000..250f5665
--- /dev/null
+++ b/0.8/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/0.8/_static/minus.png b/0.8/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
GIT binary patch
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diff --git a/0.8/_static/no_image.png b/0.8/_static/no_image.png
new file mode 100644
index 0000000000000000000000000000000000000000..8c2d48d5d3f0044cf0399b3608d8eb5db6f9d12d
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diff --git a/0.8/_static/plus.png b/0.8/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
GIT binary patch
literal 90
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literal 0
HcmV?d00001

diff --git a/0.8/_static/pygments.css b/0.8/_static/pygments.css
new file mode 100644
index 00000000..997797f2
--- /dev/null
+++ b/0.8/_static/pygments.css
@@ -0,0 +1,152 @@
+html[data-theme="light"] .highlight pre { line-height: 125%; }
+html[data-theme="light"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="light"] .highlight .hll { background-color: #7971292e }
+html[data-theme="light"] .highlight { background: #fefefe; color: #545454 }
+html[data-theme="light"] .highlight .c { color: #797129 } /* Comment */
+html[data-theme="light"] .highlight .err { color: #d91e18 } /* Error */
+html[data-theme="light"] .highlight .k { color: #7928a1 } /* Keyword */
+html[data-theme="light"] .highlight .l { color: #797129 } /* Literal */
+html[data-theme="light"] .highlight .n { color: #545454 } /* Name */
+html[data-theme="light"] .highlight .o { color: #008000 } /* Operator */
+html[data-theme="light"] .highlight .p { color: #545454 } /* Punctuation */
+html[data-theme="light"] .highlight .ch { color: #797129 } /* Comment.Hashbang */
+html[data-theme="light"] .highlight .cm { color: #797129 } /* Comment.Multiline */
+html[data-theme="light"] .highlight .cp { color: #797129 } /* Comment.Preproc */
+html[data-theme="light"] .highlight .cpf { color: #797129 } /* Comment.PreprocFile */
+html[data-theme="light"] .highlight .c1 { color: #797129 } /* Comment.Single */
+html[data-theme="light"] .highlight .cs { color: #797129 } /* Comment.Special */
+html[data-theme="light"] .highlight .gd { color: #007faa } /* Generic.Deleted */
+html[data-theme="light"] .highlight .ge { font-style: italic } /* Generic.Emph */
+html[data-theme="light"] .highlight .gh { color: #007faa } /* Generic.Heading */
+html[data-theme="light"] .highlight .gs { font-weight: bold } /* Generic.Strong */
+html[data-theme="light"] .highlight .gu { color: #007faa } /* Generic.Subheading */
+html[data-theme="light"] .highlight .kc { color: #7928a1 } /* Keyword.Constant */
+html[data-theme="light"] .highlight .kd { color: #7928a1 } /* Keyword.Declaration */
+html[data-theme="light"] .highlight .kn { color: #7928a1 } /* Keyword.Namespace */
+html[data-theme="light"] .highlight .kp { color: #7928a1 } /* Keyword.Pseudo */
+html[data-theme="light"] .highlight .kr { color: #7928a1 } /* Keyword.Reserved */
+html[data-theme="light"] .highlight .kt { color: #797129 } /* Keyword.Type */
+html[data-theme="light"] .highlight .ld { color: #797129 } /* Literal.Date */
+html[data-theme="light"] .highlight .m { color: #797129 } /* Literal.Number */
+html[data-theme="light"] .highlight .s { color: #008000 } /* Literal.String */
+html[data-theme="light"] .highlight .na { color: #797129 } /* Name.Attribute */
+html[data-theme="light"] .highlight .nb { color: #797129 } /* Name.Builtin */
+html[data-theme="light"] .highlight .nc { color: #007faa } /* Name.Class */
+html[data-theme="light"] .highlight .no { color: #007faa } /* Name.Constant */
+html[data-theme="light"] .highlight .nd { color: #797129 } /* Name.Decorator */
+html[data-theme="light"] .highlight .ni { color: #008000 } /* Name.Entity */
+html[data-theme="light"] .highlight .ne { color: #7928a1 } /* Name.Exception */
+html[data-theme="light"] .highlight .nf { color: #007faa } /* Name.Function */
+html[data-theme="light"] .highlight .nl { color: #797129 } /* Name.Label */
+html[data-theme="light"] .highlight .nn { color: #545454 } /* Name.Namespace */
+html[data-theme="light"] .highlight .nx { color: #545454 } /* Name.Other */
+html[data-theme="light"] .highlight .py { color: #007faa } /* Name.Property */
+html[data-theme="light"] .highlight .nt { color: #007faa } /* Name.Tag */
+html[data-theme="light"] .highlight .nv { color: #d91e18 } /* Name.Variable */
+html[data-theme="light"] .highlight .ow { color: #7928a1 } /* Operator.Word */
+html[data-theme="light"] .highlight .pm { color: #545454 } /* Punctuation.Marker */
+html[data-theme="light"] .highlight .w { color: #545454 } /* Text.Whitespace */
+html[data-theme="light"] .highlight .mb { color: #797129 } /* Literal.Number.Bin */
+html[data-theme="light"] .highlight .mf { color: #797129 } /* Literal.Number.Float */
+html[data-theme="light"] .highlight .mh { color: #797129 } /* Literal.Number.Hex */
+html[data-theme="light"] .highlight .mi { color: #797129 } /* Literal.Number.Integer */
+html[data-theme="light"] .highlight .mo { color: #797129 } /* Literal.Number.Oct */
+html[data-theme="light"] .highlight .sa { color: #008000 } /* Literal.String.Affix */
+html[data-theme="light"] .highlight .sb { color: #008000 } /* Literal.String.Backtick */
+html[data-theme="light"] .highlight .sc { color: #008000 } /* Literal.String.Char */
+html[data-theme="light"] .highlight .dl { color: #008000 } /* Literal.String.Delimiter */
+html[data-theme="light"] .highlight .sd { color: #008000 } /* Literal.String.Doc */
+html[data-theme="light"] .highlight .s2 { color: #008000 } /* Literal.String.Double */
+html[data-theme="light"] .highlight .se { color: #008000 } /* Literal.String.Escape */
+html[data-theme="light"] .highlight .sh { color: #008000 } /* Literal.String.Heredoc */
+html[data-theme="light"] .highlight .si { color: #008000 } /* Literal.String.Interpol */
+html[data-theme="light"] .highlight .sx { color: #008000 } /* Literal.String.Other */
+html[data-theme="light"] .highlight .sr { color: #d91e18 } /* Literal.String.Regex */
+html[data-theme="light"] .highlight .s1 { color: #008000 } /* Literal.String.Single */
+html[data-theme="light"] .highlight .ss { color: #007faa } /* Literal.String.Symbol */
+html[data-theme="light"] .highlight .bp { color: #797129 } /* Name.Builtin.Pseudo */
+html[data-theme="light"] .highlight .fm { color: #007faa } /* Name.Function.Magic */
+html[data-theme="light"] .highlight .vc { color: #d91e18 } /* Name.Variable.Class */
+html[data-theme="light"] .highlight .vg { color: #d91e18 } /* Name.Variable.Global */
+html[data-theme="light"] .highlight .vi { color: #d91e18 } /* Name.Variable.Instance */
+html[data-theme="light"] .highlight .vm { color: #797129 } /* Name.Variable.Magic */
+html[data-theme="light"] .highlight .il { color: #797129 } /* Literal.Number.Integer.Long */
+html[data-theme="dark"] .highlight pre { line-height: 125%; }
+html[data-theme="dark"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+html[data-theme="dark"] .highlight .hll { background-color: #ffd9002e }
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+html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
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\ No newline at end of file
diff --git a/0.8/_static/scripts/bootstrap.js b/0.8/_static/scripts/bootstrap.js
new file mode 100644
index 00000000..766173ab
--- /dev/null
+++ b/0.8/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
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DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Mt(e)?be.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Mt(e)?be.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const n of Object.keys(e)){const s=e[n],o=t[n],r=Mt(o)?"element":null==(i=o)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class ye extends ve{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),ge.set(this._element,this.constructor.DATA_KEY,this))}dispose(){ge.remove(this._element,this.constructor.DATA_KEY),de.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){Yt(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return ge.get(Ht(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.2.3"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const we=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;de.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Ft(this))return;const s=Pt(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Ee=`close${Ae}`,Ce=`closed${Ae}`;class Te extends ye{static get NAME(){return"alert"}close(){if(de.trigger(this._element,Ee).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),de.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}we(Te,"close"),Qt(Te);const Oe='[data-bs-toggle="button"]';class xe extends ye{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=xe.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}de.on(document,"click.bs.button.data-api",Oe,(t=>{t.preventDefault();const e=t.target.closest(Oe);xe.getOrCreateInstance(e).toggle()})),Qt(xe);const ke={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!Ft(t)&&Wt(t)))}},Le=".bs.swipe",De=`touchstart${Le}`,$e=`touchmove${Le}`,Se=`touchend${Le}`,Ie=`pointerdown${Le}`,Ne=`pointerup${Le}`,Pe={endCallback:null,leftCallback:null,rightCallback:null},je={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class Me extends ve{constructor(t,e){super(),this._element=t,t&&Me.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return Pe}static get DefaultType(){return je}static get NAME(){return"swipe"}dispose(){de.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Xt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Xt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(de.on(this._element,Ie,(t=>this._start(t))),de.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(de.on(this._element,De,(t=>this._start(t))),de.on(this._element,$e,(t=>this._move(t))),de.on(this._element,Se,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",We=".data-api",Fe="next",Be="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ke=`keydown${He}`,Qe=`mouseenter${He}`,Xe=`mouseleave${He}`,Ye=`dragstart${He}`,Ue=`load${He}${We}`,Ge=`click${He}${We}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(Fe)}nextWhenVisible(){!document.hidden&&Wt(this._element)&&this.next()}prev(){this._slide(Be)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?de.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void de.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?Fe:Be;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&de.on(this._element,Ke,(t=>this._keydown(t))),"hover"===this._config.pause&&(de.on(this._element,Qe,(()=>this.pause())),de.on(this._element,Xe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))de.on(t,Ye,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===Fe,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>de.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Kt()?t===ze?Be:Fe:t===ze?Fe:Be}_orderToDirection(t){return Kt()?t===Be?ze:qe:t===Be?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}de.on(document,Ge,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Pt(this);if(!e||!e.classList.contains(Je))return;t.preventDefault();const i=ri.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===be.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),de.on(window,Ue,(()=>{const t=ke.find('[data-bs-ride="carousel"]');for(const e of t)ri.getOrCreateInstance(e)})),Qt(ri);const ai=".bs.collapse",li=`show${ai}`,ci=`shown${ai}`,hi=`hide${ai}`,ui=`hidden${ai}`,di=`click${ai}.data-api`,fi="show",pi="collapse",gi="collapsing",mi=`:scope .${pi} .${pi}`,_i='[data-bs-toggle="collapse"]',bi={parent:null,toggle:!0},vi={parent:"(null|element)",toggle:"boolean"};class yi extends ye{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=ke.find(_i);for(const t of i){const e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(de.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",de.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(de.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),de.trigger(this._element,ui)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}de.on(document,di,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Qt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Kt()?"top-end":"top-start",Hi=Kt()?"top-start":"top-end",Wi=Kt()?"bottom-end":"bottom-start",Fi=Kt()?"bottom-start":"bottom-end",Bi=Kt()?"left-start":"right-start",zi=Kt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Ft(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!de.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),de.trigger(this._element,Li,t)}}hide(){if(Ft(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!de.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),de.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Fi:Wi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Wt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}de.on(document,$i,Ni,Vi.dataApiKeydownHandler),de.on(document,$i,ji,Vi.dataApiKeydownHandler),de.on(document,Di,Vi.clearMenus),de.on(document,Si,Vi.clearMenus),de.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Qt(Vi);const Ki=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Qi=".sticky-top",Xi="padding-right",Yi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Xi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e+t)),this._setElementAttributes(Qi,Yi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Xi),this._resetElementAttributes(Ki,Xi),this._resetElementAttributes(Qi,Yi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Xt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Xt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Xt(t)}))):Xt(t)}dispose(){this._isAppended&&(de.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),de.on(t,Zi,(()=>{Xt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Yt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),de.off(document,sn),de.on(document,on,(t=>this._handleFocusin(t))),de.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,de.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const un=".bs.modal",dn=`hide${un}`,fn=`hidePrevented${un}`,pn=`hidden${un}`,gn=`show${un}`,mn=`shown${un}`,_n=`resize${un}`,bn=`click.dismiss${un}`,vn=`mousedown.dismiss${un}`,yn=`keydown.dismiss${un}`,wn=`click${un}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||de.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(de.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])de.off(t,un);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,de.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){de.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),de.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),de.on(this._element,vn,(t=>{de.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),de.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(de.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Kt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Kt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}de.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),de.one(e,gn,(t=>{t.defaultPrevented||de.one(e,pn,(()=>{Wt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Qt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Wn=`hidden${kn}`,Fn=`resize${kn}`,Bn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||de.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),de.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(de.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),de.trigger(this._element,Wn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():de.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){de.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():de.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}de.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this))return;de.one(e,Wn,(()=>{Wt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),de.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),de.on(window,Fn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Qt(Vn);const Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Xn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Yn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)||Xn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Yn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Kt()?"left":"right",BOTTOM:"bottom",LEFT:Kt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class us extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),de.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=de.trigger(this._element,this.constructor.eventName("show")),e=(Bt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),de.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.on(t,"mouseover",zt);this._queueCallback((()=>{de.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!de.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))de.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),de.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)de.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");de.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),de.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},de.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(us);const ds={...us.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},fs={...us.DefaultType,content:"(null|string|element|function)"};class ps extends us{static get Default(){return ds}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Qt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return Es}static get DefaultType(){return Cs}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ht(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(de.off(this._config.target,_s),de.on(this._config.target,_s,ys,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Ft(e))continue;const t=ke.findOne(e.hash,this._element);Wt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),de.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Qt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Ws="fade",Fs="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),de.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?de.trigger(e,xs,{relatedTarget:t}):null;de.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),de.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Fs)}),t,t.classList.contains(Ws)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),de.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Fs)}),t,t.classList.contains(Ws)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Ft(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ke.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Hs),n(".dropdown-menu",Fs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(qs)?t:ke.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}de.on(document,$s,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Ft(this)||Vs.getOrCreateInstance(this).show()})),de.on(window,Is,(()=>{for(const t of ke.find(Rs))Vs.getOrCreateInstance(t)})),Qt(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){de.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),de.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(de.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),de.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){de.on(this._element,Qs,(t=>this._onInteraction(t,!0))),de.on(this._element,Xs,(t=>this._onInteraction(t,!1))),de.on(this._element,Ys,(t=>this._onInteraction(t,!0))),de.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}var ao;we(ro),Qt(ro),ao=function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new us(t,{delay:{show:500,hide:100}})}))},"loading"!=document.readyState?ao():document.addEventListener("DOMContentLoaded",ao)})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/0.8/_static/scripts/bootstrap.js.LICENSE.txt b/0.8/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 00000000..91ad10aa
--- /dev/null
+++ b/0.8/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.2.3 (https://getbootstrap.com/)
+  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/0.8/_static/scripts/bootstrap.js.map b/0.8/_static/scripts/bootstrap.js.map
new file mode 100644
index 00000000..d83e2f7c
--- /dev/null
+++ b/0.8/_static/scripts/bootstrap.js.map
@@ -0,0 +1 @@
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= 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (process.env.NODE_ENV !== \"production\") {\n    if (!isHTMLElement(arrowElement)) {\n      console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n    }\n\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n  if (process.env.NODE_ENV !== \"production\") {\n    var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n    if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n      return transitionProperty.indexOf(property) >= 0;\n    })) {\n      console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n    }\n  }\n\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n\n    if (process.env.NODE_ENV !== \"production\") {\n      console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n    }\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        }); // Validate the provided modifiers so that the consumer will get warned\n        // if one of the modifiers is invalid for any reason\n\n        if (process.env.NODE_ENV !== \"production\") {\n          var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n            var name = _ref.name;\n            return name;\n          });\n          validateModifiers(modifiers);\n\n          if (getBasePlacement(state.options.placement) === auto) {\n            var flipModifier = state.orderedModifiers.find(function (_ref2) {\n              var name = _ref2.name;\n              return name === 'flip';\n            });\n\n            if (!flipModifier) {\n              console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n            }\n          }\n\n          var _getComputedStyle = getComputedStyle(popper),\n              marginTop = _getComputedStyle.marginTop,\n              marginRight = _getComputedStyle.marginRight,\n              marginBottom = _getComputedStyle.marginBottom,\n              marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n          // cause bugs with positioning, so we'll warn the consumer\n\n\n          if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n            return parseFloat(margin);\n          })) {\n            console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n          }\n        }\n\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          if (process.env.NODE_ENV !== \"production\") {\n            console.error(INVALID_ELEMENT_ERROR);\n          }\n\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n        var __debug_loops__ = 0;\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (process.env.NODE_ENV !== \"production\") {\n            __debug_loops__ += 1;\n\n            if (__debug_loops__ > 100) {\n              console.error(INFINITE_LOOP_ERROR);\n              break;\n            }\n          }\n\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      if (process.env.NODE_ENV !== \"production\") {\n        console.error(INVALID_ELEMENT_ERROR);\n      }\n\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref3) {\n        var name = _ref3.name,\n            _ref3$options = _ref3.options,\n            options = _ref3$options === void 0 ? {} : _ref3$options,\n            effect = _ref3.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.2.3 (https://getbootstrap.com/)\n  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n\n  return prefix;\n};\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n  }\n\n  return selector;\n};\n\nconst getSelectorFromElement = element => {\n  const selector = getSelector(element);\n\n  if (selector) {\n    return document.querySelector(selector) ? selector : null;\n  }\n\n  return null;\n};\n\nconst getElementFromSelector = element => {\n  const selector = getSelector(element);\n  return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  } // Get transition-duration of the element\n\n\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  } // If multiple durations are defined, take the first\n\n\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n\n  return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(object);\n  }\n\n  return null;\n};\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n  const closedDetails = element.closest('details:not([open])');\n\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n\n    if (summary === null) {\n      return false;\n    }\n  }\n\n  return elementIsVisible;\n};\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  } // Can find the shadow root otherwise it'll return the document\n\n\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element;\n  } // when we don't find a shadow root\n\n\n  if (!element.parentNode) {\n    return null;\n  }\n\n  return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n\n  return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\n\nconst execute = callback => {\n  if (typeof callback === 'function') {\n    callback();\n  }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n\n  index += shouldGetNext ? 1 : -1;\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n\n    return fn.apply(element, [event]);\n  };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n\n  return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n\n    callable = wrapFunction(callable);\n  }\n\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n  if (!fn) {\n    return;\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n\n  for (const handlerKey of Object.keys(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      const event = storeElementEvent[handlerKey];\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n\n    for (const keyHandlers of Object.keys(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        const event = storeElementEvent[keyHandlers];\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n\n    let evt = new Event(event, {\n      bubbles,\n      cancelable: true\n    });\n    evt = hydrateObj(evt, args);\n\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n\n    return evt;\n  }\n\n};\n\nfunction hydrateObj(obj, meta) {\n  for (const [key, value] of Object.entries(meta || {})) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n\n        get() {\n          return value;\n        }\n\n      });\n    }\n  }\n\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n\n    const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n\n    instanceMap.set(key, instance);\n  },\n\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n\n    return null;\n  },\n\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n\n  if (value === 'false') {\n    return false;\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n\n  if (value === '' || value === 'null') {\n    return null;\n  }\n\n  if (typeof value !== 'string') {\n    return value;\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n\n    return attributes;\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n\n  static get DefaultType() {\n    return {};\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    return config;\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return { ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const property of Object.keys(configTypes)) {\n      const expectedTypes = configTypes[property];\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n\n    if (!element) {\n      return;\n    }\n\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  } // Public\n\n\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  } // Static\n\n\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n\n  static get VERSION() {\n    return VERSION;\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n\n    if (isDisabled(this)) {\n      return;\n    }\n\n    const target = getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  } // Public\n\n\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  } // Private\n\n\n  _destroyElement() {\n    this._element.remove();\n\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  } // Public\n\n\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n\n    return parents;\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n\n      previous = previous.previousElementSibling;\n    }\n\n    return [];\n  },\n\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n\n      next = next.nextElementSibling;\n    }\n\n    return [];\n  },\n\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n\n    this._initEvents();\n  } // Getters\n\n\n  static get Default() {\n    return Default$c;\n  }\n\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n\n  static get NAME() {\n    return NAME$d;\n  } // Public\n\n\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  } // Private\n\n\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n\n    this._handleSwipe();\n\n    execute(this._config.endCallback);\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n\n    if (!direction) {\n      return;\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  } // Static\n\n\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n    this._addEventListeners();\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$b;\n  }\n\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n\n  static get NAME() {\n    return NAME$c;\n  } // Public\n\n\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n\n    this._clearInterval();\n  }\n\n  cycle() {\n    this._clearInterval();\n\n    this._updateInterval();\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n\n    this.cycle();\n  }\n\n  to(index) {\n    const items = this._getItems();\n\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive());\n\n    if (activeIndex === index) {\n      return;\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n    this._slide(order, items[index]);\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      } // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n\n      this.pause();\n\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key];\n\n    if (direction) {\n      event.preventDefault();\n\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n\n    if (!element) {\n      return;\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n\n    const activeElement = this._getActive();\n\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n    if (nextElement === activeElement) {\n      return;\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement);\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // todo: change tests that use empty divs to avoid this check\n      return;\n    }\n\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n\n    this._setActiveIndicatorElement(nextElementIndex);\n\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n\n  if (slideIndex) {\n    carousel.to(slideIndex);\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n\n    carousel._maybeEnableCycle();\n\n    return;\n  }\n\n  carousel.prev();\n\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n    for (const elem of toggleList) {\n      const selector = getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n\n    this._initializeChildren();\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$a;\n  }\n\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n\n  static get NAME() {\n    return NAME$b;\n  } // Public\n\n\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n\n    let activeChildren = []; // find active children\n\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.style[dimension] = 0;\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n\n    this._queueCallback(complete, this._element, true);\n\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n\n    const dimension = this._getDimension();\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n    for (const trigger of this._triggerArray) {\n      const element = getElementFromSelector(trigger);\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n\n    this._isTransitioning = true;\n\n    const complete = () => {\n      this._isTransitioning = false;\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n\n    this._element.style[dimension] = '';\n\n    this._queueCallback(complete, this._element, true);\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  } // Private\n\n\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n\n    config.parent = getElement(config.parent);\n    return config;\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n    for (const element of children) {\n      const selected = getElementFromSelector(element);\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    const _config = {};\n\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config]();\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n\n  const selector = getSelectorFromElement(this);\n  const selectorElements = SelectorEngine.find(selector);\n\n  for (const element of selectorElements) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  } // Getters\n\n\n  static get Default() {\n    return Default$9;\n  }\n\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n\n  static get NAME() {\n    return NAME$a;\n  } // Public\n\n\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._createPopper(); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    this._element.focus();\n\n    this._element.setAttribute('aria-expanded', true);\n\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n\n    this._completeHide(relatedTarget);\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    super.dispose();\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar();\n\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Private\n\n\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    } // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy();\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n    this._element.setAttribute('aria-expanded', 'false');\n\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config);\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n\n    return config;\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n\n    let referenceElement = this._element;\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n\n    const popperConfig = this._getPopperConfig();\n\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent;\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    } // We need to trim the value because custom properties can also include spaces\n\n\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n    if (!items.length) {\n      return;\n    } // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n\n      context._completeHide(relatedTarget);\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n\n    event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n\n      instance._selectMenuItem(event);\n\n      return;\n    }\n\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  } // Public\n\n\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n\n  hide() {\n    const width = this.getWidth();\n\n    this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0;\n  } // Private\n\n\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n\n    this._element.style.overflow = 'hidden';\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n\n      this._saveInitialAttribute(element, styleProperty);\n\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$8;\n  }\n\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n\n  static get NAME() {\n    return NAME$9;\n  } // Public\n\n\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._append();\n\n    const element = this._getElement();\n\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n\n    element.classList.add(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n    this._element.remove();\n\n    this._isAppended = false;\n  } // Private\n\n\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n\n      this._element = backdrop;\n    }\n\n    return this._element;\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n\n    const element = this._getElement();\n\n    this._config.rootElement.append(element);\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  } // Getters\n\n\n  static get Default() {\n    return Default$7;\n  }\n\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n\n  static get NAME() {\n    return NAME$8;\n  } // Public\n\n\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  } // Private\n\n\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement);\n\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$6;\n  }\n\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n\n  static get NAME() {\n    return NAME$7;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n    this._isTransitioning = true;\n\n    this._scrollBar.hide();\n\n    document.body.classList.add(CLASS_NAME_OPEN);\n\n    this._adjustDialog();\n\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = false;\n    this._isTransitioning = true;\n\n    this._focustrap.deactivate();\n\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n\n  dispose() {\n    for (const htmlElement of [window, this._dialog]) {\n      EventHandler.off(htmlElement, EVENT_KEY$4);\n    }\n\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  }\n\n  handleUpdate() {\n    this._adjustDialog();\n  } // Private\n\n\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n\n    this._element.style.display = 'block';\n\n    this._element.removeAttribute('aria-hidden');\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n\n      if (this._config.keyboard) {\n        event.preventDefault();\n        this.hide();\n        return;\n      }\n\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n\n          return;\n        }\n\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none';\n\n    this._element.setAttribute('aria-hidden', true);\n\n    this._element.removeAttribute('aria-modal');\n\n    this._element.removeAttribute('role');\n\n    this._isTransitioning = false;\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n\n      this._resetAdjustments();\n\n      this._scrollBar.reset();\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC);\n\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n\n    this._element.focus();\n  }\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n    const scrollbarWidth = this._scrollBar.getWidth();\n\n    const isBodyOverflowing = scrollbarWidth > 0;\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  } // Static\n\n\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](relatedTarget);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  }); // avoid conflict when clicking modal toggler while another one is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n\n    this._addEventListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default$5;\n  }\n\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n\n  static get NAME() {\n    return NAME$6;\n  } // Public\n\n\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._isShown = true;\n\n    this._backdrop.show();\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n\n    this._element.setAttribute('aria-modal', true);\n\n    this._element.setAttribute('role', 'dialog');\n\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._focustrap.deactivate();\n\n    this._element.blur();\n\n    this._isShown = false;\n\n    this._element.classList.add(CLASS_NAME_HIDING);\n\n    this._backdrop.hide();\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n      this._element.removeAttribute('aria-modal');\n\n      this._element.removeAttribute('role');\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n\n    this._queueCallback(completeCallback, this._element, true);\n  }\n\n  dispose() {\n    this._backdrop.dispose();\n\n    this._focustrap.deactivate();\n\n    super.dispose();\n  } // Private\n\n\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n\n      if (!this._config.keyboard) {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n\n      this.hide();\n    });\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config](this);\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = getElementFromSelector(this);\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n    }\n\n    return true;\n  } // Check if a regular expression validates the attribute.\n\n\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  } // Getters\n\n\n  static get Default() {\n    return Default$4;\n  }\n\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n\n  static get NAME() {\n    return NAME$5;\n  } // Public\n\n\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n\n  changeContent(content) {\n    this._checkContent(content);\n\n    this._config.content = { ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n\n    const template = templateWrapper.children[0];\n\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n\n    return template;\n  } // Private\n\n\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n\n    this._checkContent(config.content);\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n\n    if (!templateElement) {\n      return;\n    }\n\n    content = this._resolvePossibleFunction(content);\n\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n\n      return;\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n\n    templateElement.textContent = content;\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg(this) : arg;\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n\n    templateElement.textContent = element.textContent;\n  }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 0],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n\n    super(element, config); // Private\n\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null; // Protected\n\n    this.tip = null;\n\n    this._setListeners();\n\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  } // Getters\n\n\n  static get Default() {\n    return Default$3;\n  }\n\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n\n  static get NAME() {\n    return NAME$4;\n  } // Public\n\n\n  enable() {\n    this._isEnabled = true;\n  }\n\n  disable() {\n    this._isEnabled = false;\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click;\n\n    if (this._isShown()) {\n      this._leave();\n\n      return;\n    }\n\n    this._enter();\n  }\n\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n\n    this._disposePopper();\n\n    super.dispose();\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    } // todo v6 remove this OR make it optional\n\n\n    this._disposePopper();\n\n    const tip = this._getTipElement();\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n    const {\n      container\n    } = this._config;\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n      if (this._isHovered === false) {\n        this._leave();\n      }\n\n      this._isHovered = false;\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const tip = this._getTipElement();\n\n    tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n\n      this._element.removeAttribute('aria-describedby');\n\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  } // Protected\n\n\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n\n    return this.tip;\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n    if (!tip) {\n      return null;\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n\n    return tip;\n  }\n\n  setContent(content) {\n    this._newContent = content;\n\n    if (this._isShown()) {\n      this._disposePopper();\n\n      this.show();\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({ ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n\n    return this._templateFactory;\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  } // Private\n\n\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n\n  _createPopper(tip) {\n    const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n\n    return offset;\n  }\n\n  _resolvePossibleFunction(arg) {\n    return typeof arg === 'function' ? arg.call(this._element) : arg;\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return { ...defaultBsPopperConfig,\n      ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n    };\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n          context._leave();\n        });\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n\n    if (!title) {\n      return;\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n    this._element.removeAttribute('title');\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n\n    this._isHovered = true;\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n\n    this._isHovered = false;\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n\n    config = { ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n\n    this._typeCheckConfig(config);\n\n    return config;\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n\n    return config;\n  }\n\n  _getDelegateConfig() {\n    const config = {};\n\n    for (const key in this._config) {\n      if (this.constructor.Default[key] !== this._config[key]) {\n        config[key] = this._config[key];\n      }\n    }\n\n    config.selector = false;\n    config.trigger = 'manual'; // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n\n    return config;\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n\n      this._popper = null;\n    }\n\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n\n  static get NAME() {\n    return NAME$3;\n  } // Overrides\n\n\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  } // Private\n\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  } // Getters\n\n\n  static get Default() {\n    return Default$1;\n  }\n\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n\n  static get NAME() {\n    return NAME$2;\n  } // Public\n\n\n  refresh() {\n    this._initializeTargetsAndObservables();\n\n    this._maybeEnableSmoothScroll();\n\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect();\n\n    super.dispose();\n  } // Private\n\n\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n\n    return config;\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    } // unregister any previous listeners\n\n\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        } // Chrome 60 doesn't support `scrollTo`\n\n\n        root.scrollTop = height;\n      }\n    });\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  } // The logic of selection\n\n\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n      this._process(targetElement(entry));\n    };\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n\n        this._clearActiveClass(targetElement(entry));\n\n        continue;\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n        if (!parentScrollTop) {\n          return;\n        }\n\n        continue;\n      } // if we are scrolling up, pick the smallest offsetTop\n\n\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n\n      const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(anchor.hash, anchor);\n\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n\n    this._clearActiveClass(this._config.target);\n\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n\n    this._activateParents(target);\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)';\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // todo:v6: could be only `tab`\n\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n\n    if (!this._parent) {\n      return; // todo: should Throw exception on v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    } // Set up initial aria attributes\n\n\n    this._setInitialAttributes(this._parent, this._getChildren());\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  } // Getters\n\n\n  static get NAME() {\n    return NAME$1;\n  } // Public\n\n\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n\n    if (this._elemIsActive(innerElem)) {\n      return;\n    } // Search for active tab on same parent to deactivate it\n\n\n    const active = this._getActiveElem();\n\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n\n    this._deactivate(active, innerElem);\n\n    this._activate(innerElem, active);\n  } // Private\n\n\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE);\n\n    this._activate(getElementFromSelector(element)); // Search and activate/show the proper section\n\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n\n      this._toggleDropDown(element, true);\n\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n\n    this._deactivate(getElementFromSelector(element)); // Search and deactivate the shown section too\n\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n\n      this._toggleDropDown(element, false);\n\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)) {\n      return;\n    }\n\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n\n    event.preventDefault();\n    const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n    const nextActiveElement = getNextActiveElement(this._getChildren().filter(element => !isDisabled(element)), event.target, isNext, true);\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n\n    const isActive = this._elemIsActive(child);\n\n    const outerElem = this._getOuterElement(child);\n\n    child.setAttribute('aria-selected', isActive);\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab'); // set attributes to the related panel too\n\n\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = getElementFromSelector(child);\n\n    if (!target) {\n      return;\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `#${child.id}`);\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  } // Try to get the inner element (usually the .nav-link)\n\n\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  } // Try to get the outer element (usually the .nav-item)\n\n\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n\n      if (typeof config !== 'string') {\n        return;\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n\n      data[config]();\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n\n  if (isDisabled(this)) {\n    return;\n  }\n\n  Tab.getOrCreateInstance(this).show();\n});\n/**\n * Initialize on focus\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\n\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n\n    this._setListeners();\n  } // Getters\n\n\n  static get Default() {\n    return Default;\n  }\n\n  static get DefaultType() {\n    return DefaultType;\n  }\n\n  static get NAME() {\n    return NAME;\n  } // Public\n\n\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n\n    this._clearTimeout();\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n\n      this._maybeScheduleHide();\n    };\n\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n\n\n    reflow(this._element);\n\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n\n\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n\n    this._element.classList.add(CLASS_NAME_SHOWING);\n\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n\n  dispose() {\n    this._clearTimeout();\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n\n    super.dispose();\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  } // Private\n\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout();\n\n      return;\n    }\n\n    const nextElement = event.relatedTarget;\n\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n\n    this._maybeScheduleHide();\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  } // Static\n\n\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n\n        data[config](this);\n      }\n    });\n  }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Toast);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Import and setup functions to control Bootstrap's behavior.\nimport 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\"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\"\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if ((event.ctrlKey || event.metaKey) && event.code == \"KeyK\") {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && event.code == \"Escape\") {\n        toggleSearchField();\n      }\n    },\n    true\n  );\n};\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\nvar changeSearchShortcutKey = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  var isMac = window.navigator.platform.toUpperCase().indexOf(\"MAC\") >= 0;\n  if (isMac) {\n    forms.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\")\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nfunction checkPageExistsAndRedirect(event) {\n  const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`,\n    tryUrl = event.target.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n\n  fetch(tryUrl, { method: \"HEAD\" })\n    .then(() => {\n      location.href = tryUrl;\n    }) // if the page exists, go there\n    .catch((error) => {\n      location.href = otherDocsHomepage;\n    });\n\n  // this prevents the browser from following the href of the clicked node\n  // (which is fine because this function takes care of redirecting)\n  return false;\n}\n\n// Populate the version switcher from the JSON config file\nvar themeSwitchBtns = document.querySelectorAll(\".version-switcher__button\");\nif (themeSwitchBtns.length) {\n  fetch(DOCUMENTATION_OPTIONS.theme_switcher_json_url)\n    .then((res) => {\n      return res.json();\n    })\n    .then((data) => {\n      const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n      themeSwitchBtns.forEach((btn) => {\n        // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n        btn.dataset[\"activeVersionName\"] = \"\";\n        btn.dataset[\"activeVersion\"] = \"\";\n      });\n      // create links to the corresponding page in the other docs versions\n      data.forEach((entry) => {\n        // if no custom name specified (e.g., \"latest\"), use version string\n        if (!(\"name\" in entry)) {\n          entry.name = entry.version;\n        }\n        // create the node\n        const span = document.createElement(\"span\");\n        span.textContent = `${entry.name}`;\n\n        const node = document.createElement(\"a\");\n        node.setAttribute(\n          \"class\",\n          \"list-group-item list-group-item-action py-1\"\n        );\n        node.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n        node.appendChild(span);\n\n        // on click, AJAX calls will check if the linked page exists before\n        // trying to redirect, and if not, will redirect to the homepage\n        // for that version of the docs.\n        node.onclick = checkPageExistsAndRedirect;\n        // Add dataset values for the version and name in case people want\n        // to apply CSS styling based on this information.\n        node.dataset[\"versionName\"] = entry.name;\n        node.dataset[\"version\"] = entry.version;\n\n        document.querySelector(\".version-switcher__menu\").append(node);\n        // replace dropdown button text with the preferred display name of\n        // this version, rather than using sphinx's {{ version }} variable.\n        // also highlight the dropdown entry for the currently-viewed\n        // version's entry\n        if (\n          entry.version ==\n          \"DOCUMENTATION_OPTIONS.version_switcher_version_match\"\n        ) {\n          node.classList.add(\"active\");\n          themeSwitchBtns.forEach((btn) => {\n            btn.innerText = btn.dataset[\"activeVersionName\"] = entry.name;\n            btn.dataset[\"activeVersion\"] = entry.version;\n          });\n        }\n      });\n    });\n}\n\n/*******************************************************************************\n * MutationObserver to move the ReadTheDocs button\n */\n\n/**\n * intercept the RTD flyout and place it in the rtd-footer-container if existing\n * if not it stays where on top of the page\n */\nfunction initRTDObserver() {\n  const mutatedCallback = (mutationList, observer) => {\n    mutationList.forEach((mutation) => {\n      // Check whether the mutation is for RTD, which will have a specific structure\n      if (mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n 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\ No newline at end of file
diff --git a/0.8/_static/searchtools.js b/0.8/_static/searchtools.js
new file mode 100644
index 00000000..7918c3fa
--- /dev/null
+++ b/0.8/_static/searchtools.js
@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/0.8/_static/sg_gallery-binder.css b/0.8/_static/sg_gallery-binder.css
new file mode 100644
index 00000000..420005d2
--- /dev/null
+++ b/0.8/_static/sg_gallery-binder.css
@@ -0,0 +1,11 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/0.8/_static/sg_gallery-dataframe.css b/0.8/_static/sg_gallery-dataframe.css
new file mode 100644
index 00000000..fac74c43
--- /dev/null
+++ b/0.8/_static/sg_gallery-dataframe.css
@@ -0,0 +1,47 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+  width: auto;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/0.8/_static/sg_gallery-rendered-html.css b/0.8/_static/sg_gallery-rendered-html.css
new file mode 100644
index 00000000..93dc2ffb
--- /dev/null
+++ b/0.8/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/0.8/_static/sg_gallery.css b/0.8/_static/sg_gallery.css
new file mode 100644
index 00000000..72227837
--- /dev/null
+++ b/0.8/_static/sg_gallery.css
@@ -0,0 +1,342 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+.sphx-glr-thumbcontainer[tooltip]:hover:after {
+  background: var(--sg-tooltip-background);
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px;
+  z-index: 98;
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  backdrop-filter: blur(3px);
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
+
+p.sphx-glr-signature a.reference.external {
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  padding: 3px;
+  font-size: 75%;
+  text-align: right;
+  margin-left: auto;
+  display: table;
+}
+
+.sphx-glr-clear {
+  clear: both;
+}
+
+a.sphx-glr-backref-instance {
+  text-decoration: none;
+}
diff --git a/0.8/_static/sphinx_highlight.js b/0.8/_static/sphinx_highlight.js
new file mode 100644
index 00000000..8a96c69a
--- /dev/null
+++ b/0.8/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/0.8/_static/style.css b/0.8/_static/style.css
new file mode 100644
index 00000000..83cdd28d
--- /dev/null
+++ b/0.8/_static/style.css
@@ -0,0 +1,221 @@
+:root {
+    --text-align: justify;
+    --pst-font-size-base: 16px;
+    --pst-font-size-milli: 13px;
+    --pst-font-family-base: 'Source Sans Pro', var(--pst-font-family-base-system);
+    --pst-font-family-heading: var(--pst-font-family-base);
+    --pst-font-family-monospace: 'Source Code Pro', var(--pst-font-family-monospace-system);
+}
+
+html[data-theme="light"] {
+    ---color-github: #000;
+    --braindecode-color-discourse: #000;
+    --braindecode-color-discord: #5865F2;
+    --braindecode-color-twitter: #55acee;
+    --pst-color-border-rgba: rgba(0, 0, 0, 0.125);
+    --pst-color-background-rgba: rgba(0, 0, 0, 0.03);
+    --pst-color-primary-rgba: rgba(69, 157, 185, 0.2);
+    --pst-color-secondary-rgba: rgba(238, 144, 64, 0.2);
+    --copybtn-opacity: 0.75;
+    /* sphinx-gallery overrides */
+    --sg-download-a-background-color: var(--pst-color-primary);
+    --sg-download-a-background-image: unset;
+    --sg-download-a-border-color: var(--pst-color-border-rgba);
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: var(--pst-color-secondary);
+    --sg-download-a-hover-box-shadow-1: none;
+    --sg-download-a-hover-box-shadow-2: none;
+}
+html[data-theme="dark"] {
+    --braindecode-color-github: #fff;
+    --braindecode-color-discourse: #FFF9AE;  /* color taken from their logo SVG */
+    --braindecode-color-discord: #fff;
+    --braindecode-color-twitter: #55acee;
+    --pst-color-border-rgba: rgba(201, 201, 201, 0.25);
+    --pst-color-background-rgba: rgba(255, 255, 255, 0.08);
+    --pst-color-primary-rgba: rgba(69, 157, 185, 0.3);
+    --pst-color-secondary-rgba: rgba(238, 144, 64, 0.3);
+    --copybtn-opacity: 0.25;
+    /* sphinx-gallery overrides */
+    --sg-download-a-background-color: var(--pst-color-primary);
+    --sg-download-a-background-image: unset;
+    --sg-download-a-border-color: var(--pst-color-border-rgba);
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: var(--pst-color-secondary);
+    --sg-download-a-hover-box-shadow-1: none;
+    --sg-download-a-hover-box-shadow-2: none;
+}
+
+/* ************************************************************ Sphinx fixes */
+dl.field-list {
+    grid-template-columns: auto 1fr;
+}
+
+/* ********************************************************** Sphinx-gallery */
+
+/* backreference links: restore hover decoration that SG removes */
+a.sphx-glr-backref-instance:hover {
+    text-decoration: underline;
+}
+/* backreference links: make non-Brainde func/meth calls resemble regular code */
+a[class^="sphx-glr-backref-module"] {
+    color: rgb(var(--pst-color-text-base));
+}
+/* backreference links: make Braindecode calls bold and colorful */
+a[class^="sphx-glr-backref-module-braindecode"] {
+    font-weight: 600;
+    color: rgb(var(--pst-color-link));
+}
+/* suppress redundant note at top of every tutorial and signature at the end */
+div.sphx-glr-download-link-note,
+p.sphx-glr-signature {
+    visibility: hidden;
+    height: 0;
+    margin: 0;
+    padding: 0;
+}
+/* script/notebook download buttons */
+.sphx-glr-download a.download {
+    /* ↓↓↓↓↓↓↓ these two rules copied from sphinx-design */
+    box-shadow: 0 .125rem .25rem var(--sd-color-shadow) !important;
+    transition: color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out;
+}
+/* Report embedding */
+iframe.sg_report {
+    width: 95%;
+    height: 70vh;
+    margin: 20px auto;
+    display: block;
+    border-style: solid;
+}
+/* Disable thumbnail tooltips on hover */
+.sphx-glr-thumbcontainer[tooltip]:hover::before,
+.sphx-glr-thumbcontainer[tooltip]:hover::after {
+    display: none;
+}
+
+/* ******************************** make HTML'd pandas dataframes scrollable */
+table.dataframe {
+    display: block;
+    overflow: auto;
+}
+
+/* ********************************* Long API titles need to wrap for mobile */
+div[id^="braindecode-"] h1,
+div[id^="examples-using-"] h2 {
+    word-break: break-word;
+}
+
+/* ******************************************* in-text sidebar callout boxes */
+div.sidebar,
+aside.sidebar {
+    background-color: var(--pst-color-primary-rgba);
+    border-color: rgb(var(--pst-color-info));
+}
+
+/* **************************************************************** homepage */
+img.logo {
+    max-width: 500px;
+    width: 100%;
+}
+
+/* ************************************* homepage quick links & funders list */
+ul.quicklinks {
+    font-weight: 600;
+}
+ul.quicklinks a:hover {
+    text-decoration: none;
+}
+ul.funders li {
+    margin-left: 36px;
+    text-indent: -36px;
+    padding-bottom: 9px;
+}
+ul.funders li img {
+    width: 30px;
+    max-height: 24px;
+    object-fit: contain;
+}
+
+/* these two also affect collapsible divs */
+h5.card-header {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+h5.card-header::before {
+    height: 0px;
+    margin-top: 0px;
+}
+
+/* ******************************************************** version dropdown */
+.dropdown {
+    padding: 0 .5rem;  /* match other items in the hamburger menu */
+}
+.dropdown-toggle {
+    font-weight: 600;
+}
+
+/* ********************************************* sphinx-design compatibility */
+.sd-dropdown {
+    border: 1px solid var(--pst-color-border-rgba) !important;
+    box-shadow: 1px 1px 1px var(--pst-color-shadow);
+}
+.sd-card-header {
+    border-bottom: 1px solid var(--pst-color-border-rgba) !important;
+    background-color: var(--pst-color-background-rgba) !important;
+}
+.sd-card-footer {
+    border-top: 1px solid var(--pst-color-border-rgba) !important;
+    background-color: var(--pst-color-background-rgba) !important;
+}
+p.btn a {
+    color: unset;
+}
+/* remove this when
+   https://github.com/pydata/pydata-sphinx-theme/issues/733 is addressed
+   ↓↓↓↓↓↓↓↓↓ */
+.sd-btn-primary {
+    background-color: var(--pst-color-link) !important;
+    border-color: var(--pst-color-link) !important
+}
+.sd-btn-primary:hover {
+    background-color: var(--pst-color-link-hover) !important;
+    border-color: var(--pst-color-link-hover) !important
+}
+
+/* ****************************************************** navbar quick links */
+
+#navbar-icon-links i.fa-github-square::before {
+    color: var(--braindecode-color-github);
+}
+#navbar-icon-links i.fa-twitter-square::before {
+    color: var(--braindecode-color-twitter);
+}
+#navbar-icon-links i.fa-discourse::before {
+    color: var(--braindecode-color-discourse);
+}
+#navbar-icon-links i.fa-discord::before {
+    color: var(--braindecode-color-discord);
+}
+/* *********************************************************** contrib guide */
+ul.icon-bullets {
+    list-style-type: none;
+    padding-left: 2em;
+    text-indent: -1.5em;
+}
+.small-stack {
+    font-size: 0.5em;
+}
+/* *********************************************************** copy button */
+button.copybtn {
+    /* always show; https://sphinx-copybutton.readthedocs.io/en/latest/use.html#modify-the-copy-button-s-css */
+    opacity: var(--copybtn-opacity);
+ }
+
+/* *********************************************************** miscellaneous */
+.hidden {
+    display: none;
+}
+img.hidden {
+    visibility: hidden;
+}
diff --git a/0.8/_static/styles/bootstrap.css b/0.8/_static/styles/bootstrap.css
new file mode 100644
index 00000000..0aa5f54f
--- /dev/null
+++ b/0.8/_static/styles/bootstrap.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap  v5.2.3 (https://getbootstrap.com/)
+ * Copyright 2011-2022 The Bootstrap Authors
+ * Copyright 2011-2022 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ 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var(--bs-accordion-border-color);color:var(--bs-accordion-active-color)}.accordion-button:not(.collapsed):after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button:after{background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);content:"";flex-shrink:0;height:var(--bs-accordion-btn-icon-width);margin-left:auto;transition:var(--bs-accordion-btn-icon-transition);width:var(--bs-accordion-btn-icon-width)}@media(prefers-reduced-motion:reduce){.accordion-button:after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{border-color:var(--bs-accordion-btn-focus-border-color);box-shadow:var(--bs-accordion-btn-focus-box-shadow);outline:0;z-index:3}.accordion-header{margin-bottom:0}.accordion-item{background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color);color:var(--bs-accordion-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-left-radius:var(--bs-accordion-inner-border-radius);border-bottom-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-left:0;border-radius:0;border-right:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:#6c757d;--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:#6c757d;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius);display:flex;flex-wrap:wrap;font-size:var(--bs-breadcrumb-font-size);list-style:none;margin-bottom:var(--bs-breadcrumb-margin-bottom);padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:#fff;--bs-pagination-border-width:1px;--bs-pagination-border-color:#dee2e6;--bs-pagination-border-radius:0.375rem;--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:#e9ecef;--bs-pagination-hover-border-color:#dee2e6;--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:#e9ecef;--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:#6c757d;--bs-pagination-disabled-bg:#fff;--bs-pagination-disabled-border-color:#dee2e6;display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color);color:var(--bs-pagination-hover-color);z-index:2}.page-link:focus{background-color:var(--bs-pagination-focus-bg);box-shadow:var(--bs-pagination-focus-box-shadow);color:var(--bs-pagination-focus-color);outline:0;z-index:3}.active>.page-link,.page-link.active{background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color);color:var(--bs-pagination-active-color);z-index:3}.disabled>.page-link,.page-link.disabled{background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color);color:var(--bs-pagination-disabled-color);pointer-events:none}.page-item:not(:first-child) .page-link{margin-left:-1px}.page-item:first-child 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var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:1px solid var(--bs-alert-border-color);--bs-alert-border-radius:0.375rem;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.alert-primary{--bs-alert-color:#084298;--bs-alert-bg:#cfe2ff;--bs-alert-border-color:#b6d4fe}.alert-primary 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75%,transparent 0,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-bar-animated{animation:progress-bar-stripes 1s linear 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\ No newline at end of file
diff --git a/0.8/_static/styles/pydata-sphinx-theme.css b/0.8/_static/styles/pydata-sphinx-theme.css
new file mode 100644
index 00000000..ca295d8a
--- /dev/null
+++ b/0.8/_static/styles/pydata-sphinx-theme.css
@@ -0,0 +1 @@
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diff --git a/0.8/_static/styles/theme.css b/0.8/_static/styles/theme.css
new file mode 100644
index 00000000..4519dd91
--- /dev/null
+++ b/0.8/_static/styles/theme.css
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+@import "../basic.css";
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diff --git a/0.8/_static/vendor/fontawesome/6.1.2/LICENSE.txt b/0.8/_static/vendor/fontawesome/6.1.2/LICENSE.txt
new file mode 100644
index 00000000..cc557ece
--- /dev/null
+++ b/0.8/_static/vendor/fontawesome/6.1.2/LICENSE.txt
@@ -0,0 +1,165 @@
+Fonticons, Inc. (https://fontawesome.com)
+
+--------------------------------------------------------------------------------
+
+Font Awesome Free License
+
+Font Awesome Free is free, open source, and GPL friendly. You can use it for
+commercial projects, open source projects, or really almost whatever you want.
+Full Font Awesome Free license: https://fontawesome.com/license/free.
+
+--------------------------------------------------------------------------------
+
+# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/)
+
+The Font Awesome Free download is licensed under a Creative Commons
+Attribution 4.0 International License and applies to all icons packaged
+as SVG and JS file types.
+
+--------------------------------------------------------------------------------
+
+# Fonts: SIL OFL 1.1 License
+
+In the Font Awesome Free download, the SIL OFL license applies to all icons
+packaged as web and desktop font files.
+
+Copyright (c) 2022 Fonticons, Inc. (https://fontawesome.com)
+with Reserved Font Name: "Font Awesome".
+
+This Font Software is licensed under the SIL Open Font License, Version 1.1.
+This license is copied below, and is also available with a FAQ at:
+http://scripts.sil.org/OFL
+
+SIL OPEN FONT LICENSE
+Version 1.1 - 26 February 2007
+
+PREAMBLE
+The goals of the Open Font License (OFL) are to stimulate worldwide
+development of collaborative font projects, to support the font creation
+efforts of academic and linguistic communities, and to provide a free and
+open framework in which fonts may be shared and improved in partnership
+with others.
+
+The OFL allows the licensed fonts to be used, studied, modified and
+redistributed freely as long as they are not sold by themselves. The
+fonts, including any derivative works, can be bundled, embedded,
+redistributed and/or sold with any software provided that any reserved
+names are not used by derivative works. The fonts and derivatives,
+however, cannot be released under any other type of license. The
+requirement for fonts to remain under this license does not apply
+to any document created using the fonts or their derivatives.
+
+DEFINITIONS
+"Font Software" refers to the set of files released by the Copyright
+Holder(s) under this license and clearly marked as such. This may
+include source files, build scripts and documentation.
+
+"Reserved Font Name" refers to any names specified as such after the
+copyright statement(s).
+
+"Original Version" refers to the collection of Font Software components as
+distributed by the Copyright Holder(s).
+
+"Modified Version" refers to any derivative made by adding to, deleting,
+or substituting — in part or in whole — any of the components of the
+Original Version, by changing formats or by porting the Font Software to a
+new environment.
+
+"Author" refers to any designer, engineer, programmer, technical
+writer or other person who contributed to the Font Software.
+
+PERMISSION & CONDITIONS
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of the Font Software, to use, study, copy, merge, embed, modify,
+redistribute, and sell modified and unmodified copies of the Font
+Software, subject to the following conditions:
+
+1) Neither the Font Software nor any of its individual components,
+in Original or Modified Versions, may be sold by itself.
+
+2) Original or Modified Versions of the Font Software may be bundled,
+redistributed and/or sold with any software, provided that each copy
+contains the above copyright notice and this license. These can be
+included either as stand-alone text files, human-readable headers or
+in the appropriate machine-readable metadata fields within text or
+binary files as long as those fields can be easily viewed by the user.
+
+3) No Modified Version of the Font Software may use the Reserved Font
+Name(s) unless explicit written permission is granted by the corresponding
+Copyright Holder. This restriction only applies to the primary font name as
+presented to the users.
+
+4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
+Software shall not be used to promote, endorse or advertise any
+Modified Version, except to acknowledge the contribution(s) of the
+Copyright Holder(s) and the Author(s) or with their explicit written
+permission.
+
+5) The Font Software, modified or unmodified, in part or in whole,
+must be distributed entirely under this license, and must not be
+distributed under any other license. The requirement for fonts to
+remain under this license does not apply to any document created
+using the Font Software.
+
+TERMINATION
+This license becomes null and void if any of the above conditions are
+not met.
+
+DISCLAIMER
+THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT
+OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. IN NO EVENT SHALL THE
+COPYRIGHT HOLDER BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+INCLUDING ANY GENERAL, SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL
+DAMAGES, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF THE USE OR INABILITY TO USE THE FONT SOFTWARE OR FROM
+OTHER DEALINGS IN THE FONT SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Code: MIT License (https://opensource.org/licenses/MIT)
+
+In the Font Awesome Free download, the MIT license applies to all non-font and
+non-icon files.
+
+Copyright 2022 Fonticons, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in the
+Software without restriction, including without limitation the rights to use, copy,
+modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+and to permit persons to whom the Software is furnished to do so, subject to the
+following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--------------------------------------------------------------------------------
+
+# Attribution
+
+Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font
+Awesome Free files already contain embedded comments with sufficient
+attribution, so you shouldn't need to do anything additional when using these
+files normally.
+
+We've kept attribution comments terse, so we ask that you do not actively work
+to remove them from files, especially code. They're a great way for folks to
+learn about Font Awesome.
+
+--------------------------------------------------------------------------------
+
+# Brand Icons
+
+All brand icons are trademarks of their respective owners. The use of these
+trademarks does not indicate endorsement of the trademark holder by Font
+Awesome, nor vice versa. **Please do not use brand logos for any purpose except
+to represent the company, product, or service to which they refer.**
diff --git a/0.8/_static/vendor/fontawesome/6.1.2/css/all.min.css b/0.8/_static/vendor/fontawesome/6.1.2/css/all.min.css
new file mode 100644
index 00000000..aad4b1d9
--- /dev/null
+++ b/0.8/_static/vendor/fontawesome/6.1.2/css/all.min.css
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2022 Fonticons, Inc.
+ */.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-duotone,.fa-light,.fa-regular,.fa-solid,.fa-thin,.fab,.fad,.fal,.far,.fas,.fat{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 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new file mode 100644
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new file mode 100644
index 00000000..704a3fcd
--- /dev/null
+++ b/0.8/_static/versions.json
@@ -0,0 +1,22 @@
+[
+    {
+        "name": "0.9 (devel)",
+        "version": "dev",
+        "url": "https://braindecode.org/dev/"
+    },
+    {
+        "name": "0.8 (stable)",
+        "version": "dev",
+        "url": "https://braindecode.org/stable/"
+    },
+    {
+        "name": "0.7",
+        "version": "0.7",
+        "url": "https://braindecode.org/0.7/"
+    },
+    {
+        "name": "0.6",
+        "version": "0.6",
+        "url": "https://braindecode.org/0.6/"
+    }
+]
diff --git a/0.8/_static/webpack-macros.html b/0.8/_static/webpack-macros.html
new file mode 100644
index 00000000..58db7859
--- /dev/null
+++ b/0.8/_static/webpack-macros.html
@@ -0,0 +1,30 @@
+<!--
+  AUTO-GENERATED from webpack.config.js, do **NOT** edit by hand.
+  These are re-used in layout.html
+-->
+{# Load FontAwesome icons #}
+{% macro head_pre_icons() %}
+  <link href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" />
+{% endmacro %}
+
+{% macro head_pre_assets() %}
+  <!-- Loaded before other Sphinx assets -->
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=e353d410970836974a52" rel="stylesheet" />
+{% endmacro %}
+
+{% macro head_js_preload() %}
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=e353d410970836974a52" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=e353d410970836974a52" />
+{% endmacro %}
+
+{% macro body_post() %}
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=e353d410970836974a52"></script>
+<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=e353d410970836974a52"></script>
+{% endmacro %}
\ No newline at end of file
diff --git a/0.8/api.html b/0.8/api.html
new file mode 100644
index 00000000..08c49960
--- /dev/null
+++ b/0.8/api.html
@@ -0,0 +1,1068 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>API Reference &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="_static/styles/theme.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="_static/styles/bootstrap.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="_static/styles/pydata-sphinx-theme.css?digest=e353d410970836974a52" rel="stylesheet" />
+
+  
+  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=e353d410970836974a52" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" />
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+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
+
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
+    <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
+    <link rel="stylesheet" type="text/css" href="_static/style.css?v=09706775" />
+  
+  <!-- Pre-loaded scripts that we'll load fully later -->
+  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=e353d410970836974a52" />
+<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52" />
+
+    <script src="_static/documentation_options.js?v=aad85e6b"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/design-tabs.js?v=36754332"></script>
+    <script async="async" src="https://www.googletagmanager.com/gtag/js?id=G-7Q43R82K6D"></script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'api';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        </script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="braindecode.classifier.EEGClassifier" href="generated/braindecode.classifier.EEGClassifier.html" />
+    <link rel="prev" title="Process a big data EEG resource (TUH EEG Corpus)" href="auto_examples/applied_examples/plot_tuh_eeg_corpus.html" />
+    <link rel="canonical" href="braindecode.org/index.html" />
+    
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+
+  </head>
+  
+  
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
+
+  
+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
+  <div class="search-button__wrapper">
+    <div class="search-button__overlay"></div>
+    <div class="search-button__search-container">
+<form class="bd-search d-flex align-items-center"
+      action="search.html"
+      method="get">
+  <i class="fa-solid fa-magnifying-glass"></i>
+  <input type="search"
+         class="form-control"
+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
+  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
+</form></div>
+  </div>
+  
+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
+  <label class="sidebar-toggle primary-toggle" for="__primary">
+    <span class="fa-solid fa-bars"></span>
+  </label>
+  
+  <div class="navbar-header-items__start">
+    
+      <div class="navbar-item">
+  
+
+<a class="navbar-brand logo" href="index.html">
+  
+  
+  
+  
+    
+    
+    
+    <img src="_static/braindecode_symbol.png" class="logo__image only-light" alt="Braindecode Logo"/>
+    <script>document.write(`<img src="_static/braindecode_symbol.png" class="logo__image only-dark" alt="Braindecode Logo"/>`);</script>
+  
+  
+</a></div>
+    
+  </div>
+  
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
+                        Cite
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Tutorial and Examples
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="#">
+                        API Reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
+                        Get help
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        What’s new
+                      </a>
+                    </li>
+                
+  </ul>
+</nav></div>
+      
+    </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
+        </div>
+      
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span>
+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-bs-toggle="dropdown">
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+      <span class="caret"></span>
+    </button>
+    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
+    <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+`);
+</script></div>
+      
+    </div>
+    
+  </div>
+  
+  
+    <div class="navbar-persistent--mobile">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
+    </div>
+  
+
+  
+    <label class="sidebar-toggle secondary-toggle" for="__secondary">
+      <span class="fa-solid fa-outdent"></span>
+    </label>
+  
+</div>
+
+    </nav>
+  
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      <div class="bd-sidebar-primary bd-sidebar">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
+                        Cite
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
+                        Tutorial and Examples
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="#">
+                        API Reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
+                        Get help
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
+                        What’s new
+                      </a>
+                    </li>
+                
+  </ul>
+</nav></div>
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span>
+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-bs-toggle="dropdown">
+      0.8  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </button>
+    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
+    <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.HybridNet.html">braindecode.models.HybridNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGResNet.html">braindecode.models.EEGResNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.TCN.html">braindecode.models.TCN</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.SleepStagerChambon2018.html">braindecode.models.SleepStagerChambon2018</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html">braindecode.models.SleepStagerBlanco2020</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html">braindecode.models.SleepStagerEldele2021</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.USleep.html">braindecode.models.USleep</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.TIDNet.html">braindecode.models.TIDNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.get_output_shape.html">braindecode.models.get_output_shape</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.TimeDistributed.html">braindecode.models.TimeDistributed</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.training.CroppedLoss.html">braindecode.training.CroppedLoss</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.training.TimeSeriesLoss.html">braindecode.training.TimeSeriesLoss</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.training.CroppedTrialEpochScoring.html">braindecode.training.CroppedTrialEpochScoring</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.training.predict_trials.html">braindecode.training.predict_trials</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.WindowsDataset.html">braindecode.datasets.WindowsDataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.MOABBDataset.html">braindecode.datasets.MOABBDataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.HGD.html">braindecode.datasets.HGD</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.BNCI2014001.html">braindecode.datasets.BNCI2014001</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.TUH.html">braindecode.datasets.TUH</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.TUHAbnormal.html">braindecode.datasets.TUHAbnormal</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.SleepPhysionet.html">braindecode.datasets.SleepPhysionet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.BCICompetitionIVDataset4.html">braindecode.datasets.BCICompetitionIVDataset4</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.create_from_mne_raw.html">braindecode.datasets.create_from_mne_raw</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datasets.create_from_mne_epochs.html">braindecode.datasets.create_from_mne_epochs</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_events.html">braindecode.preprocessing.create_windows_from_events</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datautil.load_concat_dataset.html">braindecode.datautil.load_concat_dataset</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.samplers.RecordingSampler.html">braindecode.samplers.RecordingSampler</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.samplers.SequenceSampler.html">braindecode.samplers.SequenceSampler</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.Transform.html">braindecode.augmentation.Transform</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.ChannelsShuffle.html">braindecode.augmentation.ChannelsShuffle</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.ChannelsDropout.html">braindecode.augmentation.ChannelsDropout</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.GaussianNoise.html">braindecode.augmentation.GaussianNoise</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.ChannelsSymmetry.html">braindecode.augmentation.ChannelsSymmetry</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.SmoothTimeMask.html">braindecode.augmentation.SmoothTimeMask</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.BandstopFilter.html">braindecode.augmentation.BandstopFilter</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.functional.gaussian_noise.html">braindecode.augmentation.functional.gaussian_noise</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.functional.bandstop_filter.html">braindecode.augmentation.functional.bandstop_filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.functional.frequency_shift.html">braindecode.augmentation.functional.frequency_shift</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.functional.sensors_rotation.html">braindecode.augmentation.functional.sensors_rotation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.augmentation.functional.mixup.html">braindecode.augmentation.functional.mixup</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.util.set_random_seeds.html">braindecode.util.set_random_seeds</a></li>
+</ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+</nav></div>
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+<div>
+  
+  <section id="api-reference">
+<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Link to this heading">#</a></h1>
+<p>This is the reference for classes (<code class="docutils literal notranslate"><span class="pre">CamelCase</span></code> names) and functions
+(<code class="docutils literal notranslate"><span class="pre">underscore_case</span></code> names) of Braindecode.</p>
+<nav class="contents local" id="contents">
+<ul class="simple">
+<li><p><a class="reference internal" href="#classifier" id="id8">Classifier</a></p></li>
+<li><p><a class="reference internal" href="#regressor" id="id9">Regressor</a></p></li>
+<li><p><a class="reference internal" href="#models" id="id10">Models</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id11">Training</a></p></li>
+<li><p><a class="reference internal" href="#datasets" id="id12">Datasets</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id13">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#data-utils" id="id14">Data Utils</a></p></li>
+<li><p><a class="reference internal" href="#samplers" id="id15">Samplers</a></p></li>
+<li><p><a class="reference internal" href="#augmentation" id="id16">Augmentation</a></p></li>
+<li><p><a class="reference internal" href="#utils" id="id17">Utils</a></p></li>
+<li><p><a class="reference internal" href="#visualization" id="id18">Visualization</a></p></li>
+</ul>
+</nav>
+<p><a class="reference internal" href="source/braindecode.html#module-braindecode" title="braindecode"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode</span></code></a>:</p>
+<span class="target" id="module-braindecode"></span><section id="classifier">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Classifier</a><a class="headerlink" href="#classifier" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.html#module-braindecode.classifier" title="braindecode.classifier"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.classifier</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.html#braindecode.classifier.EEGClassifier" title="braindecode.classifier.EEGClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a>(module, *args[, criterion, ...])</p></td>
+<td><p>Classifier that does not assume softmax activation.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="regressor">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Regressor</a><a class="headerlink" href="#regressor" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.html#module-braindecode.regressor" title="braindecode.regressor"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.regressor</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.html#braindecode.regressor.EEGRegressor" title="braindecode.regressor.EEGRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a>(module, *args[, cropped, ...])</p></td>
+<td><p>Regressor that calls loss function directly.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="models">
+<h2><a class="toc-backref" href="#id10" role="doc-backlink">Models</a><a class="headerlink" href="#models" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.models.html#module-braindecode.models.base" title="braindecode.models.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models.base</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin" title="braindecode.models.EEGModuleMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>([n_outputs, n_chans, ...])</p></td>
+<td><p>Mixin class for all EEG models in braindecode.</p></td>
+</tr>
+</tbody>
+</table>
+<p><a class="reference internal" href="source/braindecode.models.html#module-braindecode.models" title="braindecode.models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>Shallow ConvNet model from Schirrmeister et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Deep4Net</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>Deep ConvNet model from Schirrmeister et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet" title="braindecode.models.DeepSleepNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepSleepNet</span></code></a>([n_outputs, return_feats, ...])</p></td>
+<td><p>Sleep staging architecture from Supratak et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer" title="braindecode.models.EEGConformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGConformer</span></code></a>([n_outputs, n_chans, ...])</p></td>
+<td><p>EEG Conformer.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInception</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>EEG Inception for ERP-based classification</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP" title="braindecode.models.EEGInceptionERP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInceptionERP</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>EEG Inception for ERP-based classification</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI" title="braindecode.models.EEGInceptionMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>EEG Inception for Motor Imagery, as proposed in <a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#rc36ed781f4f5-1" id="id2"><span>[Rc36ed781f4f5-1]</span></a></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet" title="braindecode.models.ATCNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ATCNet</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>ATCNet model from <a class="reference internal" href="generated/braindecode.models.ATCNet.html#r2ecdb73d6ab9-1" id="id3"><span>[R2ecdb73d6ab9-1]</span></a></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet" title="braindecode.models.EEGITNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGITNet</span></code></a>([n_outputs, n_chans, n_times, ...])</p></td>
+<td><p>EEG-ITNet: An Explainable Inception Temporal</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1" title="braindecode.models.EEGNetv1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv1</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>EEGNet model from Lawhern et al. 2016.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4" title="braindecode.models.EEGNetv4"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv4</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>EEGNet v4 model from Lawhern et al 2018.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet" title="braindecode.models.HybridNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HybridNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>Hybrid ConvNet model from Schirrmeister et al 2017.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet" title="braindecode.models.EEGResNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGResNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>Residual Network for EEG.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.TCN.html#braindecode.models.TCN" title="braindecode.models.TCN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TCN</span></code></a>([n_chans, n_outputs, n_blocks, ...])</p></td>
+<td><p>Temporal Convolutional Network (TCN) from Bai et al 2018.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a>([n_chans, sfreq, ...])</p></td>
+<td><p>Sleep staging architecture from Chambon et al 2018.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020" title="braindecode.models.SleepStagerBlanco2020"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a>([n_chans, sfreq, ...])</p></td>
+<td><p>Sleep staging architecture from Blanco et al 2020.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a>([sfreq, n_tce, ...])</p></td>
+<td><p>Sleep Staging Architecture from Eldele et al 2021.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-obj docutils literal notranslate"><span class="pre">USleep</span></code></a>([n_chans, sfreq, depth, ...])</p></td>
+<td><p>Sleep staging architecture from Perslev et al 2021.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TIDNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>Thinker Invariance DenseNet model from Kostas et al 2020.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_shape</span></code></a>(model, in_chans, ...)</p></td>
+<td><p>Returns shape of neural network output for batch size equal 1.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeDistributed</span></code></a>(module)</p></td>
+<td><p>Apply module on multiple windows.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id11" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.training.html#module-braindecode.training" title="braindecode.training"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.training</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a>(loss_function)</p></td>
+<td><p>Compute Loss after averaging predictions across time.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a>(loss_function)</p></td>
+<td><p>Compute Loss between timeseries targets and predictions.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.CroppedTrialEpochScoring" title="braindecode.training.CroppedTrialEpochScoring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a>(scoring[, ...])</p></td>
+<td><p>Class to compute scores for trials from a model that predicts (super)crops.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a>(scoring[, ...])</p></td>
+<td><p>Class to compute scores for trials from a model that predicts (super)crops with time series target.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.PostEpochTrainScoring" title="braindecode.training.PostEpochTrainScoring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a>(scoring[, ...])</p></td>
+<td><p>Epoch Scoring class that recomputes predictions after the epoch on the training in validation mode.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.mixup_criterion" title="braindecode.training.mixup_criterion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mixup_criterion</span></code></a>(preds, target)</p></td>
+<td><p>Implements loss for Mixup for EEG data.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.trial_preds_from_window_preds" title="braindecode.training.trial_preds_from_window_preds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trial_preds_from_window_preds</span></code></a>(preds, ...)</p></td>
+<td><p>Assigning window predictions to trials  while removing duplicate predictions.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.training.html#braindecode.training.predict_trials" title="braindecode.training.predict_trials"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_trials</span></code></a>(module, dataset[, ...])</p></td>
+<td><p>Create trialwise predictions and optionally also return trialwise labels from cropped dataset given module.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="datasets">
+<h2><a class="toc-backref" href="#id12" role="doc-backlink">Datasets</a><a class="headerlink" href="#datasets" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.datasets.html#module-braindecode.datasets" title="braindecode.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.datasets</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseDataset</span></code></a>(raw[, description, target_name, ...])</p></td>
+<td><p>Returns samples from an mne.io.Raw object along with a target.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a>(list_of_ds[, target_transform])</p></td>
+<td><p>A base class for concatenated datasets.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.WindowsDataset" title="braindecode.datasets.WindowsDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a>(windows[, description, ...])</p></td>
+<td><p>Returns windows from an mne.Epochs object along with a target.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a>(dataset_name, subject_ids[, ...])</p></td>
+<td><p>A class for moabb datasets.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.HGD" title="braindecode.datasets.HGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HGD</span></code></a>(subject_ids)</p></td>
+<td><p>High-gamma dataset described in Schirrmeister et al. 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001" title="braindecode.datasets.BNCI2014001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a>(subject_ids)</p></td>
+<td><p>BNCI 2014-001 Motor Imagery dataset.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TUH</span></code></a>(path[, recording_ids, target_name, ...])</p></td>
+<td><p>Temple University Hospital (TUH) EEG Corpus (www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a>(path[, recording_ids, ...])</p></td>
+<td><p>Temple University Hospital (TUH) Abnormal EEG Corpus.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a>([subject_ids, recording_ids, ...])</p></td>
+<td><p>Sleep Physionet dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a>([subject_ids])</p></td>
+<td><p>BCI competition IV dataset 4.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_from_X_y</span></code></a>(X, y, drop_last_window, sfreq)</p></td>
+<td><p>Create a BaseConcatDataset of WindowsDatasets from X and y to be used for decoding with skorch and braindecode, where X is a list of pre-cut trials and y are corresponding targets.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_from_mne_raw</span></code></a>(raws, ...[, ...])</p></td>
+<td><p>Create WindowsDatasets from mne.RawArrays</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_from_mne_epochs</span></code></a>(list_of_epochs, ...)</p></td>
+<td><p>Create WindowsDatasets from mne.Epochs</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="preprocessing">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.preprocessing.html#module-braindecode.preprocessing" title="braindecode.preprocessing"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.preprocessing</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_windows_from_events</span></code></a>(concat_ds[, ...])</p></td>
+<td><p>Create windows based on events in mne.Raw.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_fixed_length_windows</span></code></a>(concat_ds[, ...])</p></td>
+<td><p>Windower that creates sliding windows.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_windows_from_target_channels</span></code></a>(concat_ds)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_demean" title="braindecode.preprocessing.exponential_moving_demean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential_moving_demean</span></code></a>(data[, ...])</p></td>
+<td><p>Perform exponential moving demeanining.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential_moving_standardize</span></code></a>(data[, ...])</p></td>
+<td><p>Perform exponential moving standardization.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.filterbank" title="braindecode.preprocessing.filterbank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filterbank</span></code></a>(raw, frequency_bands[, ...])</p></td>
+<td><p>Applies multiple bandpass filters to the signals in raw.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>(concat_ds, preprocessors[, ...])</p></td>
+<td><p>Apply preprocessors to a concat dataset.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Preprocessor</span></code></a>(fn, *[, apply_on_array])</p></td>
+<td><p>Preprocessor for an MNE Raw or Epochs object.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Resample</span></code></a>([up, down, npad, axis, window, ...])</p></td>
+<td><p>Resample an array.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropChannels</span></code></a>(ch_names[, on_missing])</p></td>
+<td><p>Drop channel(s).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a>([ref_channels, projection, ...])</p></td>
+<td><p>Specify which reference to use for EEG data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Filter</span></code></a>(l_freq, h_freq[, picks, ...])</p></td>
+<td><p>Filter a subset of channels.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Pick</span></code></a>(picks[, exclude, verbose])</p></td>
+<td><p>Pick a subset of channels.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Crop</span></code></a>([tmin, tmax, include_tmax, verbose])</p></td>
+<td><p>Crop raw data file.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="data-utils">
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Data Utils</a><a class="headerlink" href="#data-utils" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.datautil.html#module-braindecode.datautil" title="braindecode.datautil"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.datautil</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.datautil.save_concat_dataset.html#braindecode.datautil.save_concat_dataset" title="braindecode.datautil.save_concat_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_concat_dataset</span></code></a>(path, concat_dataset[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_concat_dataset</span></code></a>(path, preload[, ...])</p></td>
+<td><p>Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from files.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="samplers">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Samplers</a><a class="headerlink" href="#samplers" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.samplers.html#module-braindecode.samplers" title="braindecode.samplers"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.samplers</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler" title="braindecode.samplers.RecordingSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a>(metadata[, random_state])</p></td>
+<td><p>Base sampler simplifying sampling from recordings.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a>(metadata, n_windows, ...[, ...])</p></td>
+<td><p>Sample sequences of consecutive windows.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a>(metadata, ...[, ...])</p></td>
+<td><p>Sample examples for the relative positioning task from <a class="reference internal" href="source/braindecode.samplers.html#r0467437a2408-banville2020" id="id4"><span>[R0467437a2408-Banville2020]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler" title="braindecode.samplers.BalancedSequenceSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a>(metadata, n_windows)</p></td>
+<td><p>Balanced sampling of sequences of consecutive windows with categorical targets.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="augmentation">
+<span id="augmentation-api"></span><h2><a class="toc-backref" href="#id16" role="doc-backlink">Augmentation</a><a class="headerlink" href="#augmentation" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.augmentation.html#module-braindecode.augmentation" title="braindecode.augmentation"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.augmentation</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transform</span></code></a>([probability, random_state])</p></td>
+<td><p>Basic transform class used for implementing data augmentation operations.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.IdentityTransform" title="braindecode.augmentation.IdentityTransform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a>([probability, random_state])</p></td>
+<td><p>Identity transform.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Compose</span></code></a>(transforms)</p></td>
+<td><p>Transform composition.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a>(dataset[, transforms, ...])</p></td>
+<td><p>A base dataloader class customized to applying augmentation Transforms.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.TimeReverse" title="braindecode.augmentation.TimeReverse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeReverse</span></code></a>(probability[, random_state])</p></td>
+<td><p>Flip the time axis of each input with a given probability.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SignFlip</span></code></a>(probability[, random_state])</p></td>
+<td><p>Flip the sign axis of each input with a given probability.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a>(probability[, ...])</p></td>
+<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="source/braindecode.augmentation.html#ra7c6c14d9bd9-1" id="id5"><span>[Ra7c6c14d9bd9-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle" title="braindecode.augmentation.ChannelsShuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a>(probability[, p_shuffle, ...])</p></td>
+<td><p>Randomly shuffle channels in EEG data matrix.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a>(probability[, p_drop, ...])</p></td>
+<td><p>Randomly set channels to flat signal.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.GaussianNoise" title="braindecode.augmentation.GaussianNoise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a>(probability[, std, random_state])</p></td>
+<td><p>Randomly add white noise to all channels.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry" title="braindecode.augmentation.ChannelsSymmetry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a>(probability, ordered_ch_names)</p></td>
+<td><p>Permute EEG channels inverting left and right-side sensors.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a>(probability[, ...])</p></td>
+<td><p>Smoothly replace a randomly chosen contiguous part of all channels by zeros.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.BandstopFilter" title="braindecode.augmentation.BandstopFilter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a>(probability, sfreq[, ...])</p></td>
+<td><p>Apply a band-stop filter with desired bandwidth at a randomly selected frequency position between 0 and <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a>(probability, sfreq[, ...])</p></td>
+<td><p>Add a random shift in the frequency domain to all channels.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsRotation" title="braindecode.augmentation.SensorsRotation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a>(probability, ...[, axis, ...])</p></td>
+<td><p>Interpolates EEG signals over sensors rotated around the desired axis with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsZRotation" title="braindecode.augmentation.SensorsZRotation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a>(probability, ordered_ch_names)</p></td>
+<td><p>Interpolates EEG signals over sensors rotated around the Z axis with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsYRotation" title="braindecode.augmentation.SensorsYRotation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a>(probability, ordered_ch_names)</p></td>
+<td><p>Interpolates EEG signals over sensors rotated around the Y axis with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsXRotation" title="braindecode.augmentation.SensorsXRotation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a>(probability, ordered_ch_names)</p></td>
+<td><p>Interpolates EEG signals over sensors rotated around the X axis with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.Mixup" title="braindecode.augmentation.Mixup"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Mixup</span></code></a>(alpha[, beta_per_sample, random_state])</p></td>
+<td><p>Implements Iterator for Mixup for EEG data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.identity" title="braindecode.augmentation.functional.identity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.identity</span></code></a>(X, y)</p></td>
+<td><p>Identity operation.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.time_reverse" title="braindecode.augmentation.functional.time_reverse"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.time_reverse</span></code></a>(X, y)</p></td>
+<td><p>Flip the time axis of each input.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.sign_flip" title="braindecode.augmentation.functional.sign_flip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.sign_flip</span></code></a>(X, y)</p></td>
+<td><p>Flip the sign axis of each input.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.ft_surrogate" title="braindecode.augmentation.functional.ft_surrogate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.ft_surrogate</span></code></a>(X, y, ...[, ...])</p></td>
+<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="source/braindecode.augmentation.html#r52a4658fffa7-1" id="id6"><span>[R52a4658fffa7-1]</span></a>.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_dropout" title="braindecode.augmentation.functional.channels_dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.channels_dropout</span></code></a>(X, y, p_drop[, ...])</p></td>
+<td><p>Randomly set channels to flat signal.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_shuffle" title="braindecode.augmentation.functional.channels_shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.channels_shuffle</span></code></a>(X, y, p_shuffle)</p></td>
+<td><p>Randomly shuffle channels in EEG data matrix.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_permute" title="braindecode.augmentation.functional.channels_permute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.channels_permute</span></code></a>(X, y, permutation)</p></td>
+<td><p>Permute EEG channels according to fixed permutation matrix.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.gaussian_noise" title="braindecode.augmentation.functional.gaussian_noise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.gaussian_noise</span></code></a>(X, y, std[, ...])</p></td>
+<td><p>Randomly add white Gaussian noise to all channels.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.smooth_time_mask" title="braindecode.augmentation.functional.smooth_time_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.smooth_time_mask</span></code></a>(X, y, ...)</p></td>
+<td><p>Smoothly replace a contiguous part of all channels by zeros.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.bandstop_filter" title="braindecode.augmentation.functional.bandstop_filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.bandstop_filter</span></code></a>(X, y, sfreq, ...)</p></td>
+<td><p>Apply a band-stop filter with desired bandwidth at the desired frequency position.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.frequency_shift" title="braindecode.augmentation.functional.frequency_shift"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.frequency_shift</span></code></a>(X, y, delta_freq, ...)</p></td>
+<td><p>Adds a shift in the frequency domain to all channels.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.sensors_rotation" title="braindecode.augmentation.functional.sensors_rotation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.sensors_rotation</span></code></a>(X, y, ...)</p></td>
+<td><p>Interpolates EEG signals over sensors rotated around the desired axis with the desired angle.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.mixup" title="braindecode.augmentation.functional.mixup"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.mixup</span></code></a>(X, y, lam, idx_perm)</p></td>
+<td><p>Mixes two channels of EEG data.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="utils">
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">Utils</a><a class="headerlink" href="#utils" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.html#module-braindecode.util" title="braindecode.util"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.util</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_random_seeds</span></code></a>(seed, cuda[, cudnn_benchmark])</p></td>
+<td><p>Set seeds for python random module numpy.random and torch.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+<section id="visualization">
+<h2><a class="toc-backref" href="#id18" role="doc-backlink">Visualization</a><a class="headerlink" href="#visualization" title="Link to this heading">#</a></h2>
+<p><a class="reference internal" href="source/braindecode.visualization.html#module-braindecode.visualization" title="braindecode.visualization"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.visualization</span></code></a>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="source/braindecode.visualization.html#braindecode.visualization.compute_amplitude_gradients" title="braindecode.visualization.compute_amplitude_gradients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_amplitude_gradients</span></code></a>(model, dataset, ...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="source/braindecode.visualization.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_confusion_matrix</span></code></a>(confusion_mat[, ...])</p></td>
+<td><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="source/braindecode.visualization.html#r8046536b33dd-1" id="id7"><span>[R8046536b33dd-1]</span></a>.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
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+<p>Examples explaining more advanced topics in neural network training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
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diff --git a/stable/auto_examples/plot_bcic_iv_4_ecog_cropped.html b/0.8/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html
similarity index 59%
rename from stable/auto_examples/plot_bcic_iv_4_ecog_cropped.html
rename to 0.8/auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html
index b42490e8..4cfb3008 100644
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   <div class="sphx-glr-download-link-note admonition note">
 <p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">here</span></a>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code</p>
 </div>
 <section class="sphx-glr-example-title" id="fingers-flexion-cropped-decoding-on-bcic-iv-4-ecog-dataset">
-<span id="sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"></span><h1>Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-cropped-decoding-on-bcic-iv-4-ecog-dataset" title="Permalink to this heading">#</a></h1>
+<span id="sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"></span><h1>Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-cropped-decoding-on-bcic-iv-4-ecog-dataset" title="Link to this heading">#</a></h1>
 <p>This tutorial shows you how to train and test deep learning models with
 Braindecode on ECoG BCI IV competition dataset 4 using cropped mode. For this
 dataset we will predict 5 regression targets corresponding to flexion of each finger.
@@ -417,9 +484,9 @@
 </pre></div>
 </div>
 <section id="loading-and-preparing-the-dataset">
-<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Permalink to this heading">#</a></h2>
+<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Link to this heading">#</a></h2>
 <section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
 <p>First, we load the data. In this tutorial, we use the functionality of braindecode
 to load <a class="reference external" href="http://www.bbci.de/competition/iv/#dataset4">BCI IV competition dataset 4</a>.
 The dataset is available as a part of ECoG library:
@@ -437,26 +504,26 @@ <h3>Loading<a class="headerlink" href="#loading" title="Permalink to this headin
 <span class="kn">import</span> <span class="nn">sklearn</span>
 <span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
 
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">1</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
 </pre></div>
 </div>
 </section>
 <section id="split-dataset-into-train-and-test">
-<h3>Split dataset into train and test<a class="headerlink" href="#split-dataset-into-train-and-test" title="Permalink to this heading">#</a></h3>
+<h3>Split dataset into train and test<a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
 <p>We can easily split the dataset using additional info stored in the
 description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select <cite>train</cite> dataset
 for training and validation and <cite>test</cite> for final evaluation.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
 </pre></div>
 </div>
 </section>
 <section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
 <p>Now we apply preprocessing like bandpass filtering to our dataset. You
 can either apply functions provided by
 <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
@@ -476,8 +543,9 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 PyTorch-libraries like
 <a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
 
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">1.</span>  <span class="c1"># low cut frequency for filtering</span>
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">200.</span>  <span class="c1"># high cut frequency for filtering, for ECoG higher than for EEG</span>
@@ -489,12 +557,13 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <p>We select only first 30 seconds from the training dataset to limit time and memory
 to run this example. We split training dataset into train and validation (only 6 seconds).
 To obtain full results whole datasets should be used.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/copy.html#copy.deepcopy" title="copy.deepcopy" class="sphx-glr-backref-module-copy sphx-glr-backref-type-py-function"><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)])</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)])</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)])</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/copy.html#copy.deepcopy" title="copy.deepcopy" class="sphx-glr-backref-module-copy sphx-glr-backref-type-py-function"><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span>
+                       <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50aa969550&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42172f31f0&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -503,16 +572,16 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 (see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
     <span class="c1"># TODO: ensure that misc is not removed</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
                  <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">)</span>
 <span class="p">]</span>
 <span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
 
 <span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
@@ -521,14 +590,14 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;temp&#39;</span><span class="p">][</span><span class="s1">&#39;target_sfreq&#39;</span><span class="p">]</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 </pre></div>
 </div>
 </section>
 </section>
 <section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
 <p>In contrast to trialwise decoding, we first have to create the model
 before we can cut the dataset into windows. This is because we need to
 know the receptive field of the network to know how large the window
@@ -550,8 +619,9 @@ <h2>Create model<a class="headerlink" href="#create-model" title="Permalink to t
 to use your own model, it just has to be a normal PyTorch
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
@@ -564,48 +634,50 @@ <h2>Create model<a class="headerlink" href="#create-model" title="Permalink to t
 <span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
 <span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">1</span>
 <span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">5</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">5</span>
 
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
     <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span>
-<span class="c1"># We are removing the softmax layer to make it a regression model</span>
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">()</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.named_children" title="torch.nn.Module.named_children" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">named_children</span></a><span class="p">():</span>
-    <span class="k">if</span> <span class="s2">&quot;softmax&quot;</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">:</span>
-        <span class="k">continue</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.add_module" title="torch.nn.Module.add_module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">new_model</span><span class="o">.</span><span class="n">add_module</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span><span class="p">)</span>
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a>
 
 <span class="c1"># Send model to GPU</span>
 <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
     <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
 
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span><span class="p">,</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">,</span> <span class="n">to_dense_prediction_model</span>
 
-<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function to_dense_prediction_model is deprecated; will be removed in version 1.0. Use EEGModuleMixin.to_dense_prediction_model method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
 </pre></div>
 </div>
 <p>To know the models’ receptive field, we calculate the shape of model
 output for a dummy input.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function get_output_shape is deprecated; will be removed in version 1.0. Use EEGModuleMixin.get_output_shape method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
 </pre></div>
 </div>
 <section id="cut-compute-windows">
-<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
 
 <span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
 <span class="c1"># trials should be used.</span>
 
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span>
     <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
@@ -616,8 +688,8 @@ <h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="
     <span class="n">preload</span><span class="o">=</span><span class="kc">False</span>
 <span class="p">)</span>
 
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span>
     <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
@@ -628,8 +700,8 @@ <h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="
     <span class="n">preload</span><span class="o">=</span><span class="kc">False</span>
 <span class="p">)</span>
 
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span>
     <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
     <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
@@ -647,15 +719,15 @@ <h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="
 <p>Methods to predict all 5 fingers flexion with the same model may be considered as well.
 We encourage you to find your own way to use braindecode models to predict fingers flexions.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
 </pre></div>
 </div>
 </section>
 </section>
 <section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
 <p>In difference to trialwise decoding, we now should supply
 <code class="docutils literal notranslate"><span class="pre">cropped=True</span></code> to the EEGClassifier, and <code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code> as the
 criterion, as well as <code class="docutils literal notranslate"><span class="pre">criterion__loss_function</span></code> as the loss function
@@ -668,11 +740,10 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 cross validation on your training data.</p>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
 
-<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a>
 <span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">,</span> <a href="../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a>
 
 <span class="c1"># These values we found good for shallow network for EEG MI decoding:</span>
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
@@ -681,28 +752,28 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">8</span>
 
 <span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
     <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">aggregate_predictions</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a><span class="p">,</span>
     <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span></a><span class="p">,</span>
     <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
     <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
     <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
     <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
     <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
         <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-        <span class="p">(</span><span class="s1">&#39;r2_train&#39;</span><span class="p">,</span> <a href="../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
+        <span class="p">(</span><span class="s1">&#39;r2_train&#39;</span><span class="p">,</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
                                                    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                                   <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;r2_train&#39;</span><span class="p">)</span>
+                                                   <span class="n">name</span><span class="o">=</span><span class="s1">&#39;r2_train&#39;</span><span class="p">)</span>
          <span class="p">),</span>
-        <span class="p">(</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">,</span> <a href="../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
+        <span class="p">(</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">,</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
                                                    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                                    <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                                   <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">)</span>
+                                                   <span class="n">name</span><span class="o">=</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">)</span>
          <span class="p">)</span>
     <span class="p">],</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
@@ -712,23 +783,19 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 </div>
 <p>Model training for a specified number of epochs. <code class="docutils literal notranslate"><span class="pre">y</span></code> is None as it is already supplied
 in the dataset.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/skorch/callbacks/base.py:146: SkorchWarning: You are using an callback that overrides on_batch_begin or on_batch_end. As of skorch 0.10, the signature was changed from &#39;on_batch_{begin,end}(self, X, y, ...)&#39; to &#39;on_batch_{begin,end}(self, batch, ...)&#39;. To recover, change the signature accordingly and add &#39;X, y = batch&#39; on the first line of the method body. To suppress this warning, add:
-&#39;import warnings; from skorch.exceptions import SkorchWarning
-warnings.filterwarnings(&#39;ignore&#39;, category=SkorchWarning)&#39;.
-  warnings.warn(warning_msg, SkorchWarning)
-  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -16.9064     -8.3116        2.5378       20.7385  0.0006  1.8075
-      2    -13.7579     -7.1286        1.9596       18.0806  0.0006  1.6011
-      3    -12.7813     -6.7445        1.6787       17.1950  0.0005  1.5732
-      4    -11.8017     -6.3939        1.5638       16.4455  0.0004  1.5572
-      5    -11.2758     -6.1864        1.4237       16.0128  0.0002  1.5445
-      6    -10.5777     -5.9225        1.3549       15.4464  0.0001  1.5527
-      7     -9.8264     -5.6200        1.3132       14.7823  0.0000  1.5553
-      8     -9.0673     -5.3063        1.3316       14.0904  0.0000  1.5551
+      1    -16.9036     -8.3098        2.5378       20.7346  0.0006  0.4214
+      2    -13.7531     -7.1260        1.9596       18.0748  0.0006  0.3756
+      3    -12.7764     -6.7415        1.6787       17.1893  0.0005  0.2639
+      4    -11.7969     -6.3918        1.5638       16.4423  0.0004  0.3371
+      5    -11.2712     -6.1846        1.4237       16.0104  0.0002  0.2548
+      6    -10.5736     -5.9208        1.3549       15.4441  0.0001  0.2306
+      7     -9.8229     -5.6185        1.3132       14.7803  0.0000  0.2344
+      8     -9.0644     -5.3050        1.3316       14.0887  0.0000  0.2321
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -744,32 +811,31 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
     <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">T</span>
 
 
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
 
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a><span class="p">)</span>
 
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">)</span>
 </pre></div>
 </div>
 </section>
 <section id="plot-results">
-<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>We plot target and predicted finger flexion on training, validation, adn test sets.</p>
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We plot target and predicted finger flexion on training, validation, and test sets.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>The model is trained and validated on limited dataset (to decrease the time neded to run
+<p>The model is trained and validated on limited dataset (to decrease the time needed to run
 this example) which does not contain diverse dataset in terms of fingers flexions and may
 cause overfitting. To obtain better results use whole dataset as well as improve the decoding
 pipeline which may be not optimal for ECoG.</p>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
 
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Training dataset&#39;</span><span class="p">)</span>
@@ -795,13 +861,13 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
 <span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
         <a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
     <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.round.html#numpy.round" title="numpy.round" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">round</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 </pre></div>
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Correlation coefficient for each dimension:  [-0.1]
@@ -814,17 +880,17 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
                   <span class="n">index</span><span class="o">=</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
 
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
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@@ -839,14 +905,14 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
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-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  25.332 seconds)</p>
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+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (5 minutes 56.966 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  457 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.py</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.ipynb</span></code></a></p>
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@@ -854,137 +920,107 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
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-      <i class="fa-solid fa-angle-left"></i>
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-          <p class="prev-next-subtitle">previous</p>
-          <p class="prev-next-title">Benchmarking eager and lazy loading</p>
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-     Preprocessing
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Advanced neural network training strategies</p>
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+       href="plot_data_augmentation.html"
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preparing-the-dataset">Loading and preparing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-test">Split dataset into train and test</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-compute-windows">Cut Compute Windows</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot Results</a></li>
+</ul>
+  </nav></div>
 
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+++ b/0.8/auto_examples/advanced_training/plot_data_augmentation.html
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+<!DOCTYPE html>
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+<html lang="en" data-theme="light">
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../model_building/index.html">Basic model building and training</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Advanced neural network training strategies</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+      
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+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="data-augmentation-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"></span><h1>Data Augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#data-augmentation-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train EEG deep models with data augmentation. It
+follows the trial-wise decoding example and also illustrates the effect of a
+transform on the input signals.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id1">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id2">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id3">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-windows" id="id4">Extracting windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id5">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#defining-a-transform" id="id6">Defining a Transform</a></p>
+<ul>
+<li><p><a class="reference internal" href="#manipulating-one-session-and-visualizing-the-transformed-data" id="id7">Manipulating one session and visualizing the transformed data</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#training-a-model-with-data-augmentation" id="id8">Training a model with data augmentation</a></p>
+<ul>
+<li><p><a class="reference internal" href="#create-model" id="id9">Create model</a></p></li>
+<li><p><a class="reference internal" href="#create-an-eegclassifier-with-the-desired-augmentation" id="id10">Create an EEGClassifier with the desired augmentation</a></p></li>
+<li><p><a class="reference internal" href="#manually-composing-transforms" id="id11">Manually composing Transforms</a></p></li>
+<li><p><a class="reference internal" href="#setting-the-data-augmentation-at-the-dataset-level" id="id12">Setting the data augmentation at the Dataset level</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
+<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id2" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:56: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f1b9d8ca700&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-windows">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
+<span class="c1"># define how trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="defining-a-transform">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Defining a Transform</a><a class="headerlink" href="#defining-a-transform" title="Link to this heading">#</a></h2>
+<p>Data can be manipulated by transforms, which are callable objects. A
+transform is usually handled by a custom data loader, but can also be called
+directly on input data, as demonstrated below for illutrative purposes.</p>
+<p>First, we need to define a Transform. Here we chose the FrequencyShift, which
+randomly translates all frequencies within a given range.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a>
+
+<a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
+    <span class="n">probability</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>  <span class="c1"># defines the probability of actually modifying the input</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<section id="manipulating-one-session-and-visualizing-the-transformed-data">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Manipulating one session and visualizing the transformed data</a><a class="headerlink" href="#manipulating-one-session-and-visualizing-the-transformed-data" title="Link to this heading">#</a></h3>
+<p>Next, let us augment one session to show the resulting frequency shift. The
+data of an mne Epoch is used here to make usage of mne functions.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+<span class="c1"># This allows to apply the transform with a fixed shift (10 Hz) for</span>
+<span class="c1"># visualization instead of sampling the shift randomly between -2 and 2 Hz</span>
+<a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a><span class="o">.</span><span class="n">operation</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="kc">None</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>The psd of the transformed session has now been shifted by 10 Hz, as one can
+see on the psd plot.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+
+<span class="k">def</span> <span class="nf">plot_psd</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="p">):</span>
+    <span class="n">psds</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.time_frequency.psd_array_multitaper.html#mne.time_frequency.psd_array_multitaper" title="mne.time_frequency.psd_array_multitaper" class="sphx-glr-backref-module-mne-time_frequency sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">time_frequency</span><span class="o">.</span><span class="n">psd_array_multitaper</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+                                                          <span class="n">fmin</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">psds</span> <span class="o">=</span> <span class="mf">10.</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">log10</span></a><span class="p">(</span><span class="n">psds</span><span class="p">)</span>
+    <span class="n">psds_mean</span> <span class="o">=</span> <span class="n">psds</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freqs</span><span class="p">,</span> <span class="n">psds_mean</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">plot_psd</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">plot_psd</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="s1">&#39;shifted&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Multitaper PSD (gradiometers)&#39;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">,</span>
+       <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;Power Spectral Density (dB)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_data_augmentation_001.png" srcset="../../_images/sphx_glr_plot_data_augmentation_001.png" alt="Multitaper PSD (gradiometers)" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using multitaper spectrum estimation with 7 DPSS windows
+Using multitaper spectrum estimation with 7 DPSS windows
+</pre></div>
+</div>
+</section>
+</section>
+<section id="training-a-model-with-data-augmentation">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Training a model with data augmentation</a><a class="headerlink" href="#training-a-model-with-data-augmentation" title="Link to this heading">#</a></h2>
+<p>Now that we know how to instantiate <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
+to use them to train a model and try to improve its generalization power.
+Let’s first create a model.</p>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>The model to be trained is defined as usual.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+</pre></div>
+</div>
+</section>
+<section id="create-an-eegclassifier-with-the-desired-augmentation">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Create an EEGClassifier with the desired augmentation</a><a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Link to this heading">#</a></h3>
+<p>In order to train with data augmentation, a custom data loader can be
+for the training. Multiple transforms can be passed to it and will be applied
+sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a>
+
+<a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
+    <span class="n">probability</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">.1</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a>
+<span class="p">]</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>The model is now trained as in the trial-wise example. The
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
+transforms are passed as arguments.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">iterator_train</span><span class="o">=</span><a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
+    <span class="n">iterator_train__transforms</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>  <span class="c1"># This sets the augmentations to use</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2500        1.6077       0.2500            0.2500        3.0178  0.0006  1.4285
+      2            0.2569        1.3809       0.2569            0.2569        2.1896  0.0005  1.3198
+      3            0.3021        1.2767       0.2569            0.2569        1.7959  0.0002  1.5167
+      4            0.3229        1.1763       0.2674            0.2674        1.6416  0.0000  1.2974
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)
+</pre></div>
+</div>
+</section>
+<section id="manually-composing-transforms">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Manually composing Transforms</a><a class="headerlink" href="#manually-composing-transforms" title="Link to this heading">#</a></h3>
+<p>It would be equivalent (although more verbose) to pass to <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code> a
+composition of the same transforms:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a>
+
+<a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="setting-the-data-augmentation-at-the-dataset-level">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Setting the data augmentation at the Dataset level</a><a class="headerlink" href="#setting-the-data-augmentation-at-the-dataset-level" title="Link to this heading">#</a></h3>
+<p>Also note that it is also possible for most of the transforms to pass them
+directly to the WindowsDataset object through the <cite>transform</cite> argument, as
+most commonly done in other libraries. However, it is advised to use the
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> as above, as it is compatible with all transforms and
+can be more efficient.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 15.127 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  177 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation.ipynb</span></code></a></p>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#defining-a-transform">Defining a Transform</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#manipulating-one-session-and-visualizing-the-transformed-data">Manipulating one session and visualizing the transformed data</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-with-data-augmentation">Training a model with data augmentation</a><ul class="nav section-nav flex-column">
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-an-eegclassifier-with-the-desired-augmentation">Create an EEGClassifier with the desired augmentation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#manually-composing-transforms">Manually composing Transforms</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#setting-the-data-augmentation-at-the-dataset-level">Setting the data augmentation at the Dataset level</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../model_building/index.html">Basic model building and training</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Advanced neural network training strategies</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+  </ul>
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+      
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+  
+  
+</div>
+</div>
+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="searching-the-best-data-augmentation-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"></span><h1>Searching the best data augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#searching-the-best-data-augmentation-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to search data augmentations using braindecode.
+Indeed, it is known that the best augmentation to use often dependent on the task
+or phenomenon studied. Here we follow the methodology proposed in <a class="footnote-reference brackets" href="#id6" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> on the
+openly available BCI IV 2a Dataset.</p>
+<aside class="topic">
+<p class="topic-title">Data Augmentation</p>
+<p>Data augmentation could be a step in training deep learning models.
+For decoding brain signals, recent studies have shown that artificially
+generating samples may increase the final performance of a deep learning model <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+Other studies have shown that data augmentation can be used to cast
+a self-supervised paradigm, presenting a more diverse
+view of the data, both with pretext tasks and contrastive learning <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+</aside>
+<p>Data augmentation and self-supervised learning approaches demand an intense comparison
+to find the best fit with the data. This view is demonstrated in <a class="footnote-reference brackets" href="#id6" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and shows the
+importance of selecting the right transformation and strength for different type of
+task considered. Here, we use the augmentation module present in braindecode in
+the context of trialwise decoding with the BCI IV 2a dataset.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id8">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id9">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id10">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-windows" id="id11">Extracting windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id12">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#defining-a-list-of-transforms" id="id13">Defining a list of transforms</a></p></li>
+<li><p><a class="reference internal" href="#training-a-model-with-data-augmentation" id="id14">Training a model with data augmentation</a></p>
+<ul>
+<li><p><a class="reference internal" href="#create-model" id="id15">Create model</a></p></li>
+<li><p><a class="reference internal" href="#create-an-eegclassifier-with-the-desired-augmentation" id="id16">Create an EEGClassifier with the desired augmentation</a></p></li>
+<li><p><a class="reference internal" href="#analysing-the-best-fit" id="id17">Analysing the best fit</a></p></li>
+<li><p><a class="reference internal" href="#plot-results" id="id18">Plot results</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id19">References</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Bruno Aristimunha &lt;a.bruno@ufabc.edu.br&gt;</span>
+<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of braindecode
+to load BCI IV competition dataset 1. The dataset is available on the BNCI website.
+There is 9 subjects recorded with 22 electrodes while doing a motor imagery task,
+with 144 trials per class. We will load this dataset through the MOABB library.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>We apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related
+brain activity</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+</pre></div>
+</div>
+<p>In time series targets setup, targets variables are stored in mne.Raw object as channels
+of type <cite>misc</cite>. Thus those channels have to be selected for further processing. However,
+many mne functions ignore <cite>misc</cite> channels and perform operations only on data channels
+(see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4215eafd60&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-windows">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
+<p>Now we cut out compute windows, the inputs for the deep networks during
+training. We use the braindecode function for this, provinding parameters
+to define how trials should be used.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>Following the split defined in the BCI competition</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="defining-a-list-of-transforms">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Defining a list of transforms</a><a class="headerlink" href="#defining-a-list-of-transforms" title="Link to this heading">#</a></h2>
+<p>In this tutorial, we will use three categories of augmentations.
+This categorization has been proposed by <a class="footnote-reference brackets" href="#id6" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to explain and aggregate
+the several possibilities of augmentations in EEG, being them:</p>
+<ol class="loweralpha simple">
+<li><p>Frequency domain augmentations,</p></li>
+<li><p>Time domain augmentations,</p></li>
+<li><p>Spatial domain augmentations.</p></li>
+</ol>
+<p>From this same paper, we selected the best augmentations in each type: <code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code>,
+<code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code>, <code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code>, respectively.</p>
+<p>For each augmentation, we adjustable two values from a range for one parameter
+inside the transformation.</p>
+<p>It is important to remember that you can increase the range.
+For that, we need to define three lists of transformations and range for the parameter
+∆φmax in FTSurrogate where ∆φmax ∈ [0, 2π); for ∆t in SmoothTimeMask is ∆t ∈ [0, 2];
+For the method ChannelsDropout, we analyse the parameter p_drop ∈ [0, 1].</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">phase_noise_magnitude</span><span class="o">=</span><span class="n">phase_freq</span><span class="p">,</span>
+                               <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">phase_freq</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">mask_len_samples</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="n">second</span><span class="p">),</span>
+                                  <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">second</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p_drop</span><span class="o">=</span><span class="n">prob</span><span class="p">,</span>
+                                      <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">prob</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+</pre></div>
+</div>
+</section>
+<section id="training-a-model-with-data-augmentation">
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Training a model with data augmentation</a><a class="headerlink" href="#training-a-model-with-data-augmentation" title="Link to this heading">#</a></h2>
+<p>Now that we know how to instantiate three list of <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
+to use them to train a model and try to search the best for the dataset.
+Let’s first create a model for search a parameter.</p>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id15" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>The model to be trained is defined as usual.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+<p>Set random seed to be able to roughly reproduce results
+Note that with cudnn benchmark set to True, GPU indeterminism
+may still make results substantially different between runs.
+To obtain more consistent results at the cost of increased computation time,
+you can set <code class="docutils literal notranslate"><span class="pre">cudnn_benchmark=False</span></code> in <code class="docutils literal notranslate"><span class="pre">set_random_seeds</span></code>
+or remove <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.benchmark</span> <span class="pre">=</span> <span class="pre">True</span></code></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+</pre></div>
+</div>
+</section>
+<section id="create-an-eegclassifier-with-the-desired-augmentation">
+<h3><a class="toc-backref" href="#id16" role="doc-backlink">Create an EEGClassifier with the desired augmentation</a><a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Link to this heading">#</a></h3>
+<p>In order to train with data augmentation, a custom data loader can be
+for the training. Multiple transforms can be passed to it and will be applied
+sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>The model is now trained as in the trial-wise example. The
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
+transforms are passed as arguments.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">iterator_train</span><span class="o">=</span><a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
+    <span class="n">iterator_train__transforms</span><span class="o">=</span><span class="p">[],</span>  <span class="c1"># This sets is handled by GridSearchCV</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># GridSearchCV will control the split and train/validation over the dataset</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s1">&#39;lr_scheduler&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>To use the skorch framework, it is necessary to transform the windows
+dataset using the module SliceData. Also, it is mandatory to eval the
+generator of the training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">(</span><span class="nb">list</span><span class="p">(</span><a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
+</pre></div>
+</div>
+<p>Given the trialwise approach, here we use the KFold approach and
+GridSearchCV.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;iterator_train__transforms&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">verbose</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 6 candidates, totalling 12 fits
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=20200220, shuffle=True),
+             error_score=&#x27;raise&#x27;,
+             estimator=&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Ou...
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+),
+             param_grid={&#x27;iterator_train__transforms&#x27;: [FTSurrogate(),
+                                                        FTSurrogate(),
+                                                        SmoothTimeMask(),
+                                                        SmoothTimeMask(),
+                                                        ChannelsDropout(),
+                                                        ChannelsDropout()]},
+             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">GridSearchCV</label><div class="sk-toggleable__content"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=20200220, shuffle=True),
+             error_score=&#x27;raise&#x27;,
+             estimator=&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Ou...
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+),
+             param_grid={&#x27;iterator_train__transforms&#x27;: [FTSurrogate(),
+                                                        FTSurrogate(),
+                                                        SmoothTimeMask(),
+                                                        SmoothTimeMask(),
+                                                        ChannelsDropout(),
+                                                        ChannelsDropout()]},
+             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: EEGClassifier</label><div class="sk-toggleable__content"><pre>&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">EEGClassifier</label><div class="sk-toggleable__content"><pre>&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)</pre></div></div></div></div></div></div></div></div></div></div>
+</div>
+<br />
+<br /></section>
+<section id="analysing-the-best-fit">
+<h3><a class="toc-backref" href="#id17" role="doc-backlink">Analysing the best fit</a><a class="headerlink" href="#analysing-the-best-fit" title="Link to this heading">#</a></h3>
+<p>Next, just perform an analysis of the best fit, and the parameters,
+remembering the order that was adjusted.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+
+<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">best_aug</span></a> <span class="o">=</span> <span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a> <span class="o">=</span> <span class="s1">&#39;Best augmentation is saved in best_aug which gave a mean validation accuracy&#39;</span> <span class="o">+</span> \
+                 <span class="s1">&#39;of </span><span class="si">{}</span><span class="s1">% (train accuracy of </span><span class="si">{}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a><span class="p">)</span>
+
+<span class="n">eval_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">eval_y</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><span class="n">eval_X</span><span class="p">,</span> <span class="n">eval_y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">%.&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best augmentation is saved in best_aug which gave a mean validation accuracyof 27.78% (train accuracy of 28.47%).
+Eval accuracy is 25.00%.
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h3><a class="toc-backref" href="#id18" role="doc-backlink">Plot results</a><a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">search_results</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;param_iterator_train__transforms&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;mean_train_score&quot;</span><span class="p">,</span> <span class="n">yerr</span><span class="o">=</span><span class="s2">&quot;std_train_score&quot;</span><span class="p">,</span>
+    <span class="n">rot</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;C0&quot;</span><span class="p">,</span> <span class="s2">&quot;C0&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Data augmentation strategy&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.32</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_data_augmentation_search_001.png" srcset="../../_images/sphx_glr_plot_data_augmentation_search_001.png" alt="plot data augmentation search" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h3><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h3>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id4">3</a>,<a role="doc-backlink" href="#id5">4</a>)</span>
+<p>Rommel, C., Paillard, J., Moreau, T., &amp; Gramfort, A. (2022)
+Data augmentation for learning predictive models on EEG:
+a systematic comparison. <a class="reference external" href="https://arxiv.org/abs/2206.14483">https://arxiv.org/abs/2206.14483</a></p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A. (2021).
+Uncovering the structure of clinical EEG signals with self-supervised learning.
+Journal of Neural Engineering, 18(4), 046020.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 40.536 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  538 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-search-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation_search.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation_search.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+        <p class="prev-next-title">Data Augmentation on BCIC IV 2a Dataset</p>
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+        <p class="prev-next-title">Self-supervised learning on EEG with relative positioning</p>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-windows">Extracting windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#defining-a-list-of-transforms">Defining a list of transforms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-with-data-augmentation">Training a model with data augmentation</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-an-eegclassifier-with-the-desired-augmentation">Create an EEGClassifier with the desired augmentation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#analysing-the-best-fit">Analysing the best fit</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot results</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
+</ul>
+</li>
+</ul>
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diff --git a/stable/auto_examples/plot_relative_positioning.html b/0.8/auto_examples/advanced_training/plot_relative_positioning.html
similarity index 67%
rename from stable/auto_examples/plot_relative_positioning.html
rename to 0.8/auto_examples/advanced_training/plot_relative_positioning.html
index 8b2b0ae9..fb2bac62 100644
--- a/stable/auto_examples/plot_relative_positioning.html
+++ b/0.8/auto_examples/advanced_training/plot_relative_positioning.html
@@ -1,420 +1,487 @@
 
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-    <title>Self-supervised learning on EEG with relative positioning &#8212; Braindecode 0.7 documentation</title>
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+    <title>Self-supervised learning on EEG with relative positioning &#8212; Braindecode 0.8 documentation</title>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
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   <div class="sphx-glr-download-link-note admonition note">
 <p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-relative-positioning-py"><span class="std std-ref">here</span></a>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code</p>
 </div>
 <section class="sphx-glr-example-title" id="self-supervised-learning-on-eeg-with-relative-positioning">
-<span id="sphx-glr-auto-examples-plot-relative-positioning-py"></span><h1>Self-supervised learning on EEG with relative positioning<a class="headerlink" href="#self-supervised-learning-on-eeg-with-relative-positioning" title="Permalink to this heading">#</a></h1>
+<span id="sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"></span><h1>Self-supervised learning on EEG with relative positioning<a class="headerlink" href="#self-supervised-learning-on-eeg-with-relative-positioning" title="Link to this heading">#</a></h1>
 <p>This example shows how to train a neural network with self-supervision on sleep
-EEG data. We follow the relative positioning approach of <a class="footnote-reference brackets" href="#id15" id="id1">1</a> on the openly
-accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id16" id="id2">2</a> <a class="footnote-reference brackets" href="#id17" id="id3">3</a>.</p>
-<div class="topic">
+EEG data. We follow the relative positioning approach of <a class="footnote-reference brackets" href="#id15" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> on the openly
+accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id16" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id17" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<aside class="topic">
 <p class="topic-title">Self-supervised learning</p>
 <p>Self-supervised learning (SSL) is a learning paradigm that leverages
 unlabelled data to train neural networks. First, neural networks are
 trained on a “pretext task” which uses unlabelled data only. The pretext
 task is designed based on a prior understanding of the data under study
-(e.g., EEG has an underlying autocorrelation struture) and such that the
+(e.g., EEG has an underlying autocorrelation structure) and such that the
 processing required to perform well on this pretext task is related to the
 processing required to perform well on another task of interest.
 Once trained, these neural networks can be reused as feature extractors or
@@ -422,15 +489,15 @@
 actually interested in (e.g., sleep staging). The pretext task step can
 help reduce the quantity of labelled data needed to perform well on the
 downstream task and/or improve downstream performance as compared to a
-strictly supervised approach <a class="footnote-reference brackets" href="#id15" id="id4">1</a>.</p>
-</div>
+strictly supervised approach <a class="footnote-reference brackets" href="#id15" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+</aside>
 <p>Here, we use relative positioning (RP) as our pretext task, and perform sleep
 staging as our downstream task. RP is a simple SSL task, in which a neural
 network is trained to predict whether two randomly sampled EEG windows are
 close or far apart in time. This method was shown to yield physiologically- and
 clinically-relevant features and to boost classification performance in
-low-labels data regimes <a class="footnote-reference brackets" href="#id15" id="id5">1</a>.</p>
-<div class="contents local topic" id="this-example-covers">
+low-labels data regimes <a class="footnote-reference brackets" href="#id15" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+<nav class="contents local" id="this-example-covers">
 <p class="topic-title">This example covers:</p>
 <ul class="simple">
 <li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id19">Loading and preprocessing the dataset</a></p>
@@ -454,7 +521,7 @@
 <li><p><a class="reference internal" href="#conclusion" id="id31">Conclusion</a></p></li>
 <li><p><a class="reference internal" href="#references" id="id32">References</a></p></li>
 </ul>
-</div>
+</nav>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
 <span class="c1">#</span>
 <span class="c1"># License: BSD (3-clause)</span>
@@ -465,25 +532,25 @@
 </pre></div>
 </div>
 <section id="loading-and-preprocessing-the-dataset">
-<h2><a class="toc-backref" href="#id19">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id19" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
 <section id="loading-the-raw-recordings">
-<h3><a class="toc-backref" href="#id20">Loading the raw recordings</a><a class="headerlink" href="#loading-the-raw-recordings" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id20" role="doc-backlink">Loading the raw recordings</a><a class="headerlink" href="#loading-the-raw-recordings" title="Link to this heading">#</a></h3>
 <p>First, we load a few recordings from the Sleep Physionet dataset. Running
 this example with more recordings should yield better representations and
 downstream classification performance.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.sleep_physionet</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.sleep_physionet</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
 
-<a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
     <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
 </pre></div>
 </div>
 </section>
 <section id="preprocessing">
-<h3><a class="toc-backref" href="#id21">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id21" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
 <p>Next, we preprocess the raw data. We convert the data to microvolts and apply
 a lowpass filter. Since the Sleep Physionet data is already sampled at 100 Hz
 we don’t need to apply resampling.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
 <span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
@@ -491,30 +558,30 @@ <h3><a class="toc-backref" href="#id21">Preprocessing</a><a class="headerlink" h
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
 
 <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="p">)</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="p">)</span>
 <span class="p">]</span>
 
 <span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 
-&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f50b2580a10&gt;
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f41ed644dc0&gt;
 </pre></div>
 </div>
 </section>
 <section id="extracting-windows">
-<h3><a class="toc-backref" href="#id22">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id22" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
 <p>We extract 30-s windows to be used in both the pretext and downstream tasks.
 As RP (and SSL in general) don’t require labelled data, the pretext task
 could be performed using unlabelled windows extracted with
 <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.windower.create_fixed_length_window()</span></code>.
 Here however, purely for convenience, we directly extract labelled windows so
 that we can reuse them in the sleep staging downstream task later.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
@@ -529,45 +596,46 @@ <h3><a class="toc-backref" href="#id22">Extracting windows</a><a class="headerli
     <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
 <span class="p">}</span>
 
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
     <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="p">)</span>
 </pre></div>
 </div>
 </section>
 <section id="preprocessing-windows">
-<h3><a class="toc-backref" href="#id23">Preprocessing windows</a><a class="headerlink" href="#preprocessing-windows" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id23" role="doc-backlink">Preprocessing windows</a><a class="headerlink" href="#preprocessing-windows" title="Link to this heading">#</a></h3>
 <p>We also preprocess the windows by applying channel-wise z-score normalization.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
 
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50abf4b750&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41ed60ed30&gt;
 </pre></div>
 </div>
 </section>
 <section id="splitting-dataset-into-train-valid-and-test-sets">
-<h3><a class="toc-backref" href="#id24">Splitting dataset into train, valid and test sets</a><a class="headerlink" href="#splitting-dataset-into-train-valid-and-test-sets" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id24" role="doc-backlink">Splitting dataset into train, valid and test sets</a><a class="headerlink" href="#splitting-dataset-into-train-valid-and-test-sets" title="Link to this heading">#</a></h3>
 <p>We randomly split the recordings by subject into train, validation and
 testing sets. We further define a new Dataset class which can receive a pair
 of indices and return the corresponding windows. This will be needed when
 training and evaluating on the pretext task.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a>
 
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">])</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
     <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
     <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
 
 
-<span class="k">class</span> <span class="nc">RelativePositioningDataset</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;BaseConcatDataset with __getitem__ that expects 2 indices and a target.</span>
+<span class="k">class</span> <span class="nc">RelativePositioningDataset</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;BaseConcatDataset with __getitem__ that expects 2 indices and a target.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">list_of_ds</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">list_of_ds</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">return_pair</span> <span class="o">=</span> <span class="kc">True</span>
@@ -592,14 +660,14 @@ <h3><a class="toc-backref" href="#id24">Splitting dataset into train, valid and
 <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_train</span></a><span class="p">,</span> <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_valid</span></a><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a><span class="p">}</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
 <span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">RelativePositioningDataset</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">RelativePositioningDataset</span></a><span class="p">(</span>
         <span class="p">[</span><span class="n">ds</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a>
          <span class="k">if</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">]</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a><span class="p">])</span>
 </pre></div>
 </div>
 </section>
 <section id="creating-samplers">
-<h3><a class="toc-backref" href="#id25">Creating samplers</a><a class="headerlink" href="#creating-samplers" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id25" role="doc-backlink">Creating samplers</a><a class="headerlink" href="#creating-samplers" title="Link to this heading">#</a></h3>
 <p>Next, we need to create samplers. These samplers will be used to randomly
 sample pairs of examples to train and validate our model with
 self-supervision.</p>
@@ -608,26 +676,26 @@ <h3><a class="toc-backref" href="#id25">Creating samplers</a><a class="headerlin
 Pairs of windows that are separated by less than <cite>tau_pos</cite> samples will be
 given a label of <cite>1</cite>, while pairs of windows that are separated by more than
 <cite>tau_neg</cite> samples will be given a label of <cite>0</cite>. Here, we use the same values
-as in <a class="footnote-reference brackets" href="#id15" id="id6">1</a>, i.e., <a href="#id7"><span class="problematic" id="id8">`</span></a>tau_pos`= 1 min and <a href="#id9"><span class="problematic" id="id10">`</span></a>tau_neg`= 15 mins.</p>
+as in <a class="footnote-reference brackets" href="#id15" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, i.e., <a href="#id7"><span class="problematic" id="id8">`</span></a>tau_pos`= 1 min and <a href="#id9"><span class="problematic" id="id10">`</span></a>tau_neg`= 15 mins.</p>
 <p>The samplers also control the number of pairs to be sampled (defined with
 <cite>n_examples</cite>). This number can be large to help regularize the pretext task
-training, for instance 2,000 pairs per recording as in <a class="footnote-reference brackets" href="#id15" id="id11">1</a>. Here, we use a
+training, for instance 2,000 pairs per recording as in <a class="footnote-reference brackets" href="#id15" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>. Here, we use a
 lower number of 250 pairs per recording to reduce training time.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="mi">60</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="mi">15</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_train</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_valid</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_test</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
 
-<a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
     <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_train</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
-<a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
     <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_valid</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span><span class="o">.</span><span class="n">presample</span><span class="p">()</span>
-<a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
     <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_test</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span><span class="o">.</span><span class="n">presample</span><span class="p">()</span>
@@ -636,9 +704,9 @@ <h3><a class="toc-backref" href="#id25">Creating samplers</a><a class="headerlin
 </section>
 </section>
 <section id="creating-the-model">
-<h2><a class="toc-backref" href="#id26">Creating the model</a><a class="headerlink" href="#creating-the-model" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id26" role="doc-backlink">Creating the model</a><a class="headerlink" href="#creating-the-model" title="Link to this heading">#</a></h2>
 <p>We can now create the deep learning model. In this tutorial, we use a
-modified version of the sleep staging architecture introduced in <a class="footnote-reference brackets" href="#id18" id="id12">4</a> -
+modified version of the sleep staging architecture introduced in <a class="footnote-reference brackets" href="#id18" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a> -
 a four-layer convolutional neural network - as our embedder.
 We change the dimensionality of the last layer to obtain a 100-dimension
 embedding, use 16 convolutional channels instead of 8, and add batch
@@ -648,8 +716,8 @@ <h2><a class="toc-backref" href="#id26">Creating the model</a><a class="headerli
 feature extractor end-to-end.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a>
 
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
 <span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">:</span>
@@ -660,25 +728,26 @@ <h2><a class="toc-backref" href="#id26">Creating the model</a><a class="headerli
 <span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
 <span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
 <span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
 
 <span class="c1"># Extract number of channels and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
 
-<a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">n_classes</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span>
     <span class="n">n_conv_chs</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
-    <span class="n">input_size_s</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
     <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">apply_batch_norm</span><span class="o">=</span><span class="kc">True</span>
+    <span class="n">apply_batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">ContrastiveNet</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Contrastive module with linear layer on top of siamese embedder.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Contrastive module with linear layer on top of siamese embedder.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -689,9 +758,10 @@ <h2><a class="toc-backref" href="#id26">Creating the model</a><a class="headerli
 <span class="sd">    dropout : float</span>
 <span class="sd">        Dropout rate applied to the linear layer of the contrastive module.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a>
+        <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a>
         <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
             <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="n">dropout</span><span class="p">),</span>
             <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -699,23 +769,23 @@ <h2><a class="toc-backref" href="#id26">Creating the model</a><a class="headerli
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
         <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">x</span>
-        <span class="n">z1</span><span class="p">,</span> <span class="n">z2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x2</span><span class="p">)</span>
+        <span class="n">z1</span><span class="p">,</span> <span class="n">z2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x2</span><span class="p">)</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clf</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.abs.html#torch.abs" title="torch.abs" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">abs</span></a><span class="p">(</span><span class="n">z1</span> <span class="o">-</span> <span class="n">z2</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
 
 
-<span class="n">model</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">ContrastiveNet</span></a><span class="p">(</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">ContrastiveNet</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
 </pre></div>
 </div>
 </section>
 <section id="training">
-<h2><a class="toc-backref" href="#id27">Training</a><a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id27" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
 <p>We can now train our network on the pretext task. We use similar
-hyperparameters as in <a class="footnote-reference brackets" href="#id15" id="id13">1</a>, but reduce the number of epochs and
+hyperparameters as in <a class="footnote-reference brackets" href="#id15" id="id13" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, but reduce the number of epochs and
 increase the learning rate to account for the smaller setting of
 this example.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
 <span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">Checkpoint</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
 <span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
 
@@ -728,14 +798,11 @@ <h2><a class="toc-backref" href="#id27">Training</a><a class="headerlink" href="
 <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">early_stopping</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">(</span><span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
 <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
     <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;train_acc&#39;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;valid_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
 <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
     <span class="p">(</span><span class="s1">&#39;cp&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cp</span></a><span class="p">),</span>
     <span class="p">(</span><span class="s1">&#39;patience&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">early_stopping</span></a><span class="p">),</span>
     <span class="p">(</span><span class="s1">&#39;train_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_acc</span></a><span class="p">),</span>
-    <span class="p">(</span><span class="s1">&#39;valid_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_acc</span></a><span class="p">)</span>
 <span class="p">]</span>
 
 <span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
@@ -744,15 +811,16 @@ <h2><a class="toc-backref" href="#id27">Training</a><a class="headerlink" href="
     <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
     <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
     <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
-    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
     <span class="n">iterator_train__num_workers</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">,</span>
     <span class="n">iterator_valid__num_workers</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]),</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]),</span>
     <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
 <span class="p">)</span>
 <span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
 <span class="c1"># supplied in the dataset.</span>
@@ -764,38 +832,38 @@ <h2><a class="toc-backref" href="#id27">Training</a><a class="headerlink" href="
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
 -------  -----------  ------------  -----------  ------------  ----  ------
-      1       0.5547        0.7113       0.6000        0.6843     +  2.0523
-      2       0.6328        0.6624       0.5600        0.6505     +  1.5413
-      3       0.4922        0.8274       0.5560        0.6834        1.5482
-      4       0.5078        0.7217       0.5360        0.7066        1.5412
-      5       0.5859        0.6870       0.6240        0.6799        1.5659
-      6       0.6016        0.6796       0.6640        0.6374     +  1.5231
-      7       0.6016        0.6640       0.6560        0.6086     +  1.5416
-      8       0.5312        0.6652       0.6600        0.5995     +  1.5450
-      9       0.6250        0.6280       0.6480        0.5983     +  1.5249
-     10       0.6250        0.6371       0.6520        0.5872     +  1.5136
-     11       0.6016        0.6539       0.6720        0.5716     +  1.5214
-     12       0.6641        0.6481       0.6840        0.5585     +  1.5315
-     13       0.6172        0.6784       0.7080        0.5460     +  1.5540
-     14       0.6250        0.6074       0.7200        0.5350     +  1.5207
-     15       0.7109        0.6056       0.7320        0.5279     +  1.5138
-     16       0.7031        0.5857       0.7360        0.5259     +  1.5173
-     17       0.6250        0.6123       0.7440        0.5328        1.5113
-     18       0.6562        0.5821       0.7360        0.5331        1.5174
-     19       0.7344        0.5464       0.7400        0.5277        1.5137
-     20       0.6484        0.6304       0.7320        0.5234     +  1.5152
-     21       0.6953        0.5459       0.7400        0.5264        1.5065
-     22       0.7422        0.5492       0.7280        0.5297        1.5089
-     23       0.6562        0.5958       0.7160        0.5296        1.5068
-     24       0.7188        0.6367       0.7240        0.5252        1.5116
-     25       0.6875        0.5745       0.7200        0.5237        1.5056
+      1       0.5234        0.7013       0.6680        0.6320     +  1.1046
+      2       0.5938        0.7149       0.4880        0.8357        1.0525
+      3       0.4922        1.0039       0.6440        0.6172     +  1.0989
+      4       0.5234        0.7031       0.6120        0.5988     +  1.0352
+      5       0.5391        0.6751       0.5920        0.6210        0.9886
+      6       0.6719        0.6235       0.5880        0.6256        1.0089
+      7       0.6641        0.6316       0.6200        0.6111        0.9016
+      8       0.6562        0.6259       0.6480        0.5937     +  1.0954
+      9       0.6484        0.6236       0.6760        0.5781     +  0.9830
+     10       0.6562        0.6161       0.6880        0.5743     +  0.9600
+     11       0.6172        0.6321       0.7000        0.5791        0.8570
+     12       0.6172        0.6263       0.6800        0.5778        0.7662
+     13       0.6406        0.6410       0.6840        0.5792        0.8368
+     14       0.6484        0.5919       0.6800        0.5759        0.9937
+     15       0.6562        0.6165       0.6760        0.5720     +  0.8047
+     16       0.7344        0.5656       0.6880        0.5671     +  0.9073
+     17       0.7031        0.5917       0.7080        0.5611     +  0.8611
+     18       0.7578        0.5435       0.7120        0.5515     +  0.9327
+     19       0.7734        0.5269       0.7040        0.5421     +  0.7624
+     20       0.6719        0.5663       0.7160        0.5361     +  0.9674
+     21       0.7109        0.5336       0.7240        0.5309     +  0.7252
+     22       0.7266        0.5471       0.7280        0.5269     +  0.8019
+     23       0.5938        0.6325       0.7240        0.5237     +  0.8693
+     24       0.6953        0.5642       0.7240        0.5200     +  0.7766
+     25       0.7266        0.5010       0.7240        0.5185     +  0.8029
 </pre></div>
 </div>
 </section>
 <section id="visualizing-the-results">
-<h2><a class="toc-backref" href="#id28">Visualizing the results</a><a class="headerlink" href="#visualizing-the-results" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id28" role="doc-backlink">Visualizing the results</a><a class="headerlink" href="#visualizing-the-results" title="Link to this heading">#</a></h2>
 <section id="inspecting-pretext-task-performance">
-<h3><a class="toc-backref" href="#id29">Inspecting pretext task performance</a><a class="headerlink" href="#inspecting-pretext-task-performance" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id29" role="doc-backlink">Inspecting pretext task performance</a><a class="headerlink" href="#inspecting-pretext-task-performance" title="Link to this heading">#</a></h3>
 <p>We plot the loss and pretext task performance for the training and validation
 sets.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
@@ -812,10 +880,8 @@ <h3><a class="toc-backref" href="#id29">Inspecting pretext task performance</a><
 <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">styles</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">]</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">]</span>
 
-<span class="n">plt</span><span class="o">.</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn-talk&#39;</span><span class="p">)</span>
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">ax2</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>
 <span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys1</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">styles</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a><span class="p">):</span>
     <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
              <span class="n">c</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">)</span>
@@ -835,35 +901,35 @@ <h3><a class="toc-backref" href="#id29">Inspecting pretext task performance</a><
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_relative_positioning_001.png" srcset="../_images/sphx_glr_plot_relative_positioning_001.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><p>We also display the confusion matrix and classification report for the
+<img src="../../_images/sphx_glr_plot_relative_positioning_001.png" srcset="../../_images/sphx_glr_plot_relative_positioning_001.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><p>We also display the confusion matrix and classification report for the
 pretext task:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
 <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
 
 <span class="c1"># Switch to the test sampler</span>
-<a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">iterator_valid__sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">iterator_valid__sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a>
 <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.forward" title="skorch.net.NeuralNet.forward" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">forward</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="ow">in</span> <a href="../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a><span class="p">]</span>
 
 <span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
 <span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[66 55]
- [35 94]]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[90 31]
+ [41 88]]
               precision    recall  f1-score   support
 
-         0.0       0.65      0.55      0.59       121
-         1.0       0.63      0.73      0.68       129
+         0.0       0.69      0.74      0.71       121
+         1.0       0.74      0.68      0.71       129
 
-    accuracy                           0.64       250
-   macro avg       0.64      0.64      0.64       250
-weighted avg       0.64      0.64      0.64       250
+    accuracy                           0.71       250
+   macro avg       0.71      0.71      0.71       250
+weighted avg       0.71      0.71      0.71       250
 </pre></div>
 </div>
 </section>
 <section id="using-the-learned-representation-for-sleep-staging">
-<h3><a class="toc-backref" href="#id30">Using the learned representation for sleep staging</a><a class="headerlink" href="#using-the-learned-representation-for-sleep-staging" title="Permalink to this heading">#</a></h3>
+<h3><a class="toc-backref" href="#id30" role="doc-backlink">Using the learned representation for sleep staging</a><a class="headerlink" href="#using-the-learned-representation-for-sleep-staging" title="Link to this heading">#</a></h3>
 <p>We can now use the trained convolutional neural network as a feature
 extractor. We perform sleep stage classification from the learned feature
 representation using a linear logistic regression classifier.</p>
@@ -879,9 +945,9 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
     <span class="n">split</span><span class="o">.</span><span class="n">return_pair</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># Return single windows</span>
     <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><span class="n">split</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">)</span>
     <span class="k">with</span> <a href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
-        <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">batch_x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">))</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+        <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">batch_x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">))</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
                  <span class="k">for</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loader</span></a><span class="p">]</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a><span class="p">),</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">split</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="o">.</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a><span class="p">)</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a><span class="p">),</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">split</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="o">.</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a><span class="p">)</span>
 
 <span class="c1"># Initialize the logistic regression model</span>
 <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">log_reg</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span>
@@ -891,9 +957,9 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
 
 <span class="c1"># Fit and score the logistic regression</span>
 <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.fit" title="sklearn.pipeline.Pipeline.fit" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">])</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf_pipe</span></a><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf_pipe</span></a><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf_pipe</span></a><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
 
 <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y_pred</span></a><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_y_pred</span></a><span class="p">)</span>
@@ -909,35 +975,35 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
 <span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
 STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
 
 Increase the number of iterations (max_iter) or scale the data as shown in:
     https://scikit-learn.org/stable/modules/preprocessing.html
 Please also refer to the documentation for alternative solver options:
     https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,
+  n_iter_i = _check_optimize_result(
 Sleep staging performance with logistic regression:
-Train bal acc: 0.9052
-Valid bal acc: 0.5792
-Test bal acc: 0.5621
+Train bal acc: 0.8943
+Valid bal acc: 0.5254
+Test bal acc: 0.6476
 Results on test set:
-[[103  26   5   0   8]
- [ 11  53  11   0  34]
- [  4  42 345   6 165]
- [  0   0  70  33   2]
- [  1  23  32   0 114]]
+[[110  24   1   4   3]
+ [  9  76   7   2  15]
+ [ 28  52 410   3  69]
+ [  0   0  53  52   0]
+ [  0  58  20   0  92]]
               precision    recall  f1-score   support
 
-           0       0.87      0.73      0.79       142
-           1       0.37      0.49      0.42       109
-           2       0.75      0.61      0.67       562
-           3       0.85      0.31      0.46       105
-           4       0.35      0.67      0.46       170
+           0       0.75      0.77      0.76       142
+           1       0.36      0.70      0.48       109
+           2       0.84      0.73      0.78       562
+           3       0.85      0.50      0.63       105
+           4       0.51      0.54      0.53       170
 
-    accuracy                           0.60      1088
-   macro avg       0.64      0.56      0.56      1088
-weighted avg       0.67      0.60      0.61      1088
+    accuracy                           0.68      1088
+   macro avg       0.66      0.65      0.63      1088
+weighted avg       0.73      0.68      0.69      1088
 </pre></div>
 </div>
 <p>The balanced accuracy is much higher than chance-level (i.e., 20% for our
@@ -953,7 +1019,7 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
 <span class="c1"># tsne = TSNE(n_components=2)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">components</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA.fit_transform" title="sklearn.decomposition.PCA.fit_transform" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-method"><span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span>
 
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/cm_api.html#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-function"><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span></a><span class="p">(</span><span class="s1">&#39;viridis&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">)(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
 <span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stage</span></a> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">]):</span>
     <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mask</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a>
@@ -962,7 +1028,10 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_relative_positioning_002.png" srcset="../_images/sphx_glr_plot_relative_positioning_002.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x7f50ab5b3710&gt;
+<img src="../../_images/sphx_glr_plot_relative_positioning_002.png" srcset="../../_images/sphx_glr_plot_relative_positioning_002.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/examples/advanced_training/plot_relative_positioning.py:472: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+  colors = cm.get_cmap(&#39;viridis&#39;, 5)(range(5))
+
+&lt;matplotlib.legend.Legend object at 0x7f4215f9b790&gt;
 </pre></div>
 </div>
 <p>We see that there is sleep stage-related structure in the embedding. A
@@ -972,7 +1041,7 @@ <h3><a class="toc-backref" href="#id30">Using the learned representation for sle
 </section>
 </section>
 <section id="conclusion">
-<h2><a class="toc-backref" href="#id31">Conclusion</a><a class="headerlink" href="#conclusion" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id31" role="doc-backlink">Conclusion</a><a class="headerlink" href="#conclusion" title="Link to this heading">#</a></h2>
 <p>In this example, we used self-supervised learning (SSL) as a way to learn
 representations from unlabelled raw EEG data. Specifically, we used the
 relative positioning (RP) pretext task to train a feature extractor on a
@@ -983,45 +1052,50 @@ <h2><a class="toc-backref" href="#id31">Conclusion</a><a class="headerlink" href
 <p>Many avenues could be taken to improve on these results. For instance, using
 the entire Sleep Physionet dataset or training on larger datasets should help
 the feature extractor learn better representations during the pretext task.
-Other SSL tasks such as those described in <a class="footnote-reference brackets" href="#id15" id="id14">1</a> could further help discover
+Other SSL tasks such as those described in <a class="footnote-reference brackets" href="#id15" id="id14" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> could further help discover
 more powerful features.</p>
 </section>
 <section id="references">
-<h2><a class="toc-backref" href="#id32">References</a><a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id15"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id4">2</a>,<a href="#id5">3</a>,<a href="#id6">4</a>,<a href="#id11">5</a>,<a href="#id13">6</a>,<a href="#id14">7</a>)</span></dt>
-<dd><p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A.
+<h2><a class="toc-backref" href="#id32" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id15" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id11">5</a>,<a role="doc-backlink" href="#id13">6</a>,<a role="doc-backlink" href="#id14">7</a>)</span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A.
 (2020). Uncovering the structure of clinical EEG signals with
 self-supervised learning. arXiv preprint arXiv:2007.16104.</p>
-</dd>
-<dt class="label" id="id16"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A., &amp; Oberye, J. J.
+</aside>
+<aside class="footnote brackets" id="id16" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A., &amp; Oberye, J. J.
 (2000). Analysis of a sleep-dependent neuronal feedback loop: the
 slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical
 Engineering, 47(9), 1185-1194.</p>
-</dd>
-<dt class="label" id="id17"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
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+</aside>
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+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov,
 P. C., Mark, R. G., … &amp; Stanley, H. E. (2000). PhysioBank,
 PhysioToolkit, and PhysioNet: components of a new research resource for
 complex physiologic signals. circulation, 101(23), e215-e220.</p>
-</dd>
-<dt class="label" id="id18"><span class="brackets"><a class="fn-backref" href="#id12">4</a></span></dt>
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+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id12">4</a><span class="fn-bracket">]</span></span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
 (2018)A Deep Learning Architecture for Temporal Sleep Stage
 Classification Using Multivariate and Multimodal Time Series.
 IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
 (758-769)</p>
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+              
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+<span id="sphx-glr-auto-examples-advanced-training-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>07:13.105</strong> total execution time for <strong>auto_examples_advanced_training</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_cropped.py</span></code>)</p></td>
+<td><p>05:56.966</p></td>
+<td><p>457.1 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation_search.py</span></code>)</p></td>
+<td><p>00:40.536</p></td>
+<td><p>538.4 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_relative_positioning.py</span></code>)</p></td>
+<td><p>00:35.603</p></td>
+<td><p>213.5 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
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+<p>Examples demonstrating analaysis of EEG on data from clinical studies, sleep &amp; cognitive experiments.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
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diff --git a/stable/auto_examples/plot_bcic_iv_4_ecog_trial.html b/0.8/auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html
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+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Applied examples on real-world datasets</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
   <div class="sphx-glr-download-link-note admonition note">
 <p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">here</span></a>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code</p>
 </div>
 <section class="sphx-glr-example-title" id="fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset">
-<span id="sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"></span><h1>Fingers flexion decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset" title="Permalink to this heading">#</a></h1>
+<span id="sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"></span><h1>Fingers flexion decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset" title="Link to this heading">#</a></h1>
 <p>This tutorial shows you how to train and test deep learning models with
 Braindecode on ECoG BCI IV competition dataset 4. For this dataset we will
 predict 5 regression targets corresponding to flexion of each finger.
@@ -417,9 +484,9 @@
 </pre></div>
 </div>
 <section id="loading-and-preparing-the-dataset">
-<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Permalink to this heading">#</a></h2>
+<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Link to this heading">#</a></h2>
 <section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
 <p>First, we load the data. In this tutorial, we use the functionality of braindecode
 to load <a class="reference external" href="http://www.bbci.de/competition/iv/#dataset4">BCI IV competition dataset 4</a>.
 The dataset is available as a part of ECoG library:
@@ -433,15 +500,15 @@ <h3>Loading<a class="headerlink" href="#loading" title="Permalink to this headin
 “Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">1</span>
-<a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+<a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
 </pre></div>
 </div>
 </section>
 <section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
 <p>Now we apply preprocessing like bandpass filtering to our dataset. You
 can either apply functions provided by
 <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
@@ -461,8 +528,9 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 PyTorch-libraries like
 <a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
 
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">1.</span>  <span class="c1"># low cut frequency for filtering</span>
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">200.</span>  <span class="c1"># high cut frequency for filtering, for ECoG higher than for EEG</span>
@@ -473,10 +541,10 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 </div>
 <p>We select only first 30 seconds from each dataset to limit time and memory
 to run this example. To obtain results on the whole datasets you should remove this line.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)])</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.bcicomp.BCICompetitionIVDataset4 object at 0x7f50a8ac48d0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.bcicomp.BCICompetitionIVDataset4 object at 0x7f4217525f40&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -484,15 +552,15 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 many mne functions ignore <cite>misc</cite> channels and perform operations only on data channels
 (see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
                  <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">)</span>
 <span class="p">]</span>
 
 <span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
 
 <span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
@@ -501,22 +569,22 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;temp&#39;</span><span class="p">][</span><span class="s1">&#39;target_sfreq&#39;</span><span class="p">]</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 </pre></div>
 </div>
 </section>
 <section id="cut-compute-windows">
-<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Permalink to this heading">#</a></h3>
+<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
 <p>Now we cut out compute windows, the inputs for the deep networks during
 training. In the case of trialwise decoding of time series targets, we just have to
 decide about length windows that will be selected from the signal preceding each target.
 We use different windowing function than in standard trialwise decoding as our targets
 are stored as target channels in mne.Raw.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a>
 
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
     <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
     <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
     <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span>
@@ -529,18 +597,18 @@ <h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="
 <p>Methods to predict all 5 fingers flexion with the same model may be cnosidered as well.
 We encourage you to find your own way to use braindecode models to predict finers fexions.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
 </pre></div>
 </div>
 </section>
 <section id="split-dataset-into-train-valid-and-test">
-<h3>Split dataset into train, valid, and test<a class="headerlink" href="#split-dataset-into-train-valid-and-test" title="Permalink to this heading">#</a></h3>
+<h3>Split dataset into train, valid, and test<a class="headerlink" href="#split-dataset-into-train-valid-and-test" title="Link to this heading">#</a></h3>
 <p>We can easily split the dataset using additional info stored in the
 description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select <cite>train</cite> dataset
 for training and validation and <cite>test</cite> for final evaluation.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
 <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
 </pre></div>
 </div>
 <p>We can split train dataset into training and validation datasets using
@@ -560,7 +628,7 @@ <h3>Split dataset into train, valid, and test<a class="headerlink" href="#split-
 </section>
 </section>
 <section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
 <p>Now we create the deep learning model! Braindecode comes with some
 predefined convolutional neural network architectures for raw
 time-domain EEG. Here, we use the shallow ConvNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
@@ -569,8 +637,8 @@ <h2>Create model<a class="headerlink" href="#create-model" title="Permalink to t
 pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
 to use your own model, it just has to be a normal PyTorch
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
 
 <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
 <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
@@ -583,36 +651,33 @@ <h2>Create model<a class="headerlink" href="#create-model" title="Permalink to t
 <span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
 <span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
 
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_out_chans</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 <span class="c1"># Extract number of chans and time steps from dataset</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># 1 second long windows</span>
 
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_out_chans</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
     <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+    <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="c1"># We are removing the softmax layer to make it a regression model</span>
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">()</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.named_children" title="torch.nn.Module.named_children" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">named_children</span></a><span class="p">():</span>
-    <span class="k">if</span> <span class="s2">&quot;softmax&quot;</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">:</span>
-        <span class="k">continue</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.add_module" title="torch.nn.Module.add_module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">new_model</span><span class="o">.</span><span class="n">add_module</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span><span class="p">)</span>
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a>
-
 <span class="c1"># Send model to GPU</span>
 <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
     <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
 </pre></div>
 </div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+</pre></div>
+</div>
 </section>
 <section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
 <p>Now we train the network! EEGRegressor is a Braindecode object
 responsible for managing the training of neural networks. It inherits
 from skorch.NeuralNetRegressor, so the training logic is the same as in
@@ -624,8 +689,8 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 encourage you to perform your own hyperparameter and preprocessing optimization using
 cross validation on your training data.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
 <span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
 
 <span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
@@ -634,12 +699,12 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
 <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">8</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
 
 
 <span class="c1"># Function to compute Pearson correlation coefficient</span>
-<span class="k">def</span> <span class="nf">pearson_r_score</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="n">preds</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+<span class="k">def</span> <span class="nf">pearson_r_score</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">preds</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
         <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><span class="n">y</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">preds</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
@@ -647,19 +712,19 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 
 
 <span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
     <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span></a><span class="p">,</span>
     <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation,</span>
     <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
     <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
     <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
     <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
         <span class="s1">&#39;r2&#39;</span><span class="p">,</span>
         <span class="p">(</span><span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span><span class="n">pearson_r_score</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                         <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">)),</span>
+                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">)),</span>
         <span class="p">(</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span><span class="n">pearson_r_score</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                         <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">)),</span>
+                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">)),</span>
         <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
     <span class="p">],</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
@@ -672,21 +737,15 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_pearson_r    train_r2    valid_loss    valid_pearson_r    valid_r2      lr      dur
--------  ------------  -----------------  ----------  ------------  -----------------  ----------  ------  -------
-      1        2.5083             0.1251     -0.5575        5.6921            -0.1469     -1.6035  0.0006  31.4192
-      2        1.6500             0.3664      0.2126        3.2733            -0.0339     -0.4971  0.0006  29.6132
-      3        1.0988             0.4603      0.4332        2.9570             0.0646     -0.3525  0.0005  29.3645
-      4        1.0579             0.4546      0.5680        3.2952             0.1217     -0.5072  0.0004  29.3602
-      5        0.9256             0.4871      0.6518        3.6003             0.1068     -0.6467  0.0002  29.1617
-      6        0.8316             0.5715      0.7011        3.5173             0.0864     -0.6088  0.0001  29.3251
-      7        0.7715             0.5904      0.7115        3.4734             0.0879     -0.5887  0.0000  29.5004
-      8        0.7617             0.5930      0.7118        3.4683             0.0880     -0.5863  0.0000  29.1364
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_pearson_r    train_r2    valid_loss    valid_pearson_r    valid_r2      lr     dur
+-------  ------------  -----------------  ----------  ------------  -----------------  ----------  ------  ------
+      1        2.5083             0.1251     -0.5570        5.7352            -0.1514     -1.6146  0.0006  3.6909
+      2        1.7986             0.3712     -0.3153        5.2588            -0.1513     -1.3974  0.0000  3.7828
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">])</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">])</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">])</span>
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
@@ -695,20 +754,19 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 </div>
 </section>
 <section id="plot-results">
-<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>We plot target and predicted finger flexion on training, validation, adn test sets.</p>
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We plot target and predicted finger flexion on training, validation, and test sets.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>The model is trained and validated on limited dataset (to decrease the time neded to run
+<p>The model is trained and validated on limited dataset (to decrease the time needed to run
 this example) which does not contain diverse dataset in terms of fingers flexions and may
 cause overfitting. To obtain better results use whole dataset as well as improve the decoding
 pipeline which may be not optimal for ECoG.</p>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
 
 <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Training dataset&#39;</span><span class="p">)</span>
@@ -734,16 +792,16 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
 <span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
     <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
         <a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
     <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.round.html#numpy.round" title="numpy.round" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">round</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Correlation coefficient for each dimension:  [0.05]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Correlation coefficient for each dimension:  [0.1]
 </pre></div>
 </div>
 <p>Now we use the history stored by Skorch throughout training to plot
@@ -753,17 +811,17 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
                   <span class="n">index</span><span class="o">=</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
 
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
 <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 
-<span class="n">ax2</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
 
 <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Pearson correlation coefficient&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
@@ -778,14 +836,14 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" alt="plot bcic iv 4 ecog trial" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  43.141 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1883 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-4-ecog-trial-py">
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" alt="plot bcic iv 4 ecog trial" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 20.608 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  317 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/9dd4851efc4f3e6584054e947df0f447/plot_bcic_iv_4_ecog_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code></a></p>
+<p><a class="reference download internal" download="" href="../../_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code></a></p>
 </div>
 <div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/e98035eece5207cc7fa43203bc5a6fe5/plot_bcic_iv_4_ecog_trial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.ipynb</span></code></a></p>
+<p><a class="reference download internal" download="" href="../../_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.ipynb</span></code></a></p>
 </div>
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 <p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
@@ -793,135 +851,105 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 </section>
 
 
-            </article>
-            
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-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
-  <a class='left-prev' id="prev-link" href="plot_data_augmentation_search.html" title="previous page">
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+                <footer class="bd-footer-article">
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+    <a class="left-prev"
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+       title="previous page">
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-          <p class="prev-next-subtitle">previous</p>
-          <p class="prev-next-title">Searching the best data augmentation on BCIC IV 2a Dataset</p>
+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Applied examples on real-world datasets</p>
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-  </a>
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-      <p class="prev-next-subtitle">next</p>
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-     Loading
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-    <a class="reference internal nav-link" href="#preprocessing">
-     Preprocessing
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-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#cut-compute-windows">
-     Cut Compute Windows
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#split-dataset-into-train-valid-and-test">
-     Split dataset into train, valid, and test
+    <a class="right-next"
+       href="plot_sleep_staging_chambon2018.html"
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-   Training
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+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+
+
+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Applied examples on real-world datasets</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Chambon2018 network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the time distributed approach of <a class="footnote-reference brackets" href="#id10" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to learn on
+sequences of EEG windows using the openly accessible Sleep Physionet dataset
+<a class="footnote-reference brackets" href="#id11" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id12" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
+a lowpass filter. We omit the downsampling step of <a class="footnote-reference brackets" href="#id10" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> as the Sleep
+Physionet data is already sampled at a lower 100 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f41f4f8fdf0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="window-preprocessing">
+<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Link to this heading">#</a></h3>
+<p>We also preprocess the windows by applying channel-wise z-score normalization
+in each window.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4217631580&gt;
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id10" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows, such that the embeddings of
+multiple consecutive windows can be concatenated and provided to a final
+classifier. We can achieve this by defining Sampler objects that return
+sequences of window indices.
+To simplify the example, we train the whole model end-to-end on sequences,
+rather than using the two-step approach of <a class="footnote-reference brackets" href="#id10" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> (i.e. training the feature
+extractor on single windows, then freezing its weights and training the
+classifier).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  372
+Validation examples:  383
+</pre></div>
+</div>
+<p>We also implement a transform to extract the label of the center window of a
+sequence to use it as target.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use label of center window in the sequence</span>
+<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting on the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the sleep
+staging architecture introduced in <a class="footnote-reference brackets" href="#id10" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is a four-layer convolutional
+neural network. We use the time distributed version of the model, where the
+feature vectors of a sequence of windows are concatenated and passed to a
+linear layer for classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+
+<a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
+    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <a class="reference internal" href="../../source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id10" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, as these hyperparameters were
+optimized on a different dataset (MASS SS3) and with a different number of
+recordings. Generally speaking, it is recommended to perform
+hyperparameter optimization if reusing this code on a different dataset or
+with more recordings.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">10</span>
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_acc    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  -----------  ---------------  ------------  ------
+      1           0.2136        1.5768       0.0888           0.2000        1.6372  1.2452
+      2           0.2036        1.4788       0.0888           0.2000        1.6674  1.1995
+      3           0.2199        1.4032       0.0966           0.2102        1.6340  1.2571
+      4           0.2923        1.3400       0.1175           0.2512        1.6769  1.4723
+      5           0.4023        1.2092       0.4700           0.4077        1.6190  1.6105
+      6           0.5281        1.0958       0.2689           0.4086        1.6232  1.2061
+      7           0.5726        0.9687       0.5953           0.5680        1.3616  1.1775
+      8           0.6405        0.8909       0.7050           0.5777        1.2944  1.1070
+      9           0.6877        0.8175       0.7102           0.5808        1.3463  1.1882
+     10           0.6977        0.7450       0.5039           0.5214        1.6099  1.1088
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=Sequential(
+    (0): TimeDistributed(
+      (module): SleepStagerChambon2018(
+        (spatial_conv): Conv2d(1, 2, kernel_size=(2, 1), stride=(1, 1))
+        (feature_extractor): Sequential(
+          (0): Conv2d(1, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
+          (1): Identity()
+          (2): ReLU()
+          (3): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
+          (4): Conv2d(8, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
+          (5): Identity()
+          (6): ReLU()
+          (7): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
+        )
+      )
+    )
+    (1): Sequential(
+      (0): Flatten(start_dim=1, end_dim=-1)
+      (1): Dropout(p=0.5, inplace=False)
+      (2): Linear(in_features=816, out_features=5, bias=True)
+    )
+  ),
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span>
+                      <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Wake&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;REM&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_002.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+           0       0.13      0.33      0.19        61
+           1       0.19      0.14      0.16        21
+           2       0.55      0.31      0.40       194
+           3       0.26      0.25      0.26        75
+           4       0.03      0.03      0.03        32
+
+    accuracy                           0.27       383
+   macro avg       0.23      0.21      0.21       383
+weighted avg       0.37      0.27      0.29       383
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_003.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>Our model was able to learn despite the low amount of data that was available
+(only two recordings in this example) and reached a balanced accuracy of
+about 36% in a 5-class classification task (chance-level = 20%) on held-out
+data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To further improve performance, more recordings should be included in the
+training set, and hyperparameters should be selected accordingly.
+Increasing the sequence length was also shown in <a class="footnote-reference brackets" href="#id10" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to help improve
+performance, especially when few EEG channels are available.</p>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+(2018)A Deep Learning Architecture for Temporal Sleep Stage
+Classification Using Multivariate and Multimodal Time Series.
+IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+(758-769)</p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id12" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
+Circulation 101(23):e215-e220</p>
+</aside>
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Applied examples on real-world datasets</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
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+</nav></div>
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+    
+    <li class="breadcrumb-item active" aria-current="page">Sleep staging on the Sleep Physionet dataset using Eldele2021</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Eldele2021<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We use the attention-based model from <a class="footnote-reference brackets" href="#id8" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> with the time distributed approach of <a class="footnote-reference brackets" href="#id9" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>
+to learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id10" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id11" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
+a lowpass filter.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f41f6172ca0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.
+The Eldele2021 model takes a single channel as input. Here, the Fpz-Cz channel is used as it
+was found to give better performance than using the Pz-Oz channel</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">picks</span><span class="o">=</span><span class="s2">&quot;Fpz-Cz&quot;</span><span class="p">,</span>  <span class="c1"># the other option is Pz-Oz,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/windowers.py:130: UserWarning: Using reject or picks or flat or dropping bad windows means mne Epochs are created, which will be substantially slower and may be deprecated in the future.
+  warnings.warn(&#39;Using reject or picks or flat or dropping bad windows means &#39;
+</pre></div>
+</div>
+</section>
+<section id="window-preprocessing">
+<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Link to this heading">#</a></h3>
+<p>We also preprocess the windows by applying channel-wise z-score normalization
+in each window.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42173f84f0&gt;
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id9" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows, such that the embeddings of
+multiple consecutive windows can be concatenated and provided to a final
+classifier. We can achieve this by defining Sampler objects that return
+sequences of window indices.
+To simplify the example, we train the whole model end-to-end on sequences,
+rather than using the two-step approach of <a class="footnote-reference brackets" href="#id9" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> (i.e. training the feature
+extractor on single windows, then freezing its weights and training the
+classifier).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  133
+Validation examples:  133
+</pre></div>
+</div>
+<p>We also implement a transform to extract the label of the center window of a
+sequence to use it as target.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<span class="c1"># Use label of center window in the sequence</span>
+<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting on the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the sleep
+staging architecture introduced in <a class="footnote-reference brackets" href="#id8" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is an attention-based
+neural network. We use the time distributed version of the model, where the
+feature vectors of a sequence of windows are concatenated and passed to a
+linear layer for classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to reproduce results</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+
+<a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
+    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <a class="reference internal" href="../../source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_acc    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  -----------  ---------------  ------------  ------
+      1           0.1471        3.1814       0.3534           0.1827        3.4743  1.6137
+      2           0.2712        1.8800       0.1955           0.2186        2.1542  1.4520
+      3           0.4550        1.4935       0.2256           0.2200        4.2234  1.4485
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=Sequential(
+    (0): TimeDistributed(
+      (module): SleepStagerEldele2021(
+        (feature_extractor): Sequential(
+          (0): _MRCNN(
+            (GELU): GELU(approximate=&#39;none&#39;)
+            (features1): Sequential(
+              (0): Conv1d(1, 64, kernel_size=(50,), stride=(6,), padding=(24,), bias=False)
+              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): GELU(approximate=&#39;none&#39;)
+              (3): MaxPool1d(kernel_size=8, stride=2, padding=4, dilation=1, ceil_mode=False)
+              (4): Dropout(p=0.5, inplace=False)
+              (5): Conv1d(64, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
+              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (7): GELU(approximate=&#39;none&#39;)
+              (8): Conv1d(128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
+              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (10): GELU(approximate=&#39;none&#39;)
+              (11): MaxPool1d(kernel_size=4, stride=4, padding=2, dilation=1, ceil_mode=False)
+            )
+            (features2): Sequential(
+              (0): Conv1d(1, 64, kernel_size=(400,), stride=(50,), padding=(200,), bias=False)
+              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): GELU(approximate=&#39;none&#39;)
+              (3): MaxPool1d(kernel_size=4, stride=2, padding=2, dilation=1, ceil_mode=False)
+              (4): Dropout(p=0.5, inplace=False)
+              (5): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
+              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (7): GELU(approximate=&#39;none&#39;)
+              (8): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
+              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (10): GELU(approximate=&#39;none&#39;)
+              (11): MaxPool1d(kernel_size=2, stride=2, padding=1, dilation=1, ceil_mode=False)
+            )
+            (dropout): Dropout(p=0.5, inplace=False)
+            (AFR): Sequential(
+              (0): _SEBasicBlock(
+                (conv1): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
+                (bn1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                (relu): ReLU(inplace=True)
+                (conv2): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
+                (bn2): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                (se): _SELayer(
+                  (avg_pool): AdaptiveAvgPool1d(output_size=1)
+                  (fc): Sequential(
+                    (0): Linear(in_features=30, out_features=1, bias=False)
+                    (1): ReLU(inplace=True)
+                    (2): Linear(in_features=1, out_features=30, bias=False)
+                    (3): Sigmoid()
+                  )
+                )
+                (downsample): Sequential(
+                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,), bias=False)
+                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                )
+                (features): Sequential(
+                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
+                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                  (2): ReLU(inplace=True)
+                  (3): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
+                  (4): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                  (5): _SELayer(
+                    (avg_pool): AdaptiveAvgPool1d(output_size=1)
+                    (fc): Sequential(
+                      (0): Linear(in_features=30, out_features=1, bias=False)
+                      (1): ReLU(inplace=True)
+                      (2): Linear(in_features=1, out_features=30, bias=False)
+                      (3): Sigmoid()
+                    )
+                  )
+                )
+              )
+            )
+          )
+          (1): _TCE(
+            (layers): ModuleList(
+              (0-1): 2 x _EncoderLayer(
+                (self_attn): _MultiHeadedAttention(
+                  (convs): ModuleList(
+                    (0-2): 3 x _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
+                  )
+                  (linear): Linear(in_features=80, out_features=80, bias=True)
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+                (feed_forward): _PositionwiseFeedForward(
+                  (w_1): Linear(in_features=80, out_features=120, bias=True)
+                  (w_2): Linear(in_features=120, out_features=80, bias=True)
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+                (sublayer_output): ModuleList(
+                  (0-1): 2 x _SublayerOutput(
+                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                )
+                (conv): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
+              )
+            )
+            (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
+          )
+        )
+      )
+    )
+    (1): Sequential(
+      (0): Flatten(start_dim=1, end_dim=-1)
+      (1): Dropout(p=0.5, inplace=False)
+      (2): Linear(in_features=7200, out_features=5, bias=True)
+    )
+  ),
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">)</span>
+<span class="c1">#                      class_names=[&#39;Wake&#39;, &#39;N1&#39;, &#39;N2&#39;, &#39;N3&#39;, &#39;N4&#39;, &#39;REM&#39;])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_002.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+              precision    recall  f1-score   support
+
+           0       1.00      0.02      0.03        60
+           1       0.00      0.00      0.00         6
+           2       0.00      0.00      0.00        48
+           3       0.15      1.00      0.26        19
+           4       0.00      0.00      0.00         0
+
+    accuracy                           0.15       133
+   macro avg       0.23      0.20      0.06       133
+weighted avg       0.47      0.15      0.05       133
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_003.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>The model was able to learn despite the low amount of data that was available
+(only two recordings in this example) and reached a balanced accuracy of
+about 43% in a 5-class classification task (chance-level = 20%) on held-out
+data over 10 epochs.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To further improve performance, the number of epochs should be increased.
+It has been reduced here for faster run-time in document generation. In
+testing, 10 epochs provided reasonable performance with around 89% balanced
+accuracy on training data and around 43% on held out validation data.
+Increasing the number of training recordings and optimizing the hyperparameters
+will also help increase performance</p>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id8" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id7">2</a>)</span>
+<p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
+Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
+Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
+</aside>
+<aside class="footnote brackets" id="id9" role="note">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>)</span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+(2018)A Deep Learning Architecture for Temporal Sleep Stage
+Classification Using Multivariate and Multimodal Time Series.
+IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+(758-769)</p>
+</aside>
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">4</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
+Circulation 101(23):e215-e220</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.212 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  15 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sleep_staging_eldele2021.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sleep_staging_eldele2021.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+
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+  
+</div>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extract-windows">Extract windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#window-preprocessing">Window preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-sequence-samplers">Create sequence samplers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
+</ul>
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diff --git a/0.8/auto_examples/applied_examples/plot_sleep_staging_usleep.html b/0.8/auto_examples/applied_examples/plot_sleep_staging_usleep.html
new file mode 100644
index 00000000..a20c6f98
--- /dev/null
+++ b/0.8/auto_examples/applied_examples/plot_sleep_staging_usleep.html
@@ -0,0 +1,1059 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>Sleep staging on the Sleep Physionet dataset using U-Sleep network &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
+  
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"></span><h1>Sleep staging on the Sleep Physionet dataset using U-Sleep network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network" title="Link to this heading">#</a></h1>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Please take a look at the simpler sleep staging example
+<span class="xref std std-ref">here</span>
+before going through this example. The current example uses a more complex
+architecture and a sequence-to-sequence (seq2seq) approach.</p>
+</div>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the U-Sleep approach of <a class="footnote-reference brackets" href="#id10" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to learn on sequences of EEG
+windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id11" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id12" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The example is written to have a very short execution time.
+This number of epochs is here too small and very few recordings are used.
+To obtain competitive results you need to use more data and more epochs.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Theo Gnassounou &lt;theo.gnassounou@inria.fr&gt;</span>
+<span class="c1">#          Omar Chehab &lt;l-emir-omar.chehab@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We scale each channel recording-wise to
+have zero median and unit interquartile range. We don’t upsample to 128 Hz as
+done in <a class="footnote-reference brackets" href="#id10" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> so that we keep the example as light as possible. No filtering
+is described in <a class="footnote-reference brackets" href="#id10" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f41eda76310&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the sequence-to-sequence approach of <a class="footnote-reference brackets" href="#id10" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows. We can achieve this by defining
+Sampler objects that return sequences of windows.
+Non-overlapping sequences of 35 windows are used in <a class="footnote-reference brackets" href="#id10" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, however to limit
+the memory requirements for this example we use shorter sequences of 3
+windows.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows; originally 35 in paper</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Non-overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>133
+133
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting to the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the
+U-Sleep architecture introduced in <a class="footnote-reference brackets" href="#id10" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is fully convolutional
+neural network.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+<a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a><span class="p">(</span>
+    <span class="n">n_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">depth</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
+    <span class="n">with_skip_connection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <a class="reference internal" href="../../source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id10" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, as these hyperparameters were
+optimized on different datasets and with a different number of recordings.
+Generally speaking, it is recommended to perform hyperparameter
+optimization if reusing this code on a different dataset or with more
+recordings.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a>
+
+
+<span class="k">def</span> <span class="nf">balanced_accuracy_multi</span><span class="p">(</span><a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="k">return</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
+    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
+    <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Deactivate the default valid_acc callback:</span>
+<a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.set_params" title="skorch.net.NeuralNet.set_params" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">set_params</span></a><span class="p">(</span><span class="n">callbacks__valid_acc</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/torch/nn/modules/conv.py:309: UserWarning: Using padding=&#39;same&#39; with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:1003.)
+  return F.conv1d(input, weight, bias, self.stride,
+  epoch    train_bal_acc    train_loss    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  ---------------  ------------  ------
+      1           0.2040        1.6129           0.1707        1.5807  2.2238
+      2           0.2224        1.5430           0.1594        1.5866  1.8603
+      3           0.3464        1.4928           0.1853        1.6026  1.7761
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=======================================================================================================================================================
+  Layer (type (var_name):depth-idx)                  Input Shape               Output Shape              Param #                   Kernel Shape
+  ======================================================================================================================================================
+  USleep (USleep)                                    [1, 2, 3000]              [1, 5]                    --                        --
+  ├─Sequential (encoder): 1-1                        --                        --                        --                        --
+  │    └─_EncoderBlock (0): 2-1                      [1, 2, 3000]              [1, 6, 1500]              --                        7
+  │    │    └─Sequential (block_prepool): 3-1        [1, 2, 3000]              [1, 6, 3000]              102                       --
+  │    │    └─MaxPool1d (maxpool): 3-2               [1, 6, 3000]              [1, 6, 1500]              --                        2
+  │    └─_EncoderBlock (1): 2-2                      [1, 6, 1500]              [1, 9, 750]               --                        7
+  │    │    └─Sequential (block_prepool): 3-3        [1, 6, 1500]              [1, 9, 1500]              405                       --
+  │    │    └─MaxPool1d (maxpool): 3-4               [1, 9, 1500]              [1, 9, 750]               --                        2
+  │    └─_EncoderBlock (2): 2-3                      [1, 9, 750]               [1, 11, 375]              --                        7
+  │    │    └─Sequential (block_prepool): 3-5        [1, 9, 750]               [1, 11, 750]              726                       --
+  │    │    └─MaxPool1d (maxpool): 3-6               [1, 11, 750]              [1, 11, 375]              --                        2
+  │    └─_EncoderBlock (3): 2-4                      [1, 11, 375]              [1, 15, 188]              --                        7
+  │    │    └─Sequential (block_prepool): 3-7        [1, 11, 375]              [1, 15, 375]              1,200                     --
+  │    │    └─ConstantPad1d (pad): 3-8               [1, 15, 375]              [1, 15, 377]              --                        --
+  │    │    └─MaxPool1d (maxpool): 3-9               [1, 15, 377]              [1, 15, 188]              --                        2
+  │    └─_EncoderBlock (4): 2-5                      [1, 15, 188]              [1, 20, 94]               --                        7
+  │    │    └─Sequential (block_prepool): 3-10       [1, 15, 188]              [1, 20, 188]              2,160                     --
+  │    │    └─MaxPool1d (maxpool): 3-11              [1, 20, 188]              [1, 20, 94]               --                        2
+  │    └─_EncoderBlock (5): 2-6                      [1, 20, 94]               [1, 28, 47]               --                        7
+  │    │    └─Sequential (block_prepool): 3-12       [1, 20, 94]               [1, 28, 94]               4,004                     --
+  │    │    └─MaxPool1d (maxpool): 3-13              [1, 28, 94]               [1, 28, 47]               --                        2
+  │    └─_EncoderBlock (6): 2-7                      [1, 28, 47]               [1, 40, 24]               --                        7
+  │    │    └─Sequential (block_prepool): 3-14       [1, 28, 47]               [1, 40, 47]               7,960                     --
+  │    │    └─ConstantPad1d (pad): 3-15              [1, 40, 47]               [1, 40, 49]               --                        --
+  │    │    └─MaxPool1d (maxpool): 3-16              [1, 40, 49]               [1, 40, 24]               --                        2
+  │    └─_EncoderBlock (7): 2-8                      [1, 40, 24]               [1, 55, 12]               --                        7
+  │    │    └─Sequential (block_prepool): 3-17       [1, 40, 24]               [1, 55, 24]               15,565                    --
+  │    │    └─MaxPool1d (maxpool): 3-18              [1, 55, 24]               [1, 55, 12]               --                        2
+  │    └─_EncoderBlock (8): 2-9                      [1, 55, 12]               [1, 77, 6]                --                        7
+  │    │    └─Sequential (block_prepool): 3-19       [1, 55, 12]               [1, 77, 12]               29,876                    --
+  │    │    └─MaxPool1d (maxpool): 3-20              [1, 77, 12]               [1, 77, 6]                --                        2
+  │    └─_EncoderBlock (9): 2-10                     [1, 77, 6]                [1, 108, 3]               --                        7
+  │    │    └─Sequential (block_prepool): 3-21       [1, 77, 6]                [1, 108, 6]               58,536                    --
+  │    │    └─MaxPool1d (maxpool): 3-22              [1, 108, 6]               [1, 108, 3]               --                        2
+  │    └─_EncoderBlock (10): 2-11                    [1, 108, 3]               [1, 152, 2]               --                        7
+  │    │    └─Sequential (block_prepool): 3-23       [1, 108, 3]               [1, 152, 3]               115,368                   --
+  │    │    └─ConstantPad1d (pad): 3-24              [1, 152, 3]               [1, 152, 5]               --                        --
+  │    │    └─MaxPool1d (maxpool): 3-25              [1, 152, 5]               [1, 152, 2]               --                        2
+  │    └─_EncoderBlock (11): 2-12                    [1, 152, 2]               [1, 214, 1]               --                        7
+  │    │    └─Sequential (block_prepool): 3-26       [1, 152, 2]               [1, 214, 2]               228,338                   --
+  │    │    └─MaxPool1d (maxpool): 3-27              [1, 214, 2]               [1, 214, 1]               --                        2
+  ├─Sequential (bottom): 1-2                         [1, 214, 1]               [1, 302, 1]               --                        --
+  │    └─Conv1d (0): 2-13                            [1, 214, 1]               [1, 302, 1]               452,698                   [7]
+  │    └─ELU (1): 2-14                               [1, 302, 1]               [1, 302, 1]               --                        --
+  │    └─BatchNorm1d (2): 2-15                       [1, 302, 1]               [1, 302, 1]               604                       --
+  ├─Sequential (decoder): 1-3                        --                        --                        --                        --
+  │    └─_DecoderBlock (0): 2-16                     [1, 302, 1]               [1, 214, 2]               --                        7
+  │    │    └─Sequential (block_preskip): 3-28       [1, 302, 1]               [1, 214, 2]               129,898                   --
+  │    │    └─Sequential (block_postskip): 3-29      [1, 428, 2]               [1, 214, 2]               641,786                   --
+  │    └─_DecoderBlock (1): 2-17                     [1, 214, 2]               [1, 152, 3]               --                        7
+  │    │    └─Sequential (block_preskip): 3-30       [1, 214, 2]               [1, 152, 4]               65,512                    --
+  │    │    └─Sequential (block_postskip): 3-31      [1, 304, 3]               [1, 152, 3]               323,912                   --
+  │    └─_DecoderBlock (2): 2-18                     [1, 152, 3]               [1, 108, 6]               --                        7
+  │    │    └─Sequential (block_preskip): 3-32       [1, 152, 3]               [1, 108, 6]               33,156                    --
+  │    │    └─Sequential (block_postskip): 3-33      [1, 216, 6]               [1, 108, 6]               163,620                   --
+  │    └─_DecoderBlock (3): 2-19                     [1, 108, 6]               [1, 77, 12]               --                        7
+  │    │    └─Sequential (block_preskip): 3-34       [1, 108, 6]               [1, 77, 12]               16,863                    --
+  │    │    └─Sequential (block_postskip): 3-35      [1, 154, 12]              [1, 77, 12]               83,237                    --
+  │    └─_DecoderBlock (4): 2-20                     [1, 77, 12]               [1, 55, 24]               --                        7
+  │    │    └─Sequential (block_preskip): 3-36       [1, 77, 12]               [1, 55, 24]               8,635                     --
+  │    │    └─Sequential (block_postskip): 3-37      [1, 110, 24]              [1, 55, 24]               42,515                    --
+  │    └─_DecoderBlock (5): 2-21                     [1, 55, 24]               [1, 40, 47]               --                        7
+  │    │    └─Sequential (block_preskip): 3-38       [1, 55, 24]               [1, 40, 48]               4,520                     --
+  │    │    └─Sequential (block_postskip): 3-39      [1, 80, 47]               [1, 40, 47]               22,520                    --
+  │    └─_DecoderBlock (6): 2-22                     [1, 40, 47]               [1, 28, 94]               --                        7
+  │    │    └─Sequential (block_preskip): 3-40       [1, 40, 47]               [1, 28, 94]               2,324                     --
+  │    │    └─Sequential (block_postskip): 3-41      [1, 56, 94]               [1, 28, 94]               11,060                    --
+  │    └─_DecoderBlock (7): 2-23                     [1, 28, 94]               [1, 20, 188]              --                        7
+  │    │    └─Sequential (block_preskip): 3-42       [1, 28, 94]               [1, 20, 188]              1,180                     --
+  │    │    └─Sequential (block_postskip): 3-43      [1, 40, 188]              [1, 20, 188]              5,660                     --
+  │    └─_DecoderBlock (8): 2-24                     [1, 20, 188]              [1, 15, 375]              --                        7
+  │    │    └─Sequential (block_preskip): 3-44       [1, 20, 188]              [1, 15, 376]              645                       --
+  │    │    └─Sequential (block_postskip): 3-45      [1, 30, 375]              [1, 15, 375]              3,195                     --
+  │    └─_DecoderBlock (9): 2-25                     [1, 15, 375]              [1, 11, 750]              --                        7
+  │    │    └─Sequential (block_preskip): 3-46       [1, 15, 375]              [1, 11, 750]              363                       --
+  │    │    └─Sequential (block_postskip): 3-47      [1, 22, 750]              [1, 11, 750]              1,727                     --
+  │    └─_DecoderBlock (10): 2-26                    [1, 11, 750]              [1, 9, 1500]              --                        7
+  │    │    └─Sequential (block_preskip): 3-48       [1, 11, 750]              [1, 9, 1500]              225                       --
+  │    │    └─Sequential (block_postskip): 3-49      [1, 18, 1500]             [1, 9, 1500]              1,161                     --
+  │    └─_DecoderBlock (11): 2-27                    [1, 9, 1500]              [1, 6, 3000]              --                        7
+  │    │    └─Sequential (block_preskip): 3-50       [1, 9, 1500]              [1, 6, 3000]              126                       --
+  │    │    └─Sequential (block_postskip): 3-51      [1, 12, 3000]             [1, 6, 3000]              522                       --
+  ├─Sequential (clf): 1-4                            [1, 6, 3000]              [1, 6, 1]                 --                        --
+  │    └─Conv1d (0): 2-28                            [1, 6, 3000]              [1, 6, 3000]              42                        [1]
+  │    └─Tanh (1): 2-29                              [1, 6, 3000]              [1, 6, 3000]              --                        --
+  │    └─AvgPool1d (2): 2-30                         [1, 6, 3000]              [1, 6, 1]                 --                        [3000]
+  ├─Sequential (final_layer): 1-5                    [1, 6, 1]                 [1, 5, 1]                 --                        --
+  │    └─Conv1d (0): 2-31                            [1, 6, 1]                 [1, 5, 1]                 35                        [1]
+  │    └─ELU (1): 2-32                               [1, 5, 1]                 [1, 5, 1]                 --                        --
+  │    └─Conv1d (2): 2-33                            [1, 5, 1]                 [1, 5, 1]                 30                        [1]
+  │    └─Identity (3): 2-34                          [1, 5, 1]                 [1, 5, 1]                 --                        --
+  ======================================================================================================================================================
+  Total params: 2,482,011
+  Trainable params: 2,482,011
+  Non-trainable params: 0
+  Total mult-adds (M): 22.43
+  ======================================================================================================================================================
+  Input size (MB): 0.02
+  Forward/backward pass size (MB): 2.91
+  Params size (MB): 9.93
+  Estimated Total Size (MB): 12.86
+  ======================================================================================================================================================,
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_001.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span>
+                      <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Wake&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;REM&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_002.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+           0       0.12      0.31      0.17        64
+           1       0.03      0.05      0.04        22
+           2       0.52      0.34      0.41       197
+           3       0.26      0.20      0.23        84
+           4       0.25      0.03      0.06        32
+
+    accuracy                           0.26       399
+   macro avg       0.24      0.19      0.18       399
+weighted avg       0.35      0.26      0.28       399
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_003.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>Our model was able to learn, as shown by the decreasing training and
+validation loss values, despite the low amount of data that was available
+(only two recordings in this example). To further improve performance, more
+recordings should be included in the training set, the model should be
+trained for more epochs and hyperparameters should be optimized.</p>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id12" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Applied examples on real-world datasets</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+    <li class="breadcrumb-item active" aria-current="page">Process a big data EEG resource (TUH EEG Corpus)</li>
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+              
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="process-a-big-data-eeg-resource-tuh-eeg-corpus">
+<span id="sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"></span><h1>Process a big data EEG resource (TUH EEG Corpus)<a class="headerlink" href="#process-a-big-data-eeg-resource-tuh-eeg-corpus" title="Link to this heading">#</a></h1>
+<p>In this example, we showcase usage of the Temple University Hospital EEG Corpus
+(<a class="reference external" href="https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg">https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg</a>)
+including simple preprocessing steps as well as cutting of compute windows.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#creating-the-dataset-using-tuhmock" id="id1">Creating the dataset using TUHMock</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id2">Preprocessing</a></p>
+<ul>
+<li><p><a class="reference internal" href="#selecting-recordings" id="id3">Selecting recordings</a></p></li>
+<li><p><a class="reference internal" href="#combining-preprocessing-steps" id="id4">Combining preprocessing steps</a></p></li>
+<li><p><a class="reference internal" href="#cut-compute-windows" id="id5">Cut Compute Windows</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">tempfile</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
+</pre></div>
+</div>
+<section id="creating-the-dataset-using-tuhmock">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Creating the dataset using TUHMock</a><a class="headerlink" href="#creating-the-dataset-using-tuhmock" title="Link to this heading">#</a></h2>
+<p>Since the data is not available at the time of the creation of this example,
+we are required to mock some of the dataset functionality. Therefore, if you
+want to try this code with the actual data, please disconsider this section.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
+</pre></div>
+</div>
+<p>Firstly, we start by creating a TUH mock dataset using braindecode’s _TUHMock class.
+The complete code can be found at <a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datasets.TUH()</span></code></a>, but we will give
+a small description of how it works.
+This class is able to read the recordings from TUH_PATH and generate a description
+by parsing information from file paths, such as patient id and recording data.
+THis description can later be accessed by the object’s .description method.
+After that, the files are sorted chronologically by year, month, day,
+patient id, recording session and segment, and then use the description corresponding
+to the specified by recording ids.
+FInally, additional information regarding age and gender of the subjects are parsed
+directly to the description.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
+<span class="c1"># specify the number of jobs for loading and windowing</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a> <span class="o">=</span> <span class="mi">2</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
+    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span> <span class="k">if</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;_TUHMock&#39;</span> <span class="k">else</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We can visualize our data’s statistics using the class’ “description” method</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plt_histogram</span><span class="p">(</span><span class="n">df_of_ages_genders</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">ylim</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">show_title</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="c1"># Dafarame containing info about gender and age of subjects</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df_of_ages_genders</span>
+    <span class="n">male_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;M&#39;</span><span class="p">]</span>
+    <span class="n">female_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;F&#39;</span><span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">18</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">show_title</span><span class="p">:</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle" title="matplotlib.pyplot.suptitle" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span></a><span class="p">(</span><span class="s2">&quot;Age information&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span>
+
+    <span class="c1"># First plot: Male individuals</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span>
+             <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html#matplotlib.pyplot.axhline" title="matplotlib.pyplot.axhline" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axhline</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;mean age </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> &quot;</span>
+                      <span class="sa">f</span><span class="s2">&quot;(±</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html#matplotlib.pyplot.barh" title="matplotlib.pyplot.barh" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">height</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span>
+             <span class="n">width</span><span class="o">=</span><span class="n">ylim</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+    <span class="c1"># Legend</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ylim</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;male (</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">male_df</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%)&quot;</span><span class="p">,</span>
+              <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca" title="matplotlib.pyplot.gca" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">gca</span></a><span class="p">()</span><span class="o">.</span><span class="n">invert_xaxis</span><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tick_params.html#matplotlib.pyplot.tick_params" title="matplotlib.pyplot.tick_params" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+
+    <span class="c1"># First plot: Female individuals</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span>
+             <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html#matplotlib.pyplot.axhline" title="matplotlib.pyplot.axhline" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axhline</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;mean age </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> (&quot;</span>
+                      <span class="sa">f</span><span class="s2">&quot;±</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html#matplotlib.pyplot.barh" title="matplotlib.pyplot.barh" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">height</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span>
+             <span class="n">width</span><span class="o">=</span><span class="n">ylim</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+    <span class="c1"># Label</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ylim</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;female (</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">female_df</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%)&quot;</span><span class="p">,</span>
+              <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="matplotlib.pyplot.ylim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="matplotlib.pyplot.subplots_adjust" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">&quot;age [years]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tick_params.html#matplotlib.pyplot.tick_params" title="matplotlib.pyplot.tick_params" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+
+
+<span class="n">df</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="o">.</span><span class="n">description</span>
+<span class="n">plt_histogram</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" srcset="../../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" alt="Age information, male (60.0%), female (40.0%)" class = "sphx-glr-single-img"/></section>
+<section id="preprocessing">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h2>
+<section id="selecting-recordings">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Selecting recordings</a><a class="headerlink" href="#selecting-recordings" title="Link to this heading">#</a></h3>
+<p>First, we will do some selection of available recordings based on the duration.
+We will select those recordings that have at least five minutes duration.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">select_by_duration</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">inf</span></a>
+    <span class="c1"># determine length of the recordings and select based on tmin and tmax</span>
+    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">d_i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
+        <span class="n">duration</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">/</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+        <span class="c1"># select the ones in the required duration range</span>
+        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">&lt;=</span> <span class="n">duration</span> <span class="o">&lt;=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">:</span>
+            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d_i</span><span class="p">)</span>
+    <span class="n">splits</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)</span>
+    <span class="n">split</span> <span class="o">=</span> <span class="n">splits</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">split</span>
+
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="kc">None</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_duration</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>Next, we will discard all recordings that have an incomplete channel
+configuration on the channels that we are interested. The subdivisions of the
+recordings into ‘le’ and ‘ar’ labels represents the channels for
+the re-referencing of the signals.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;FP1&#39;</span><span class="p">,</span> <span class="s1">&#39;FP2&#39;</span><span class="p">,</span> <span class="s1">&#39;F3&#39;</span><span class="p">,</span> <span class="s1">&#39;F4&#39;</span><span class="p">,</span> <span class="s1">&#39;C3&#39;</span><span class="p">,</span> <span class="s1">&#39;C4&#39;</span><span class="p">,</span> <span class="s1">&#39;P3&#39;</span><span class="p">,</span> <span class="s1">&#39;P4&#39;</span><span class="p">,</span> <span class="s1">&#39;O1&#39;</span><span class="p">,</span> <span class="s1">&#39;O2&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;F7&#39;</span><span class="p">,</span> <span class="s1">&#39;F8&#39;</span><span class="p">,</span> <span class="s1">&#39;T3&#39;</span><span class="p">,</span> <span class="s1">&#39;T4&#39;</span><span class="p">,</span> <span class="s1">&#39;T5&#39;</span><span class="p">,</span> <span class="s1">&#39;T6&#39;</span><span class="p">,</span> <span class="s1">&#39;FZ&#39;</span><span class="p">,</span> <span class="s1">&#39;CZ&#39;</span><span class="p">,</span> <span class="s1">&#39;PZ&#39;</span><span class="p">])</span>
+
+<span class="c1"># TUH data is subdivided into &#39;le&#39; and &#39;ar&#39; recordings references</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;EEG A1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG FP1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG C4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG F7-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG T6-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-REF&#39;</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;EEG A1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG FP1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG C4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG F7-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG T6-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-LE&#39;</span><span class="p">])</span>
+<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ar&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a><span class="p">,</span> <span class="s1">&#39;le&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a><span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">select_by_channels</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">):</span>
+    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
+        <span class="n">ref</span> <span class="o">=</span> <span class="s1">&#39;ar&#39;</span> <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;-REF&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;le&#39;</span>
+        <span class="c1"># these are the channels we are looking for</span>
+        <span class="n">seta</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">[</span><span class="n">ref</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="c1"># these are the channels of the recoding</span>
+        <span class="n">setb</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">)</span>
+        <span class="c1"># if recording contains all channels we are looking for, include it</span>
+        <span class="k">if</span> <span class="n">seta</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">setb</span><span class="p">):</span>
+            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_channels</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="combining-preprocessing-steps">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Combining preprocessing steps</a><a class="headerlink" href="#combining-preprocessing-steps" title="Link to this heading">#</a></h3>
+<p>Next, we use braindecode’s preprocess to combine and execute several preprocessing
+steps that are executed through ‘mne’:</p>
+<ul class="simple">
+<li><p>Crop the recordings to a region of interest</p></li>
+<li><p>Re-reference all recordings to ‘ar’ (requires load)</p></li>
+<li><p>Rename channels to short channel names</p></li>
+<li><p>Pick channels of interest</p></li>
+<li><p>Scale signals to micro volts (requires load)</p></li>
+<li><p>Clip outlier values to +/- 800 micro volts (requires load)</p></li>
+<li><p>Resample recordings to a common frequency (requires load)</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">custom_rename_channels</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">mapping</span><span class="p">):</span>
+    <span class="c1"># rename channels which are dependent on referencing:</span>
+    <span class="c1"># le: EEG 01-LE, ar: EEG 01-REF</span>
+    <span class="c1"># mne fails if the mapping contains channels as keys that are not present</span>
+    <span class="c1"># in the raw</span>
+    <span class="n">reference</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+    <span class="k">assert</span> <span class="n">reference</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;le&#39;</span><span class="p">,</span> <span class="s1">&#39;ref&#39;</span><span class="p">],</span> <span class="s1">&#39;unexpected referencing&#39;</span>
+    <span class="n">reference</span> <span class="o">=</span> <span class="s1">&#39;le&#39;</span> <span class="k">if</span> <span class="n">reference</span> <span class="o">==</span> <span class="s1">&#39;le&#39;</span> <span class="k">else</span> <span class="s1">&#39;ar&#39;</span>
+    <span class="n">raw</span><span class="o">.</span><span class="n">rename_channels</span><span class="p">(</span><span class="n">mapping</span><span class="p">[</span><span class="n">reference</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">custom_crop</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="c1"># crop recordings to tmin – tmax. can be incomplete if recording</span>
+    <span class="c1"># has lower duration than tmax</span>
+    <span class="c1"># by default mne fails if tmax is bigger than duration</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="nb">min</span><span class="p">((</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
+    <span class="n">raw</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="n">include_tmax</span><span class="p">)</span>
+
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_crop</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;set_eeg_reference&#39;</span><span class="p">,</span> <span class="n">ref_channels</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">,</span> <span class="n">ch_type</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_rename_channels</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">,</span>
+                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_channels&#39;</span><span class="p">,</span> <span class="n">ch_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">,</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.clip.html#numpy.clip" title="numpy.clip" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">clip</span></a><span class="p">,</span> <span class="n">a_min</span><span class="o">=-</span><span class="mi">800</span><span class="p">,</span> <span class="n">a_max</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">),</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+</pre></div>
+</div>
+<p>Next, we can apply the defined preprocessors on the selected recordings in parallel.
+We additionally use the serialization functionality of
+<a class="reference internal" href="../../source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> to limit memory usage during
+preprocessing, as each file must be loaded into memory for some of the
+preprocessing steps to work.
+This also makes it possible to use the lazy
+loading capabilities of <a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a>, as
+the preprocessed data is automatically reloaded with <code class="docutils literal notranslate"><span class="pre">preload=False</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Here we use <code class="docutils literal notranslate"><span class="pre">n_jobs=2</span></code> as the machines the documentation is build on
+only have two cores. This number should be modified based on the machine
+that is available for preprocessing.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># please insert actual output directory here</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
+    <span class="n">save_dir</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="cut-compute-windows">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Cut Compute Windows</a><a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<p>We can finally generate compute windows. The resulting dataset is now ready
+to be used for model training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Generate compute windows here and store them to disk</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 7.601 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  12 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-tuh-eeg-corpus-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tuh_eeg_corpus.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tuh_eeg_corpus.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+</section>
+
+
+  
+</div>
+
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
+  
+    <div class="footer-article-item"><!-- Previous / next buttons -->
+<div class="prev-next-area">
+    <a class="left-prev"
+       href="plot_sleep_staging_usleep.html"
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+        <p class="prev-next-subtitle">previous</p>
+        <p class="prev-next-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</p>
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+  <div class="page-toc tocsection onthispage">
+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-the-dataset-using-tuhmock">Creating the dataset using TUHMock</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#selecting-recordings">Selecting recordings</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#combining-preprocessing-steps">Combining preprocessing steps</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-compute-windows">Cut Compute Windows</a></li>
+</ul>
+</li>
+</ul>
+  </nav></div>
+
+</div></div>
+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
+          </footer>
+        
+      </main>
+    </div>
+  </div>
+  
+    
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="../../_static/scripts/bootstrap.js?digest=e353d410970836974a52"></script>
+<script src="../../_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52"></script>
+
+    <script src="https://braindecode.org/versionwarning.js"></script>
+    
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
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+    <div class="footer-items__start">
+      
+        <div class="footer-item"><p class="text-center text-muted small">&copy; Copyright 2018–2023, Braindecode Developers.</p></div>
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+    <div class="footer-items__end">
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+  Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.13.3.
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\ No newline at end of file
diff --git a/0.8/auto_examples/applied_examples/sg_execution_times.html b/0.8/auto_examples/applied_examples/sg_execution_times.html
new file mode 100644
index 00000000..ceb00b66
--- /dev/null
+++ b/0.8/auto_examples/applied_examples/sg_execution_times.html
@@ -0,0 +1,500 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>Computation times &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
+  
+  <!-- Loaded before other Sphinx assets -->
+  <link href="../../_static/styles/theme.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="../../_static/styles/bootstrap.css?digest=e353d410970836974a52" rel="stylesheet" />
+<link href="../../_static/styles/pydata-sphinx-theme.css?digest=e353d410970836974a52" rel="stylesheet" />
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+              
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+  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-applied-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>01:12.147</strong> total execution time for <strong>auto_examples_applied_examples</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_chambon2018.py</span></code>)</p></td>
+<td><p>00:21.296</p></td>
+<td><p>11.9 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code>)</p></td>
+<td><p>00:20.608</p></td>
+<td><p>317.1 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_usleep.py</span></code>)</p></td>
+<td><p>00:11.429</p></td>
+<td><p>40.7 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_eldele2021.py</span></code>)</p></td>
+<td><p>00:11.212</p></td>
+<td><p>14.5 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_eeg_corpus.py</span></code>)</p></td>
+<td><p>00:07.601</p></td>
+<td><p>12.4 MB</p></td>
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diff --git a/stable/auto_examples/benchmark_lazy_eager_loading.html b/0.8/auto_examples/datasets_io/benchmark_lazy_eager_loading.html
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
 </ul>
-
-  </div>
-</nav>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Loading and organizing data</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Benchmarking eager and lazy loading</li>
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+    </div>
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+  
   <div class="sphx-glr-download-link-note admonition note">
 <p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">here</span></a>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Go to the end</span></a>
 to download the full example code</p>
 </div>
 <section class="sphx-glr-example-title" id="benchmarking-eager-and-lazy-loading">
-<span id="sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"></span><h1>Benchmarking eager and lazy loading<a class="headerlink" href="#benchmarking-eager-and-lazy-loading" title="Permalink to this heading">#</a></h1>
+<span id="sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"></span><h1>Benchmarking eager and lazy loading<a class="headerlink" href="#benchmarking-eager-and-lazy-loading" title="Link to this heading">#</a></h1>
 <p>In this example, we compare the execution time and memory requirements of 1)
 eager loading, i.e., preloading the entire data into memory and 2) lazy loading,
 i.e., only loading examples from disk when they are required. We also include
@@ -420,7 +483,7 @@
 when they are required. This means large datasets can be used for training,
 however this introduces some file-reading overhead every time an example must
 be extracted. Some preprocessing steps that require continuous data also have to
-be implemented differently to accomodate the nature of windowed data. Overall
+be implemented differently to accommodate the nature of windowed data. Overall
 though, we can reduce the impact of lazy loading by using the <cite>num_workers</cite>
 parameter of pytorch’s <cite>Dataloader</cite> class, which dispatches the data loading to
 multiple processes.</p>
@@ -443,12 +506,12 @@
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
 
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">,</span> <a href="../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a>
 
 
-<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;WARNING&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages everytime a window is extracted</span>
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;WARNING&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
 </pre></div>
 </div>
 <p>We start by setting two pytorch internal parameters that can affect the
@@ -457,7 +520,7 @@
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">N_JOBS</span> <span class="o">=</span> <span class="mi">8</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># Enables automatic algorithm optimizations</span>
+<a href="https://pytorch.org/docs/stable/backends.html#torch.backends.cudnn.benchmark" title="torch.backends.cudnn.benchmark" class="sphx-glr-backref-module-torch-backends-cudnn sphx-glr-backref-type-py-attribute"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># Enables automatic algorithm optimizations</span>
 <a href="https://pytorch.org/docs/stable/generated/torch.set_num_threads.html#torch.set_num_threads" title="torch.set_num_threads" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span></a><span class="p">(</span><span class="n">N_JOBS</span><span class="p">)</span>  <span class="c1"># Sets the available number of threads</span>
 </pre></div>
 </div>
@@ -468,7 +531,7 @@
 <p>Each one of these steps will be timed, so we can report the total time taken
 to prepare the data and train the model.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">load_example_data</span><span class="p">(</span><span class="n">preload</span><span class="p">,</span> <span class="n">window_len_s</span><span class="p">,</span> <span class="n">n_recordings</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create windowed dataset from subjects of the TUH Abnormal dataset.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create windowed dataset from subjects of the TUH Abnormal dataset.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -492,7 +555,7 @@
 
     <span class="n">recording_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_recordings</span><span class="p">))</span>
 
-    <span class="n">ds</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a><span class="p">(</span>
+    <span class="n">ds</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a><span class="p">(</span>
         <span class="n">TUH_PATH</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span>
         <span class="n">target_name</span><span class="o">=</span><span class="s1">&#39;pathological&#39;</span><span class="p">,</span>
         <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
@@ -501,11 +564,11 @@
     <span class="n">window_len_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs</span> <span class="o">*</span> <span class="n">window_len_s</span><span class="p">)</span>
     <span class="n">window_stride_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)</span>
     <span class="c1"># window_stride_samples = int(fs * window_len_s)</span>
-    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
         <span class="n">ds</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">window_size_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
         <span class="n">window_stride_samples</span><span class="o">=</span><span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
 
     <span class="c1"># Drop bad epochs</span>
     <span class="c1"># XXX: This could be parallelized.</span>
@@ -519,7 +582,7 @@
 
 <span class="k">def</span> <span class="nf">create_example_model</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">window_len_samples</span><span class="p">,</span>
                          <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;shallow&#39;</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create model, loss and optimizer.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create model, loss and optimizer.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -544,14 +607,14 @@
 <span class="sd">        Optimizer</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">&#39;shallow&#39;</span><span class="p">:</span>
-        <span class="n">model</span> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+        <span class="n">model</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
             <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
             <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
             <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
             <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
             <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
     <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">&#39;deep&#39;</span><span class="p">:</span>
-        <span class="n">model</span> <span class="o">=</span> <a href="../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a><span class="p">(</span>
+        <span class="n">model</span> <span class="o">=</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a><span class="p">(</span>
             <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
             <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
             <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
@@ -573,7 +636,7 @@
 
 
 <span class="k">def</span> <span class="nf">run_training</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Run training loop.</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run training loop.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -611,7 +674,7 @@
             <span class="n">loss_val</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
             <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
 
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Epoch </span><span class="si">{</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s1"> - mean training loss: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">loss_vals</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Epoch </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1"> - mean training loss: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">loss_vals</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="n">model</span>
 </pre></div>
@@ -657,7 +720,7 @@
         <span class="s1">&#39;pin_memory&#39;</span><span class="p">:</span> <span class="n">pin_memory</span><span class="p">,</span>
         <span class="s1">&#39;cuda&#39;</span><span class="p">:</span> <span class="n">cuda</span>
     <span class="p">}</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Repetition </span><span class="si">{</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">N_REPETITIONS</span><span class="si">}</span><span class="s1">:</span><span class="se">\n</span><span class="si">{</span><span class="n">results</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Repetition </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">N_REPETITIONS</span><span class="si">}</span><span class="s1">:</span><span class="se">\n</span><span class="si">{</span><span class="n">results</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
 
     <span class="c1"># Load the dataset</span>
     <span class="n">data_loading_start</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
@@ -716,95 +779,96 @@
 during model training, but to potentially be pretty competitive if multiple
 workers were enabled (i.e.., <cite>num_workers &gt; 0</cite>). Training on a CUDA device
 should also yield substantial speedups.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.000 seconds)</p>
 <p><strong>Estimated memory usage:</strong>  0 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-benchmark-lazy-eager-loading-py">
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-benchmark-lazy-eager-loading-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/2a53ed6bee60a29af3f760043d35ed09/benchmark_lazy_eager_loading.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">benchmark_lazy_eager_loading.py</span></code></a></p>
+<p><a class="reference download internal" download="" href="../../_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">benchmark_lazy_eager_loading.py</span></code></a></p>
 </div>
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-<p><a class="reference download internal" download="" href="../_downloads/25408d8d92a873e370d4750ba414b84a/benchmark_lazy_eager_loading.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">benchmark_lazy_eager_loading.ipynb</span></code></a></p>
+<p><a class="reference download internal" download="" href="../../_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">benchmark_lazy_eager_loading.ipynb</span></code></a></p>
 </div>
 </div>
 <p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/0.8/auto_examples/datasets_io/index.html b/0.8/auto_examples/datasets_io/index.html
new file mode 100644
index 00000000..50b5f105
--- /dev/null
+++ b/0.8/auto_examples/datasets_io/index.html
@@ -0,0 +1,571 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>Loading and organizing data &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
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+  <!-- Loaded before other Sphinx assets -->
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+<p>Examples introducing data loading and basic data processing.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
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+<p class="admonition-title">Note</p>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-preprocessing-with-parallelization-and-serialization">
+<span id="sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"></span><h1>Benchmarking preprocessing with parallelization and serialization<a class="headerlink" href="#benchmarking-preprocessing-with-parallelization-and-serialization" title="Link to this heading">#</a></h1>
+<p>In this example, we compare the execution time and memory requirements of
+preprocessing data with the parallelization and serialization functionalities
+available in <a class="reference internal" href="../../source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a>.</p>
+<p>We compare 4 cases:</p>
+<ol class="arabic simple">
+<li><p>Sequential, no serialization</p></li>
+<li><p>Sequential, with serialization</p></li>
+<li><p>Parallel, no serialization</p></li>
+<li><p>Parallel, with serialization</p></li>
+</ol>
+<p>Case 1 is the simplest approach, in which all recordings in a
+<a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> are preprocessed one after the
+other. In this scenario, <a class="reference internal" href="../../source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> acts
+inplace, which means memory usage will likely stay stable (depending on the
+preprocessing operations) if recordings have been preloaded. However, two
+potential issues arise when working with large datasets: (1) if recordings have
+not been preloaded before preprocessing, <cite>preprocess()</cite> will need to load them
+and keep them in memory, in which case memory can become a bottleneck, and (2)
+sequential preprocessing can take a considerable amount of time to run when
+working with many recordings.</p>
+<p>A solution to the first issue (memory usage) is to save the preprocessed data
+to a file so it can be cleared from memory before moving on to the next
+recording (case 2). The recordings can then be reloaded with <cite>preload=False</cite>
+once they have all been saved to disk. This enables using the lazy loading
+capabilities of <a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> and avoids
+potential memory bottlenecks. The downside is that the writing to disk can take
+some time and of course requires disk space.</p>
+<p>A solution to the second issue (slow preprocessing) is to parallelize the
+preprocessing over multiple cores whenever possible (case 3). This can speed up
+preprocessing significantly. However, this approach will increase memory usage
+because of the way parallelization is implemented internally (with
+<cite>joblib</cite>, copies of (part of) the data must be made when sending arguments to
+parallel processes).</p>
+<p>Finally, case 4 (combining parallelization and serialization) is likely to be
+both fast and memory efficient. As shown in this example, this remains a
+tradeoff though, and the selected configuration should depend on the size of
+the dataset and the specific operations applied to the recordings.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">tempfile</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a>
+<span class="kn">from</span> <span class="nn">memory_profiler</span> <span class="kn">import</span> <span class="n">memory_usage</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>We create a function that goes through the usual three steps of data
+preparation: (1) data loading, (2) continuous data preprocessing,
+(3) windowing and (4) windowed data preprocessing. We use the
+<a class="reference internal" href="../../source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> dataset for testing purposes.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">):</span>
+    <span class="k">if</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">:</span>
+        <span class="n">tmp_dir</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory" title="tempfile.TemporaryDirectory" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-class"><span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span></a><span class="p">()</span>
+        <span class="n">save_dir</span> <span class="o">=</span> <span class="n">tmp_dir</span><span class="o">.</span><span class="n">name</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">save_dir</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># (1) Load the data</span>
+    <span class="n">concat_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+        <span class="n">subject_ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">),</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
+    <span class="n">sfreq</span> <span class="o">=</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+
+    <span class="c1"># (2) Preprocess the continuous data</span>
+    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span>
+        <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+        <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="p">]</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+
+    <span class="c1"># (3) Window the data</span>
+    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+        <span class="n">concat_ds</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+
+    <span class="c1"># Preprocess the windowed data</span>
+    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>Next, we can run our function and measure its run time and peak memory usage
+for each one of our 4 cases above. We call the function multiple times with
+each configuration to get better estimates.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To better characterize the run time vs. memory usage tradeoff for your
+specific configuration (as this will differ based on available hardware,
+data size and preprocessing operations), we recommend adapting this example
+to your use case and running it on your machine.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Number of repetitions</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Number of recordings to load and preprocess</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>  <span class="c1"># Number of parallel processes</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a><span class="p">(</span>
+        <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a><span class="p">),</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="p">],</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">):</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">memory_usage</span><span class="p">(</span>
+        <span class="n">proc</span><span class="o">=</span><span class="p">(</span><span class="n">prepare_data</span><span class="p">,</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="p">{})))</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a>
+
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="o">.</span><span class="n">append</span><span class="p">({</span>
+        <span class="s1">&#39;n_recs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span>
+        <span class="s1">&#39;max_mem&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a><span class="p">,</span>
+        <span class="s1">&#39;save&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span>
+        <span class="s1">&#39;n_jobs&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">,</span>
+        <span class="s1">&#39;time&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a>
+    <span class="p">})</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmpwm3jj9yv/0/0-raw.fif
+Closing /tmp/tmpwm3jj9yv/0/0-raw.fif
+[done]
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmpwm3jj9yv/1/1-raw.fif
+Closing /tmp/tmpwm3jj9yv/1/1-raw.fif
+[done]
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwm3jj9yv contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmpwm3jj9yv/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmpwm3jj9yv/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Loading data for 835 events and 3000 original time points ...
+0 bad epochs dropped
+Loading data for 1086 events and 3000 original time points ...
+0 bad epochs dropped
+Loading data for 835 events and 3000 original time points ...
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:566: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwm3jj9yv contains other subdirectories or files [&#39;1&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Loading data for 1086 events and 3000 original time points ...
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwm3jj9yv contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Reading /tmp/tmpwm3jj9yv/0/0-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+835 matching events found
+No baseline correction applied
+0 projection items activated
+Reading /tmp/tmpwm3jj9yv/1/1-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+1086 matching events found
+No baseline correction applied
+0 projection items activated
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Opening raw data file /tmp/tmps0okxq7l/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmps0okxq7l/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Reading /tmp/tmps0okxq7l/0/0-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+835 matching events found
+No baseline correction applied
+0 projection items activated
+Reading /tmp/tmps0okxq7l/1/1-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+1086 matching events found
+No baseline correction applied
+0 projection items activated
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Using data from preloaded Raw for 835 events and 3000 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 1086 events and 3000 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 835 events and 3000 original time points ...
+Using data from preloaded Raw for 1086 events and 3000 original time points ...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmpw6ld5zmx/0/0-raw.fif
+Closing /tmp/tmpw6ld5zmx/0/0-raw.fif
+[done]
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmpw6ld5zmx/1/1-raw.fif
+Closing /tmp/tmpw6ld5zmx/1/1-raw.fif
+[done]
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpw6ld5zmx contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmpw6ld5zmx/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmpw6ld5zmx/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Loading data for 835 events and 3000 original time points ...
+0 bad epochs dropped
+Loading data for 1086 events and 3000 original time points ...
+0 bad epochs dropped
+Loading data for 835 events and 3000 original time points ...
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:566: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpw6ld5zmx contains other subdirectories or files [&#39;1&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Loading data for 1086 events and 3000 original time points ...
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpw6ld5zmx contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Reading /tmp/tmpw6ld5zmx/0/0-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+835 matching events found
+No baseline correction applied
+0 projection items activated
+Reading /tmp/tmpw6ld5zmx/1/1-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+1086 matching events found
+No baseline correction applied
+0 projection items activated
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Opening raw data file /tmp/tmp7ty3c7sx/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmp7ty3c7sx/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Reading /tmp/tmp7ty3c7sx/0/0-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+835 matching events found
+No baseline correction applied
+0 projection items activated
+Reading /tmp/tmp7ty3c7sx/1/1-epo.fif ...
+Isotrak not found
+    Found the data of interest:
+        t =       0.00 ...   29990.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+1086 matching events found
+No baseline correction applied
+0 projection items activated
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Using data from preloaded Raw for 835 events and 3000 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 1086 events and 3000 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 835 events and 3000 original time points ...
+Using data from preloaded Raw for 1086 events and 3000 original time points ...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/bru/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+</pre></div>
+</div>
+<p>Finally, we can plot the results:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span> <span class="s1">&#39;tab:orange&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span> <span class="s1">&#39;tab:blue&#39;</span><span class="p">}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">m</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">])}</span>
+<span class="k">for</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">),</span> <span class="n">sub_df</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;save&#39;</span><span class="p">,</span> <span class="s1">&#39;n_jobs&#39;</span><span class="p">]):</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;max_mem&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">],</span>
+               <span class="n">marker</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;save=</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="si">}</span><span class="s1">, n_jobs=</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Execution time (s)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Memory usage (MiB)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Loading and preprocessing </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="si">}</span><span class="s1"> recordings from Sleep &#39;</span>
+             <span class="s1">&#39;Physionet&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_benchmark_preprocessing_001.png" srcset="../../_images/sphx_glr_plot_benchmark_preprocessing_001.png" alt="Loading and preprocessing 2 recordings from Sleep Physionet" class = "sphx-glr-single-img"/><p>We see that parallel preprocessing without serialization (blue crosses) is
+faster than simple sequential processing (blue circles), however it uses more
+memory.</p>
+<p>Combining parallel preprocessing and serialization (orange crosses) reduces
+memory usage significantly, however it increases run time by a few seconds.
+Depending on available resources (e.g. in limited memory settings), it might
+therefore be more advantageous to use both parallelization and serialization
+together.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 14.203 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  115 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-benchmark-preprocessing-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark_preprocessing.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark_preprocessing.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+  
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+        <p class="prev-next-title">Benchmarking eager and lazy loading</p>
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="custom-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"></span><h1>Custom Dataset Example<a class="headerlink" href="#custom-dataset-example" title="Link to this heading">#</a></h1>
+<p>This example shows how to convert data X and y as numpy arrays to a braindecode
+compatible data format.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a>
+</pre></div>
+</div>
+<p>To set up the example, we first fetch some data using mne:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
+<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
+
+<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
+<span class="c1"># and then return the paths to the files.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Load each of the files</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
+         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+</pre></div>
+</div>
+<p>We take the required data, targets and additional information sampling
+frequency and channel names from the loaded data. Note that this data and
+information can originate from any source.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">raw</span><span class="o">.</span><span class="n">get_data</span><span class="p">()</span> <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;ch_names&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Convert to data format compatible with skorch and braindecode:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>  <span class="c1"># look as dataset description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+Using data from preloaded Raw for 40 events and 500 original time points ...
+0 bad epochs dropped
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>target</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>14</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>You can manipulate the dataset</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">))</span>  <span class="c1"># get the number of samples</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>240
+</pre></div>
+</div>
+<p>You can now index the data</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span><span class="o">.</span><span class="n">shape</span></a>  <span class="c1"># the EEG data</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;n_channels=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="si">}</span><span class="s2">  -- n_times=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a><span class="si">}</span><span class="s2"> -- y_i=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;start_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="si">}</span><span class="s2"> -- stop_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using data from preloaded Raw for 1 events and 500 original time points ...
+n_channels=64  -- n_times=500 -- y_i=5
+start_ind=0 -- stop_ind=500
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.589 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-custom-dataset-example-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_dataset_example.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Load and save dataset example</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="load-and-save-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"></span><h1>Load and save dataset example<a class="headerlink" href="#load-and-save-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we show how to load and save braindecode datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">tempfile</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datautil</span> <span class="kn">import</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+</pre></div>
+</div>
+<p>First, we load some dataset using MOABB.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span>
+    <span class="n">dataset_name</span><span class="o">=</span><span class="s1">&#39;BNCI2014001&#39;</span><span class="p">,</span>
+    <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+</pre></div>
+</div>
+<p>We can apply preprocessing steps to the dataset. It is also possible to skip
+this step and not apply any preprocessing.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">preprocessors</span><span class="o">=</span><span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">fn</span><span class="o">=</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">10</span><span class="p">)]</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7fe8652c0f10&gt;
+</pre></div>
+</div>
+<p>We save the dataset to a an existing directory. It will create a ‘.fif’ file
+for every dataset in the concat dataset. Additionally it will create two
+JSON files, the first holding the description of the dataset, the second
+holding the name of the target. If you want to store to the same directory
+several times, for example due to trying different preprocessing, you can
+choose to overwrite the existing files.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># write in a temporary directory</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Writing /tmp/tmp6jixnyvl/0/0-raw.fif
+Closing /tmp/tmp6jixnyvl/0/0-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/1/1-raw.fif
+Closing /tmp/tmp6jixnyvl/1/1-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/2/2-raw.fif
+Closing /tmp/tmp6jixnyvl/2/2-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/3/3-raw.fif
+Closing /tmp/tmp6jixnyvl/3/3-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/4/4-raw.fif
+Closing /tmp/tmp6jixnyvl/4/4-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/5/5-raw.fif
+Closing /tmp/tmp6jixnyvl/5/5-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/6/6-raw.fif
+Closing /tmp/tmp6jixnyvl/6/6-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/7/7-raw.fif
+Closing /tmp/tmp6jixnyvl/7/7-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/8/8-raw.fif
+Closing /tmp/tmp6jixnyvl/8/8-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/9/9-raw.fif
+Closing /tmp/tmp6jixnyvl/9/9-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/10/10-raw.fif
+Closing /tmp/tmp6jixnyvl/10/10-raw.fif
+[done]
+Writing /tmp/tmp6jixnyvl/11/11-raw.fif
+Closing /tmp/tmp6jixnyvl/11/11-raw.fif
+[done]
+</pre></div>
+</div>
+<p>We load the saved dataset from a directory. Signals can be preloaded in
+compliance with mne. Optionally, only specific ‘.fif’ files can be loaded
+by specifying their ids. The target name can be changed, if the dataset
+supports it (TUHAbnormal for example supports ‘pathological’, ‘age’, and
+‘gender’. If you stored a preprocessed version with target ‘pathological’
+it is possible to change the target upon loading).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">ids_to_load</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opening raw data file /tmp/tmp6jixnyvl/1/1-raw.fif...
+    Range : 0 ... 3868 =      0.000 ...   386.800 secs
+Ready.
+Reading 0 ... 3868  =      0.000 ...   386.800 secs...
+Opening raw data file /tmp/tmp6jixnyvl/3/3-raw.fif...
+    Range : 0 ... 3868 =      0.000 ...   386.800 secs
+Ready.
+Reading 0 ... 3868  =      0.000 ...   386.800 secs...
+</pre></div>
+</div>
+<p>The serialization utility also supports WindowsDatasets, so we create
+compute windows next.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Using data from preloaded Raw for 48 events and 40 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Using data from preloaded Raw for 48 events and 40 original time points ...
+0 bad epochs dropped
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_1</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_3</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>Again, we save the dataset to an existing directory. It will create a
+‘-epo.fif’ file for every dataset in the concat dataset. Additionally it
+will create a JSON file holding the description of the dataset. If you
+want to store to the same directory several times, for example due to
+trying different windowing parameters, you can choose to overwrite the
+existing files.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using data from preloaded Raw for 1 events and 40 original time points ...
+Using data from preloaded Raw for 48 events and 40 original time points ...
+Using data from preloaded Raw for 1 events and 40 original time points ...
+Using data from preloaded Raw for 48 events and 40 original time points ...
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:566: UserWarning: The number of saved datasets (2) does not match the number of existing subdirectories (12). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/bru/PycharmProjects/braindecode-new/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmp6jixnyvl contains other subdirectories or files [&#39;11&#39;, &#39;2&#39;, &#39;10&#39;, &#39;3&#39;, &#39;4&#39;, &#39;6&#39;, &#39;7&#39;, &#39;5&#39;, &#39;8&#39;, &#39;9&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+</pre></div>
+</div>
+<p>Load the saved dataset from a directory. Signals can be preloaded in
+compliance with mne. Optionally, only specific ‘-epo.fif’ files can be
+loaded by specifying their ids.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">ids_to_load</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Reading /tmp/tmp6jixnyvl/0/0-epo.fif ...
+    Found the data of interest:
+        t =       0.00 ...    3900.00 ms
+        0 CTF compensation matrices available
+Adding metadata with 4 columns
+48 matching events found
+No baseline correction applied
+0 projection items activated
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
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+      
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+        
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+              
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+<div class="header-article-items header-article__inner">
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+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">MNE Dataset Example</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="mne-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"></span><h1>MNE Dataset Example<a class="headerlink" href="#mne-dataset-example" title="Link to this heading">#</a></h1>
+<p>This example shows how to convert data from mne.Raws or mne.Epochs to a
+braindecode compatible data format.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>First, fetch some data using mne:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
+<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
+
+<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
+<span class="c1"># and then return the paths to the files.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Load each of the files</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
+         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/bru/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+</pre></div>
+</div>
+<p>Convert Raw objects to a compatible data format:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;event_code&quot;</span><span class="p">:</span> <span class="n">code</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">}</span>
+                <span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+</pre></div>
+</div>
+<p>If trials were already cut beforehand and are available as mne.Epochs:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+                  <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Not setting metadata
+1 matching events found
+No baseline correction applied
+0 projection items activated
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+Creating RawArray with float64 data, n_channels=64, n_times=81
+    Range : 0 ... 80 =      0.000 ...     0.500 secs
+Ready.
+Adding metadata with 4 columns
+2 matching events found
+No baseline correction applied
+0 projection items activated
+Using data from preloaded Raw for 2 events and 50 original time points ...
+0 bad epochs dropped
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>0</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>None</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>None</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>None</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>None</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>None</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>None</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.745 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-mne-dataset-example-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mne_dataset_example.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
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+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">MOABB Dataset Example</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="moabb-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"></span><h1>MOABB Dataset Example<a class="headerlink" href="#moabb-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we show how to fetch and prepare a MOABB dataset for usage
+with Braindecode.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#          Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
+<span class="c1">#          Daniel Wilson &lt;dan.c.wil@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+</pre></div>
+</div>
+<p>First, we create a dataset based on BCIC IV 2a fetched with MOABB,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+</pre></div>
+</div>
+<p>The dataset has a pandas DataFrame with additional description of its internal datasets</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>session_T</td>
+      <td>run_5</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_0</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_1</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_2</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_3</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_4</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>1</td>
+      <td>session_E</td>
+      <td>run_5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>We can iterate through dataset which yields one time point of a continuous signal x,
+and a target y (which can be None if targets are not defined for the entire
+continuous signal).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="k">break</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(26, 1) None
+</pre></div>
+</div>
+<p>We can apply preprocessing transforms that are defined in mne and work
+in-place, such as resampling, bandpass filtering, or electrode selection.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>250.0
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+100.0
+</pre></div>
+</div>
+<p>We can easily split the dataset based on a criteria applied to the description
+DataFrame:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">({</span><span class="n">subset_name</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span> <span class="k">for</span> <span class="n">subset_name</span><span class="p">,</span> <span class="n">subset</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;session_E&#39;: 232164, &#39;session_T&#39;: 232164}
+</pre></div>
+</div>
+<p>See our <a class="reference external" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Trialwise Decoding</a> and
+<a class="reference external" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding</a> examples for
+training with this dataset.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.399 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  125 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-moabb-dataset-example-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_moabb_dataset_example.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_moabb_dataset_example.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+  
+</div>
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diff --git a/0.8/auto_examples/datasets_io/plot_split_dataset.html b/0.8/auto_examples/datasets_io/plot_split_dataset.html
new file mode 100644
index 00000000..936fac2e
--- /dev/null
+++ b/0.8/auto_examples/datasets_io/plot_split_dataset.html
@@ -0,0 +1,1066 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
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+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>Split Dataset Example &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
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+  <!-- Loaded before other Sphinx assets -->
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+<li class="toctree-l2"><a class="reference internal" href="benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_custom_dataset_example.html">Custom Dataset Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<p class="admonition-title">Note</p>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="split-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"></span><h1>Split Dataset Example<a class="headerlink" href="#split-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we aim to show multiple ways of how you can split your datasets for
+training, testing, and evaluating your models.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-the-dataset" id="id1">Loading the dataset</a></p></li>
+<li><p><a class="reference internal" href="#splitting" id="id2">Splitting</a></p>
+<ul>
+<li><p><a class="reference internal" href="#by-description-information" id="id3">By description information</a></p></li>
+<li><p><a class="reference internal" href="#by-row-index" id="id4">By row index</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#observation" id="id5">Observation</a></p></li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+</pre></div>
+</div>
+<section id="loading-the-dataset">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Loading the dataset</a><a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h2>
+<p>Firstly, we create a dataset using the braindecode class &lt;MOABBDataset&gt; to load
+it fetched from MOABB. In this example, we’re using Dataset 2a from BCI
+Competition IV.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="splitting">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Splitting</a><a class="headerlink" href="#splitting" title="Link to this heading">#</a></h2>
+<section id="by-description-information">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">By description information</a><a class="headerlink" href="#by-description-information" title="Link to this heading">#</a></h3>
+<p>The class &lt;MOABBDataset&gt; has a pandas DataFrame containing additional
+description of its internal datasets, which can be used to help splitting the data
+based on recording information, such as subject, session, and run of each trial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>Here, we’re splitting the data based on different runs. The method split returns
+a dictionary with string keys corresponding to unique entries in the description
+DataFrame column.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4215eef7c0&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41edd9b7c0&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41ed644dc0&gt;, &#39;3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41ed644760&gt;, &#39;4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4218ff5670&gt;, &#39;5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4216e7fb50&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /></section>
+<section id="by-row-index">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">By row index</a><a class="headerlink" href="#by-row-index" title="Link to this heading">#</a></h3>
+<p>Another way we can split the dataset is based on a list of integers corresponding to
+rows in the description. In this case, the returned dictionary will have
+‘0’ as the only key.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41f52db2e0&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>However, if we want multiple splits based on indices, we can also define a list
+containing lists of integers. In this case, the dictionary will have string keys
+representing the index of the dataset split in the order of the given list of
+integers.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41eda85670&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41f52db4f0&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41f52db0a0&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>You can also name each split in the output dictionary by specifying the keys
+of each list of indexes in the input dictionary:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;valid&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s2">&quot;test&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]}</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41ed644760&gt;, &#39;valid&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4216e7ff70&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4218f23a60&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /></section>
+</section>
+<section id="observation">
+<h2><a class="toc-backref" href="#id5" role="doc-backlink">Observation</a><a class="headerlink" href="#observation" title="Link to this heading">#</a></h2>
+<p>Similarly, we can split datasets after creating windows using the same methods.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Splitting by different runs</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Using description info&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="c1"># Splitting by row index</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Splitting by row index&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Multiple row index split&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="c1"># Specifying output&#39;s keys</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Specifying keys&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">test</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using description info
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42175d8970&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42175d8b80&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42175d8d90&gt;, &#39;3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4217525250&gt;, &#39;4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4217525670&gt;, &#39;5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42174c5af0&gt;}
+
+Splitting by row index
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f421d2921f0&gt;}
+
+Multiple row index split
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4217525250&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42175d8d90&gt;}
+
+Specifying keys
+{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f41ed644760&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f42175d8b80&gt;}
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.031 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-split-dataset-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_split_dataset.py</span></code></a></p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-dataset">Loading the dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#splitting">Splitting</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#by-description-information">By description information</a></li>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="multiple-discrete-targets-with-the-tuh-eeg-corpus">
+<span id="sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"></span><h1>Multiple discrete targets with the TUH EEG Corpus<a class="headerlink" href="#multiple-discrete-targets-with-the-tuh-eeg-corpus" title="Link to this heading">#</a></h1>
+<dl class="simple">
+<dt>Welcome to this tutorial where we demonstrate how to work with multiple discrete</dt><dd><p>targets for each recording in the TUH EEG Corpus. We’ll guide you through the
+process step by step.</p>
+</dd>
+</dl>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+
+<span class="c1"># Setting Logging Level</span>
+<span class="c1"># ----------------------</span>
+<span class="c1">#</span>
+<span class="c1"># We&#39;ll set the logging level to &#39;ERROR&#39; to avoid excessive messages when</span>
+<span class="c1"># extracting windows:</span>
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
+</pre></div>
+</div>
+<p>If you want to try this code with the actual data, please delete the next
+section. We are required to mock some dataset functionality, since the data
+is not available at creation time of this example.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
+</pre></div>
+</div>
+<section id="creating-temple-university-hospital-tuh-eeg-corpus-dataset">
+<h2>Creating Temple University Hospital (TUH) EEG Corpus Dataset<a class="headerlink" href="#creating-temple-university-hospital-tuh-eeg-corpus-dataset" title="Link to this heading">#</a></h2>
+<p>We start by creating a TUH dataset. Instead of just a <a href="#id1"><span class="problematic" id="id2">`</span></a>str, we give it
+multiple strings as target names. Each of the strings has to exist as a
+column in the description DataFrame.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
+<span class="n">tuh</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
+    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">),</span>  <span class="c1"># use both age and gender as decoding target</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tuh</span><span class="o">.</span><span class="n">description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>                                                path version  year  month  day  subject  session  segment  age gender
+0  tuh_eeg/v1.1.0/edf/02_tcp_le/000/00000058/s001...  v1.1.0  2003      2    5       58        1        0    0      M
+1  tuh_eeg/v1.1.0/edf/01_tcp_ar/099/00009932/s004...  v1.1.0  2014      9   30     9932        4       13   53      F
+2  tuh_eeg/v1.1.0/edf/03_tcp_ar_a/123/00012331/s0...  v1.1.0  2014     12   14    12331        3        2   39      M
+3  tuh_eeg/v1.1.0/edf/01_tcp_ar/000/00000000/s001...  v1.1.0  2015     12   30        0        1        0   37      M
+4  tuh_eeg/v1.2.0/edf/03_tcp_ar_a/149/00014928/s0...  v1.2.0  2016      1   15    14928        4        7   83      F
+</pre></div>
+</div>
+</section>
+<section id="exploring-data">
+<h2>Exploring Data<a class="headerlink" href="#exploring-data" title="Link to this heading">#</a></h2>
+<p>Iterating through the dataset gives <cite>x</cite> as an ndarray with shape
+<cite>(n_channels x 1)</cite> and <cite>y</cite> as a list containing <cite>[age of the subject, gender
+of the subject]</cite>.
+Let’s look at the last example as it has more interesting age/gender labels
+(compare to the last row of the dataframe above).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <span class="n">tuh</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x=array([[-0.92922518],
+       [ 1.22477157],
+       [ 0.12493701],
+       [ 1.28417889],
+       [ 0.13578887],
+       [-1.56835277],
+       [ 1.25153176],
+       [-1.7638324 ],
+       [ 0.14345092],
+       [-0.30167542],
+       [ 0.68056549],
+       [ 1.70380673],
+       [-0.70052629],
+       [-1.21543634],
+       [ 0.96330007],
+       [ 0.02736802],
+       [ 1.65902848],
+       [-0.15756383],
+       [ 0.38658058],
+       [-0.04194231],
+       [-0.15447331]])
+y=[83, &#39;F&#39;]
+</pre></div>
+</div>
+</section>
+<section id="creating-windows">
+<h2>Creating Windows<a class="headerlink" href="#creating-windows" title="Link to this heading">#</a></h2>
+<p>We will skip preprocessing steps for now, since it is not the aim of this
+example. Instead, we will directly create compute windows. We specify a
+mapping from genders ‘M’ and ‘F’ to integers, since this is required for
+decoding.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <span class="n">tuh</span><span class="p">,</span>
+    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">mapping</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>  <span class="c1"># map non-digit targets</span>
+<span class="p">)</span>
+<span class="c1"># store the number of windows required for loading later on</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.set_description" title="braindecode.datasets.BaseConcatDataset.set_description" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">set_description</span></a><span class="p">({</span>
+    <span class="s2">&quot;n_windows&quot;</span><span class="p">:</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">]})</span>
+</pre></div>
+</div>
+</section>
+<section id="exploring-windows">
+<h2>Exploring Windows<a class="headerlink" href="#exploring-windows" title="Link to this heading">#</a></h2>
+<p>Iterating through the dataset gives <cite>x</cite> as an ndarray with shape
+<cite>(n_channels x 1000)</cite>, <cite>y</cite> as <cite>[age, gender]</cite>, and <cite>ind</cite>.
+Let’s look at the last example again.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x=array([[ 0.16017832,  0.11754766,  0.6515615 , ..., -0.50407195,
+         0.7429778 , -0.9292252 ],
+       [ 0.86039335,  0.5169721 , -1.0308012 , ..., -1.1209397 ,
+         1.5210943 ,  1.2247716 ],
+       [-2.373041  , -0.14827406,  0.25829056, ...,  1.1486133 ,
+         0.15352039,  0.12493701],
+       ...,
+       [ 0.30362263, -0.939559  ,  2.5686462 , ..., -0.5503989 ,
+        -1.3299779 ,  0.3865806 ],
+       [ 1.7443461 ,  1.1792846 , -0.25878426, ..., -0.5670986 ,
+        -1.2997373 , -0.04194231],
+       [ 1.0537782 ,  1.4429058 , -0.05458383, ...,  1.000525  ,
+         0.66521484, -0.15447332]], dtype=float32)
+y=[83, 1]
+ind=[3, 2600, 3600]
+</pre></div>
+</div>
+</section>
+<section id="dataloader-for-model-training">
+<h2>DataLoader for Model Training<a class="headerlink" href="#dataloader-for-model-training" title="Link to this heading">#</a></h2>
+<p>We give the dataset to a pytorch DataLoader, such that it can be used for
+model training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span>
+    <span class="n">dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="exploring-dataloader">
+<h2>Exploring DataLoader<a class="headerlink" href="#exploring-dataloader" title="Link to this heading">#</a></h2>
+<p>When iterating through the DataLoader, we get <cite>batch_X</cite> as a tensor with shape
+<cite>(4 x n_channels x 1000)</cite>, <cite>batch_y</cite> as <cite>[tensor([4 x age of subject]),
+tensor([4 x gender of subject])]</cite>, and <cite>batch_ind</cite>. To view the last example,
+simply iterate through the DataLoader:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a><span class="p">:</span>
+    <span class="k">pass</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>batch_X=tensor([[[ 0.8024, -0.4564, -1.4433,  ..., -0.7404, -2.2522, -3.0508],
+         [ 0.4418,  0.5201, -1.2037,  ...,  1.0273,  1.0506,  0.1677],
+         [-2.1145,  0.7075,  0.1768,  ..., -0.0260, -0.7216,  0.2742],
+         ...,
+         [ 0.3986,  0.3912,  0.8000,  ...,  1.0593, -0.8097, -1.1729],
+         [-0.6267, -0.8419, -1.3996,  ...,  0.6685,  2.9266, -0.1319],
+         [ 1.1409,  1.6246, -0.6229,  ...,  0.5953, -0.3169, -1.4604]],
+
+        [[-0.2885, -0.0168,  1.6279,  ...,  0.6533,  2.7673, -0.6671],
+         [-0.0448, -1.1422, -0.5395,  ..., -0.4664, -0.1882,  0.2031],
+         [ 0.6282, -0.4535, -0.8585,  ..., -0.2657, -0.9942, -0.3878],
+         ...,
+         [-0.1360,  0.9132,  0.9481,  ..., -0.5033, -1.3406,  0.1251],
+         [ 0.6089, -0.9883, -0.2919,  ..., -1.3796,  0.8036,  0.7666],
+         [ 0.8174, -0.3829, -1.1508,  ..., -0.8516,  2.0426,  1.1322]],
+
+        [[-0.4122,  1.2526, -0.9711,  ..., -0.6890, -1.4660,  1.2500],
+         [-0.5510,  0.3613,  1.0280,  ..., -1.1350, -0.2249, -0.9406],
+         [ 0.7754,  0.7664, -1.1807,  ...,  1.8206,  1.1281,  0.1607],
+         ...,
+         [-0.9353,  0.0165, -0.6509,  ..., -0.7276,  0.2137, -0.5619],
+         [-0.0408, -1.9825, -0.3648,  ..., -1.6978,  0.8943,  1.2374],
+         [-1.1185, -0.4398, -0.8218,  ..., -1.5172,  2.2975,  1.0311]],
+
+        [[ 0.1602,  0.1175,  0.6516,  ..., -0.5041,  0.7430, -0.9292],
+         [ 0.8604,  0.5170, -1.0308,  ..., -1.1209,  1.5211,  1.2248],
+         [-2.3730, -0.1483,  0.2583,  ...,  1.1486,  0.1535,  0.1249],
+         ...,
+         [ 0.3036, -0.9396,  2.5686,  ..., -0.5504, -1.3300,  0.3866],
+         [ 1.7443,  1.1793, -0.2588,  ..., -0.5671, -1.2997, -0.0419],
+         [ 1.0538,  1.4429, -0.0546,  ...,  1.0005,  0.6652, -0.1545]]])
+batch_y=[tensor([83, 83, 83, 83]), tensor([1, 1, 1, 1])]
+batch_ind=[tensor([0, 1, 2, 3]), tensor([   0, 1000, 2000, 2600]), tensor([1000, 2000, 3000, 3600])]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.122 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  11 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tuh_discrete_multitarget.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tuh_discrete_multitarget.ipynb</span></code></a></p>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-temple-university-hospital-tuh-eeg-corpus-dataset">Creating Temple University Hospital (TUH) EEG Corpus Dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exploring-data">Exploring Data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-windows">Creating Windows</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exploring-windows">Exploring Windows</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataloader-for-model-training">DataLoader for Model Training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#exploring-dataloader">Exploring DataLoader</a></li>
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+<div class="header-article-items header-article__inner">
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+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
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+      
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+              
+              
+<div>
+  
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+<span id="sphx-glr-auto-examples-datasets-io-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>00:10.152</strong> total execution time for <strong>auto_examples_datasets_io</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_split_dataset.py</span></code>)</p></td>
+<td><p>00:06.031</p></td>
+<td><p>10.2 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_discrete_multitarget.py</span></code>)</p></td>
+<td><p>00:04.122</p></td>
+<td><p>10.7 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a> (<code class="docutils literal notranslate"><span class="pre">benchmark_lazy_eager_loading.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_preprocessing.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_dataset_example.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_load_save_datasets.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mne_dataset_example.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_moabb_dataset_example.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="model_building/index.html">Basic model building and training</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+<li class="toctree-l2"><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="model_building/plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+  <section id="examples">
+<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h1>
+<p>Learn how to use braindecode by the examples. There are several ways to learn and understand how a library works. Here at braindecode, we encourage hands-on mode.</p>
+<p>See which tutorial best fits the approach you want and try to reproduce it on the examples data or on your data :)</p>
+<div class="sphx-glr-thumbnails"></div><section id="basic-model-building-and-training">
+<h2>Basic model building and training<a class="headerlink" href="#basic-model-building-and-training" title="Link to this heading">#</a></h2>
+<p>Examples introducing fundamental model building and training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div></div></section>
+<section id="loading-and-organizing-data">
+<h2>Loading and organizing data<a class="headerlink" href="#loading-and-organizing-data" title="Link to this heading">#</a></h2>
+<p>Examples introducing data loading and basic data processing.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div></div></section>
+<section id="advanced-neural-network-training-strategies">
+<h2>Advanced neural network training strategies<a class="headerlink" href="#advanced-neural-network-training-strategies" title="Link to this heading">#</a></h2>
+<p>Examples explaining more advanced topics in neural network training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div></div></section>
+<section id="applied-examples-on-real-world-datasets">
+<h2>Applied examples on real-world datasets<a class="headerlink" href="#applied-examples-on-real-world-datasets" title="Link to this heading">#</a></h2>
+<p>Examples demonstrating analaysis of EEG on data from clinical studies, sleep &amp; cognitive experiments.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
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+<p>Examples introducing fundamental model building and training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
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+<p class="admonition-title">Note</p>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="simple-training-on-mne-epochs">
+<span id="sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"></span><h1>Simple training on MNE epochs<a class="headerlink" href="#simple-training-on-mne-epochs" title="Link to this heading">#</a></h1>
+<p>The braindecode library gives you access to a large number of neural network
+architectures that were developed for EEG data decoding. This tutorial will
+show you how you can easily use any of these models to decode your own data.
+In particular, we assume that have your data in an MNE format and want to
+train one of the Braindecode models on it.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#finding-the-model-you-want" id="id3">Finding the model you want</a></p>
+<ul>
+<li><p><a class="reference internal" href="#exploring-the-braindecode-online-documentation" id="id4">Exploring the braindecode online documentation</a></p></li>
+<li><p><a class="reference internal" href="#examining-the-model" id="id5">Examining the model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#loading-your-own-data-with-mne" id="id6">Loading your own data with MNE</a></p></li>
+<li><p><a class="reference internal" href="#training-your-model-scikit-learn-compatible" id="id7">Training your model (scikit-learn compatible)</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id8">References</a></p></li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="finding-the-model-you-want">
+<h2><a class="toc-backref" href="#id3" role="doc-backlink">Finding the model you want</a><a class="headerlink" href="#finding-the-model-you-want" title="Link to this heading">#</a></h2>
+<section id="exploring-the-braindecode-online-documentation">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Exploring the braindecode online documentation</a><a class="headerlink" href="#exploring-the-braindecode-online-documentation" title="Link to this heading">#</a></h3>
+<p>Let’s suppose you recently stumbled upon the Schirrmeister 2017 article <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+In this article, the authors mention that their novel architecture ShallowConvNet
+is performing well on the BCI Competition IV 2a dataset and you would like to use
+it on your own data. Fortunately, the authors also mentioned they published their
+architecture on Braindecode!</p>
+<p>In order to use this architecture, you first need to find what is its exact
+name in Braindecode. To do so, you can visit the Braindecode online documentation
+which lists all the available models.</p>
+<p>Models list: <a class="reference external" href="https://braindecode.org/stable/api.html#models">https://braindecode.org/stable/api.html#models</a></p>
+<p>Alternatively, the API also provide a dictionary with all available models:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models.util</span> <span class="kn">import</span> <span class="n">models_dict</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;All the Braindecode models:</span><span class="se">\n</span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">models_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>All the Braindecode models:
+[&#39;ATCNet&#39;, &#39;Deep4Net&#39;, &#39;DeepSleepNet&#39;, &#39;EEGConformer&#39;, &#39;EEGITNet&#39;, &#39;EEGInception&#39;, &#39;EEGInceptionERP&#39;, &#39;EEGInceptionMI&#39;, &#39;EEGNetv1&#39;, &#39;EEGNetv4&#39;, &#39;EEGResNet&#39;, &#39;HybridNet&#39;, &#39;ShallowFBCSPNet&#39;, &#39;SleepStagerBlanco2020&#39;, &#39;SleepStagerChambon2018&#39;, &#39;SleepStagerEldele2021&#39;, &#39;TCN&#39;, &#39;TIDNet&#39;, &#39;USleep&#39;]
+</pre></div>
+</div>
+<p>After your investigation, you found out that the model you are looking for is
+<code class="docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code>. You can now import it from Braindecode:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+</pre></div>
+</div>
+</section>
+<section id="examining-the-model">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Examining the model</a><a class="headerlink" href="#examining-the-model" title="Link to this heading">#</a></h3>
+<p>Now that you found your model, you must check which parameters it expects.
+You can find this information either in the online documentation here:
+<a class="reference internal" href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.ShallowFBCSPNet</span></code></a> or directly in the module’s docstring:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shallow ConvNet model from Schirrmeister et al 2017.
+
+Model described in [Schirrmeister2017]_.
+
+Parameters
+----------
+n_chans : int
+    Number of EEG channels.
+n_outputs : int
+    Number of outputs of the model. This is the number of classes
+    in the case of classification.
+n_times : int
+    Number of time samples of the input window.
+n_filters_time: int
+    Number of temporal filters.
+filter_time_length: int
+    Length of the temporal filter.
+n_filters_spat: int
+    Number of spatial filters.
+pool_time_length: int
+    Length of temporal pooling filter.
+pool_time_stride: int
+    Length of stride between temporal pooling filters.
+final_conv_length: int | str
+    Length of the final convolution layer.
+    If set to &quot;auto&quot;, length of the input signal must be specified.
+conv_nonlin: callable
+    Non-linear function to be used after convolution layers.
+pool_mode: str
+    Method to use on pooling layers. &quot;max&quot; or &quot;mean&quot;.
+pool_nonlin: callable
+    Non-linear function to be used after pooling layers.
+split_first_layer: bool
+    Split first layer into temporal and spatial layers (True) or just use temporal (False).
+    There would be no non-linearity between the split layers.
+batch_norm: bool
+    Whether to use batch normalisation.
+batch_norm_alpha: float
+    Momentum for BatchNorm2d.
+drop_prob: float
+    Dropout probability.
+chs_info : list of dict
+    Information about each individual EEG channel. This should be filled with
+    ``info[&quot;chs&quot;]``. Refer to :class:`mne.Info` for more details.
+input_window_seconds : float
+    Length of the input window in seconds.
+sfreq : float
+    Sampling frequency of the EEG recordings.
+in_chans : int
+    Alias for `n_chans`.
+n_classes: int
+    Alias for `n_outputs`.
+input_window_samples: int | None
+    Alias for `n_times`.
+add_log_softmax: bool
+    Whether to use log-softmax non-linearity as the output function.
+    LogSoftmax final layer will be removed in the future.
+    Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+    Check the documentation of the torch.nn loss functions:
+    https://pytorch.org/docs/stable/nn.html#loss-functions.
+
+Raises
+------
+ValueError: If some input signal-related parameters are not specified
+            and can not be inferred.
+
+FutureWarning: If add_log_softmax is True, since LogSoftmax final layer
+               will be removed in the future.
+
+Notes
+-----
+If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.
+
+References
+----------
+.. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+   L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+   &amp; Ball, T. (2017).
+   Deep learning with convolutional neural networks for EEG decoding and
+   visualization.
+   Human Brain Mapping , Aug. 2017.
+   Online: http://dx.doi.org/10.1002/hbm.23730
+</pre></div>
+</div>
+<p>Additionally, you might be interested in visualizing the model’s architecture.
+This can be done by initializing the model and calling its <code class="docutils literal notranslate"><span class="pre">__str__()</span></code> method.
+To initialize it, we need to specify some parameters that we set at random
+values for now:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <span class="n">n_chans</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 32, 1000]             [1, 2]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 32, 1000]             [1, 32, 1000, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 32, 1000, 1]          [1, 1, 1000, 32]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1000, 32]          [1, 40, 976, 1]           52,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 976, 1]           [1, 40, 976, 1]           80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 976, 1]           [1, 40, 976, 1]           --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 976, 1]           [1, 40, 61, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 61, 1]            [1, 2]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 61, 1]            [1, 2, 1, 1]              4,882                     [61, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 2, 1, 1]              [1, 2, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 2, 1, 1]              [1, 2]                    --                        --
+============================================================================================================================================
+Total params: 57,202
+Trainable params: 57,202
+Non-trainable params: 0
+Total mult-adds (M): 0.00
+============================================================================================================================================
+Input size (MB): 0.13
+Forward/backward pass size (MB): 0.31
+Params size (MB): 0.02
+Estimated Total Size (MB): 0.46
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="loading-your-own-data-with-mne">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Loading your own data with MNE</a><a class="headerlink" href="#loading-your-own-data-with-mne" title="Link to this heading">#</a></h2>
+<p>In this tutorial, we demonstrate how to train the model on MNE data.
+MNE is quite a popular library for EEG data analysis as it provides methods
+to load data from many different file formats and a large collection of algorithms
+to preprocess it.
+However, Braindecode is not limited to MNE and can be used with numpy arrays or
+PyTorch tensors/datasets.</p>
+<p>For this example, we generate some random data containing 100 examples with each
+3 channels and 1024 time points. We also generate some random labels for our data
+that simulate a 4-class classification problem.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.create_info.html#mne.create_info" title="mne.create_info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">create_info</span></a><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C3&#39;</span><span class="p">,</span> <span class="s1">&#39;C4&#39;</span><span class="p">,</span> <span class="s1">&#39;Cz&#39;</span><span class="p">],</span> <span class="n">sfreq</span><span class="o">=</span><span class="mf">256.</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)</span>  <span class="c1"># 100 epochs, 3 channels, 4 seconds (@256Hz)</span>
+<a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">EpochsArray</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a><span class="o">=</span><a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>  <span class="c1"># 4 classes</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Not setting metadata
+100 matching events found
+No baseline correction applied
+0 projection items activated
+&lt;EpochsArray |  100 events (all good), 0 – 3.99609 s, baseline off, ~2.4 MB, data loaded,
+ &#39;1&#39;: 100&gt;
+</pre></div>
+</div>
+</section>
+<section id="training-your-model-scikit-learn-compatible">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">Training your model (scikit-learn compatible)</a><a class="headerlink" href="#training-your-model-scikit-learn-compatible" title="Link to this heading">#</a></h2>
+<p>Now that you know which model you want to use, you know how to instantiate it,
+and that we have some fake data, it is time to train the model!</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference external" href="https://skorch.readthedocs.io">Skorch</a>  is a library that allows you to wrap
+any PyTorch module into a scikit-learn-compatible classifier or regressor.
+Braindecode provides wrappers that inherit form the original Skorch ones and simply
+implement a few additional features that facilitate the use of Braindecode models.</p>
+</div>
+<p>To train a Braindecode model, the easiest way is by using braindecode’s
+Skorch wrappers. These wrappers are <a class="reference internal" href="../../source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> and
+<a class="reference internal" href="../../source/braindecode.html#braindecode.EEGRegressor" title="braindecode.EEGRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code></a>. As our fake data is a classification task,
+we will use the former.</p>
+<p>The wrapper <a class="reference internal" href="../../source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> expects a model class as its first argument but
+to facilitate the usage, you can also simply pass the name of any braindecode model as a string.
+The wrapper automatically finds and instantiates the model for you.
+If you want to pass parameters to your model, you can give them to the wrapper
+with the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="n">net</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <span class="s1">&#39;ShallowFBCSPNet&#39;</span><span class="p">,</span>
+    <span class="n">module__final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
+    <span class="c1"># To train a neural network you need validation split, here, we use 20%.</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, we passed one additional parameter to the wrapper: <code class="docutils literal notranslate"><span class="pre">module__final_conv_length</span></code>
+that will be forwarded to the model (without the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>).</p>
+<p>We also note that the parameters <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_times</span></code> and <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code> were not specified
+even if <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.ShallowFBCSPNet</span></code> needs them to be initialized. This is because the
+wrapper will automatically infer them, along with some other signal-related parameters,
+from the input data at training time.</p>
+<p>Now that we have our model wrapped in a scikit-learn-compatible classifier,
+we can train it by simply calling the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">net</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    valid_acc    valid_loss     dur
+-------  -----------  ------------  ------
+      1       0.3000       31.5428  0.0574
+      2       0.3000       31.5428  0.0081
+      3       0.3000       31.5428  0.0078
+      4       0.3000       31.5428  0.0077
+      5       0.3000       31.5428  0.0077
+      6       0.3000       31.5428  0.0086
+      7       0.3000       31.5428  0.0103
+      8       0.3000       31.5428  0.0088
+      9       0.3000       31.5428  0.0083
+     10       0.3000       31.5428  0.0078
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 3, 1024]              [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 3, 1024]              [1, 3, 1024, 1]           --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 3, 1024, 1]           [1, 1, 1024, 3]           --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1024, 3]           [1, 40, 1000, 1]          5,840                     --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1000, 1]          [1, 40, 1000, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1000, 1]          [1, 40, 1000, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1000, 1]          [1, 40, 62, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 62, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 62, 1]            [1, 4, 1, 1]              9,924                     [62, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 15,844
+  Trainable params: 15,844
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.01
+  Forward/backward pass size (MB): 0.32
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.37
+  ============================================================================================================================================,
+)
+</pre></div>
+</div>
+<p>The pre-trained model is accessible via the <code class="docutils literal notranslate"><span class="pre">module_</span></code> attribute:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 3, 1024]              [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 3, 1024]              [1, 3, 1024, 1]           --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 3, 1024, 1]           [1, 1, 1024, 3]           --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1024, 3]           [1, 40, 1000, 1]          5,840                     --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1000, 1]          [1, 40, 1000, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1000, 1]          [1, 40, 1000, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1000, 1]          [1, 40, 62, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 62, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 62, 1]            [1, 4, 1, 1]              9,924                     [62, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 15,844
+Trainable params: 15,844
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.01
+Forward/backward pass size (MB): 0.32
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.37
+============================================================================================================================================
+</pre></div>
+</div>
+<p>And we can see that all the following parameters were automatically inferred
+from the training data:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_chans</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_times</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_outputs</span><span class="si">=}</span><span class="s1">&#39;</span>
+      <span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">input_window_seconds</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">sfreq</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">chs_info</span><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>net.module_.n_chans=3
+net.module_.n_times=1024
+net.module_.n_outputs=4
+net.module_.input_window_seconds=4.0
+net.module_.sfreq=256.0
+net.module_.chs_info=[{&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;C3&#39;, &#39;scanno&#39;: 1, &#39;logno&#39;: 1}, {&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;C4&#39;, &#39;scanno&#39;: 2, &#39;logno&#39;: 2}, {&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;Cz&#39;, &#39;scanno&#39;: 3, &#39;logno&#39;: 3}]
+</pre></div>
+</div>
+<p>Depending on the type of data used for training, some parameters might not be
+possible to infer. For example if you pass a numpy array or a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.dataset.WindowsDataset</span></code> with <code class="docutils literal notranslate"><span class="pre">target_from=&quot;metadata&quot;</span></code>,</p>
+<blockquote>
+<div><p>then only <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_times</span></code> and <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code> will be inferred.</p>
+</div></blockquote>
+<p>And if you pass other types of datasets, only <code class="docutils literal notranslate"><span class="pre">n_chans</span></code> and <code class="docutils literal notranslate"><span class="pre">n_times</span></code> will be inferred.
+In these case, you will have to pass the missing parameters manually
+(with the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>).</p>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id2" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter,
+M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T.(2017).
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Human Brain Mapping, Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.213 seconds)</p>
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+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-basic-training-epochs-py">
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new file mode 100644
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Basic model building and training</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+  </ul>
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+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cropped-decoding-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"></span><h1>Cropped Decoding on BCIC IV 2a Dataset<a class="headerlink" href="#cropped-decoding-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id6">Loading and preprocessing the dataset</a></p></li>
+<li><p><a class="reference internal" href="#create-model-and-compute-windowing-parameters" id="id7">Create model and compute windowing parameters</a></p></li>
+<li><p><a class="reference internal" href="#cut-the-data-into-windows" id="id8">Cut the data into windows</a></p></li>
+<li><p><a class="reference internal" href="#split-the-dataset" id="id9">Split the dataset</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id10">Training</a></p></li>
+<li><p><a class="reference internal" href="#plot-results" id="id11">Plot Results</a></p></li>
+<li><p><a class="reference internal" href="#plot-confusion-matrix" id="id12">Plot Confusion Matrix</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id13">References</a></p></li>
+</ul>
+</nav>
+<p>Building on the <a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Trialwise decoding</a>,
+we now do more data-efficient cropped decoding!</p>
+<p>In Braindecode, there are two supported configurations created for
+training models: trialwise decoding and cropped decoding. We will
+explain this visually by comparing trialwise to cropped decoding.</p>
+<img alt="Trialwise decoding" src="https://braindecode.org/stable/_static/trialwise_explanation.png" />
+<img alt="Cropped decoding" src="https://braindecode.org/stable/_static/cropped_explanation.png" />
+<p>On the left, you see trialwise decoding:</p>
+<ol class="arabic simple">
+<li><p>A complete trial is pushed through the network.</p></li>
+<li><p>The network produces a prediction.</p></li>
+<li><p>The prediction is compared to the target (label) for that trial to
+compute the loss.</p></li>
+</ol>
+<p>On the right, you see cropped decoding:</p>
+<ol class="arabic simple">
+<li><p>Instead of a complete trial, crops are pushed through the network.</p></li>
+<li><p>For computational efficiency, multiple neighbouring crops are pushed
+through the network simultaneously (these neighbouring crops are
+called compute windows)</p></li>
+<li><p>Therefore, the network produces multiple predictions (one per crop in
+the window)</p></li>
+<li><p>The individual crop predictions are <em>AVERAGED</em> before computing the
+loss function</p></li>
+</ol>
+<p>This averaging of predictions of small sub-windows is the key difference
+between trialwise and cropped decoding. It was introduced in <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and it impact
+on the parameters of the network.</p>
+<p>It is important to note that the averaging of predictions is only done
+during training. During testing, the network is still applied to crops
+and the predictions are averaged afterwards.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<ul class="simple">
+<li><p>The network architecture implicitly defines the crop size (it is the
+receptive field size, i.e., the number of timesteps the network uses
+to make a single prediction)</p></li>
+<li><p>The window size is a user-defined hyperparameter, called
+<code class="docutils literal notranslate"><span class="pre">input_window_samples</span></code> in Braindecode. It mostly affects runtime
+(larger window sizes should be faster). As a rule of thumb, you can
+set it to two times the crop size.</p></li>
+<li><p>Crop size and window size together define how many predictions the
+network makes per window: <code class="docutils literal notranslate"><span class="pre">#window</span> <span class="pre">−</span> <span class="pre">#crop</span> <span class="pre">+</span> <span class="pre">1</span> <span class="pre">=</span> <span class="pre">#predictions</span></code></p></li>
+</ul>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>For cropped decoding, the above training setup is mathematically
+similar to sampling crops in your dataset, pushing them through the
+network and training directly on the individual crops. However, the
+if their position would be randomly selected, the crops would be less
+correlated in contrast to the neighbourhood crops selected from a window.
+At the same time, the above training setup is much faster as it avoids
+redundant computations by using dilated convolutions, see <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.
+However, the two setups are only mathematically related in case (1)
+your network does not use any padding or only left padding and
+(2) your loss function leads
+to the same gradients when using the averaged output. The first is true
+for our shallow and deep ConvNet models and the second is true for the
+log-softmax outputs and negative log likelihood loss that is typically
+used for classification in PyTorch.</p>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<p>Loading and preprocessing stays the same as in the <a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Trialwise decoding
+tutorial</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>
+    <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                 <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f421d2921f0&gt;
+</pre></div>
+</div>
+</section>
+<section id="create-model-and-compute-windowing-parameters">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">Create model and compute windowing parameters</a><a class="headerlink" href="#create-model-and-compute-windowing-parameters" title="Link to this heading">#</a></h2>
+<p>In contrast to trialwise decoding, we first have to create the model
+before we can cut the dataset into windows. This is because we need to
+know the neural network parameters to know how large the sub-window
+stride should be.</p>
+<p>We first choose the compute/input window size that will be fed to the
+network during training. This has to be larger than the networks
+the number of timesteps size and can otherwise be chosen for computational
+efficiency (see explanations in the beginning of this tutorial). Here we
+choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+</pre></div>
+</div>
+<p>Now we create the model. To enable it to be used in cropped decoding
+efficiently, we manually set the length of the final convolution layer
+to some length that makes the number of timesteps of the ConvNet smaller
+than <code class="docutils literal notranslate"><span class="pre">input_window_samples</span></code> (see <code class="docutils literal notranslate"><span class="pre">final_conv_length=30</span></code> in the model
+definition).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <span class="n">_</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1000]             [1, 4, 32]                --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1000]             [1, 22, 1000, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1000, 1]          [1, 1, 1000, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1000, 22]          [1, 40, 976, 1]           36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 976, 1]           [1, 40, 976, 1]           80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 976, 1]           [1, 40, 976, 1]           --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 976, 1]           [1, 40, 61, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 61, 1]            [1, 4, 32]                --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 61, 1]            [1, 4, 32, 1]             4,804                     [30, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 32, 1]             [1, 4, 32, 1]             --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 32, 1]             [1, 4, 32]                --                        --
+============================================================================================================================================
+Total params: 41,124
+Trainable params: 41,124
+Non-trainable params: 0
+Total mult-adds (M): 0.15
+============================================================================================================================================
+Input size (MB): 0.09
+Forward/backward pass size (MB): 0.31
+Params size (MB): 0.02
+Estimated Total Size (MB): 0.42
+============================================================================================================================================
+</pre></div>
+</div>
+<p>And now we transform model with strides to a model that outputs dense
+prediction, so we can use it to obtain predictions for all
+crops.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="o">.</span><span class="n">to_dense_prediction_model</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>To know the models’ output shape without the last layer, we calculate the
+shape of model output for a dummy input.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="o">.</span><span class="n">get_output_shape</span><span class="p">()[</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="cut-the-data-into-windows">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Cut the data into windows</a><a class="headerlink" href="#cut-the-data-into-windows" title="Link to this heading">#</a></h2>
+<p>In contrast to trialwise decoding, we have to supply an explicit
+window size and window stride to the <code class="docutils literal notranslate"><span class="pre">create_windows_from_events</span></code>
+function.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-the-dataset">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Split the dataset</a><a class="headerlink" href="#split-the-dataset" title="Link to this heading">#</a></h2>
+<p>This code is the same as in trialwise decoding.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id10" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>In difference to trialwise decoding, we now should supply
+<code class="docutils literal notranslate"><span class="pre">cropped=True</span></code> to the EEGClassifier, and <code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code> as the
+criterion, as well as <code class="docutils literal notranslate"><span class="pre">criterion__loss_function</span></code> as the loss function
+applied to the meaned predictions.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for motor decoding, however we strongly
+encourage you to perform your own hyperparameter optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
+
+<span class="c1"># These values we found good for shallow network:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="c1"># For deep4 they should be:</span>
+<span class="c1"># lr = 1 * 0.01</span>
+<span class="c1"># weight_decay = 0.5 * 0.001</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.nll_loss.html#torch.nn.functional.nll_loss" title="torch.nn.functional.nll_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
+<span class="c1"># in the dataset.</span>
+<span class="n">_</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  ----------------  ------------  ------  ------
+      1            0.2778        1.5658            0.2535        1.5302  0.0006  4.2915
+      2            0.3090        1.4417            0.2604        1.4977  0.0000  4.2254
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2><a class="toc-backref" href="#id11" role="doc-backlink">Plot Results</a><a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Note that we drop further in the classification error and
+loss as in the trialwise decoding tutorial.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span>
+                   <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<span class="c1"># get percent of misclass for better visual comparison to loss</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
+               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/></section>
+<section id="plot-confusion-matrix">
+<h2><a class="toc-backref" href="#id12" role="doc-backlink">Plot Confusion Matrix</a><a class="headerlink" href="#plot-confusion-matrix" title="Link to this heading">#</a></h2>
+<p>Generate a confusion matrix as in <a class="footnote-reference brackets" href="#id5" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<span class="c1"># generate confusion matrices</span>
+<span class="c1"># get the targets</span>
+<span class="n">y_true</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<span class="c1"># generating confusion matrix</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<span class="c1"># add class labels</span>
+<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">window_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;mapping&#39;</span><span class="p">]</span>
+<span class="c1"># sort the labels by values (values are integer class labels)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+
+<span class="c1"># plot the basic conf. matrix</span>
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 640x480 with 1 Axes&gt;
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id4" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id3">2</a>)</span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model-and-compute-windowing-parameters">Create model and compute windowing parameters</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-the-data-into-windows">Cut the data into windows</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#split-the-dataset">Split the dataset</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Basic model building and training</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_basic_training_epochs.html">Simple training on MNE epochs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
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+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="basic-brain-decoding-on-eeg-data">
+<span id="sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"></span><h1>Basic Brain Decoding on EEG Data<a class="headerlink" href="#basic-brain-decoding-on-eeg-data" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train and test deep learning models with
+Braindecode in a classical EEG setting: you have trials of data with
+labels (e.g., Right Hand, Left Hand, etc.).</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preparing-the-data" id="id8">Loading and preparing the data</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-the-dataset" id="id9">Loading the dataset</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id10">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-compute-windows" id="id11">Extracting Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#splitting-the-dataset-into-training-and-validation-sets" id="id12">Splitting the dataset into training and validation sets</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#creating-a-model" id="id13">Creating a model</a></p></li>
+<li><p><a class="reference internal" href="#model-training" id="id14">Model Training</a></p></li>
+<li><p><a class="reference internal" href="#plotting-results" id="id15">Plotting Results</a></p></li>
+<li><p><a class="reference internal" href="#plotting-a-confusion-matrix" id="id16">Plotting a  Confusion Matrix</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id17">References</a></p></li>
+</ul>
+</nav>
+<section id="loading-and-preparing-the-data">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Loading and preparing the data</a><a class="headerlink" href="#loading-and-preparing-the-data" title="Link to this heading">#</a></h2>
+<section id="loading-the-dataset">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Loading the dataset</a><a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we load the BCI Competition
+IV 2a data <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> using braindecode’s wrapper to load via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB library</a> <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To load your own datasets either via mne or from
+preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
+Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
+Tutorial</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Now we apply preprocessing like bandpass filtering to our dataset. You
+can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Generally, braindecode prepocessing is directly applied to the loaded
+data, and not applied on-the-fly as transformations, such as in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4215f85d30&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-compute-windows">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Extracting Compute Windows</a><a class="headerlink" href="#extracting-compute-windows" title="Link to this heading">#</a></h3>
+<p>Now we extract compute windows from the signals, these will be the inputs
+to the deep networks during training. In the case of trialwise
+decoding, we just have to decide if we want to include some part
+before and/or after the trial. For our work with this dataset,
+it was often beneficial to also include the 500 ms before the trial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="splitting-the-dataset-into-training-and-validation-sets">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Splitting the dataset into training and validation sets</a><a class="headerlink" href="#splitting-the-dataset-into-training-and-validation-sets" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
+<code class="docutils literal notranslate"><span class="pre">T</span></code> for training and <code class="docutils literal notranslate"><span class="pre">test</span></code> for validation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="creating-a-model">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Creating a model</a><a class="headerlink" href="#creating-a-model" title="Link to this heading">#</a></h2>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the shallow ConvNet model from <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+<section id="model-training">
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Model Training</a><a class="headerlink" href="#model-training" title="Link to this heading">#</a></h2>
+<p>Now we will train the network! <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code> is a Braindecode object
+responsible for managing the training of neural networks. It inherits
+from skorch <a class="reference external" href="https://skorch.readthedocs.io/en/stable/classifier.html#">NeuralNetClassifier</a>,
+so the training logic is the same as in <a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for motor decoding, however we strongly
+encourage you to perform your own hyperparameter optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="c1"># We found these values to be good for the shallow network:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="c1"># For deep4 they should be:</span>
+<span class="c1"># lr = 1 * 0.01</span>
+<span class="c1"># weight_decay = 0.5 * 0.001</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for the specified number of epochs. `y` is None as it is</span>
+<span class="c1"># already supplied in the dataset.</span>
+<span class="n">_</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2500        1.5950       0.2535            0.2535        4.6092  0.0006  1.2582
+      2            0.2708        1.2975       0.2535            0.2535        3.4426  0.0005  1.2589
+      3            0.3333        1.2141       0.2674            0.2674        2.7940  0.0002  1.2030
+      4            0.3438        1.1842       0.2778            0.2778        2.4347  0.0000  1.1932
+</pre></div>
+</div>
+</section>
+<section id="plotting-results">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Plotting Results</a><a class="headerlink" href="#plotting-results" title="Link to this heading">#</a></h2>
+<p>Now we use the history stored by Skorch throughout training to plot
+accuracy and loss curves.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<span class="c1"># get percent of misclass for better visual comparison to loss</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
+               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/></section>
+<section id="plotting-a-confusion-matrix">
+<h2><a class="toc-backref" href="#id16" role="doc-backlink">Plotting a  Confusion Matrix</a><a class="headerlink" href="#plotting-a-confusion-matrix" title="Link to this heading">#</a></h2>
+<p>Here we generate a confusion matrix as in <a class="footnote-reference brackets" href="#id7" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<span class="c1"># generate confusion matrices</span>
+<span class="c1"># get the targets</span>
+<span class="n">y_true</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<span class="c1"># generating confusion matrix</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<span class="c1"># add class labels</span>
+<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">window_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;mapping&#39;</span><span class="p">]</span>
+<span class="c1"># sort the labels by values (values are integer class labels)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+
+<span class="c1"># plot the basic conf. matrix</span>
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 640x480 with 1 Axes&gt;
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id3">1</a>,<a role="doc-backlink" href="#id4">2</a>)</span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.360 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  502 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_2a_moabb_trial.ipynb</span></code></a></p>
+</div>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Basic model building and training</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">How to train, test and tune your model?</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="how-to-train-test-and-tune-your-model">
+<span id="sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"></span><h1>How to train, test and tune your model?<a class="headerlink" href="#how-to-train-test-and-tune-your-model" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to properly train, tune and test your deep learning
+models with Braindecode. We will use the BCIC IV 2a dataset <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> as a showcase example.</p>
+<p>The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing of data,
+defining a model, and other details which are not exclusive to this page (see
+<a class="reference external" href="./plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding Tutorial</a>). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.</p>
+<p>In general, we distinguish between “usual” training and evaluation and hyperparameter search.
+The tutorial is therefore split into two parts, one for the three different training schemes
+and one for the two different hyperparameter tuning methods.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#why-should-i-care-about-model-evaluation" id="id8">Why should I care about model evaluation?</a></p></li>
+<li><p><a class="reference internal" href="#loading-and-preprocessing-of-data-defining-a-model-etc" id="id9">Loading and preprocessing of data, defining a model, etc.</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-data" id="id10">Loading data</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing-data" id="id11">Preprocessing data</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-windows" id="id12">Extraction of the Windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-test" id="id13">Split dataset into train and test</a></p></li>
+<li><p><a class="reference internal" href="#create-model" id="id14">Create model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-train-and-evaluate-your-model" id="id15">How to train and evaluate your model</a></p>
+<ul>
+<li><p><a class="reference internal" href="#option-1-simple-train-test-split" id="id16">Option 1: Simple Train-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-first-option-with-a-util-function" id="id17">Let’s visualize the first option with a util function.</a></p></li>
+<li><p><a class="reference internal" href="#option-2-train-val-test-split" id="id18">Option 2: Train-Val-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-second-option-with-a-util-function" id="id19">Let’s visualize the second option with a util function.</a></p></li>
+<li><p><a class="reference internal" href="#option-3-k-fold-cross-validation" id="id20">Option 3: k-Fold Cross Validation</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-third-option-with-a-util-function" id="id21">Let’s visualize the third option with a util function.</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-tune-your-hyperparameters" id="id22">How to tune your hyperparameters</a></p>
+<ul>
+<li><p><a class="reference internal" href="#option-1-train-val-test-split" id="id23">Option 1: Train-Val-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#option-2-k-fold-cross-validation" id="id24">Option 2: k-Fold Cross Validation</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#references" id="id25">References</a></p></li>
+</ul>
+</nav>
+<section id="why-should-i-care-about-model-evaluation">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Why should I care about model evaluation?</a><a class="headerlink" href="#why-should-i-care-about-model-evaluation" title="Link to this heading">#</a></h2>
+<p>Short answer: To produce reliable results!</p>
+<p>In machine learning, we usually follow the scheme of splitting the
+data into two parts, training and testing sets. It sounds like a
+simple division, right? But the story does not end here.</p>
+<ul class="simple">
+<li><p>What are model’s parameters?</p></li>
+</ul>
+<p>Model’s parameters are learnable weights which are used in the
+extraction of the relevant features and in performing the final inference.
+In the context of deep learning, these are usually fully connected weights,
+convolutional kernels, biases, etc.</p>
+<ul class="simple">
+<li><p>What are model’s hyperparameters?</p></li>
+</ul>
+<p>Model’s hyperparameters are used to set the capacity (size) of the model
+and to guide the parameter learning process.
+In the context of deep learning, examples of the hyperparameters are the
+number of convolutional layers and the number of convolutional kernels in
+each of them, the number and size of the fully connected weights,
+choice of the optimizer and its learning rate, the number of training epochs,
+choice of the nonlinearities, etc.</p>
+<p>While developing a ML model you usually have to adjust and tune
+hyperparameters of your model or pipeline (e.g., number of layers,
+learning rate, number of epochs). Deep learning models usually have
+many free parameters; they could be considered as complex models with
+many degrees of freedom. If you kept using the test dataset to
+evaluate your adjustment, you would run into data leakage.</p>
+<p>This means that if you use the test set to adjust the hyperparameters
+of your model, the model implicitly learns or memorizes the test set.
+Therefore, the trained model is no longer independent of the test set
+(even though it was never used for training explicitly!).
+If you perform any hyperparameter tuning, you need a third split,
+the so-called validation set.</p>
+<p>This tutorial shows the three basic schemes for training and evaluating
+the model as well as two methods to tune your hyperparameters.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>You might recognize that the accuracy gets better throughout
+the experiments of this tutorial. The reason behind that is that
+we always use the same model with the same parameters in every
+segment to keep the tutorial short and readable. If you do your
+own experiments you always have to reinitialize the model before
+training.</p>
+</div>
+</section>
+<section id="loading-and-preprocessing-of-data-defining-a-model-etc">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Loading and preprocessing of data, defining a model, etc.</a><a class="headerlink" href="#loading-and-preprocessing-of-data-defining-a-model-etc" title="Link to this heading">#</a></h2>
+<section id="loading-data">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Loading data</a><a class="headerlink" href="#loading-data" title="Link to this heading">#</a></h3>
+<p>In this example, we load the BCI Competition IV 2a data <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, for one
+subject (subject id 3), using braindecode’s wrapper to load via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB library</a> <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing-data">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Preprocessing data</a><a class="headerlink" href="#preprocessing-data" title="Link to this heading">#</a></h3>
+<p>In this example, preprocessing includes signal rescaling, the bandpass filtering
+(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+the exponential moving mean and variance.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.0</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.0</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;pick_types&quot;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+        <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
+    <span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;filter&quot;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Preprocess the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4217628ee0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extraction-of-the-windows">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Extraction of the Windows</a><a class="headerlink" href="#extraction-of-the-windows" title="Link to this heading">#</a></h3>
+<p>Extraction of the trials (windows) from the time series is based on the
+events inside the dataset. One event is the demarcation of the stimulus or
+the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+before the corresponding event and the duration of the event itself.
+#Therefore, we set the <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> to -0.5 [s] and the
+<code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> to 0 [s].</p>
+<p>We extract from the dataset the sampling frequency, which is the same for
+all datasets in this case, and we tested it.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> and <code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> are
+defined in seconds and need to be converted into samples (multiplication
+with the sampling frequency), relative to the event.
+This variable is dataset dependent.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the window start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
+<span class="c1"># define how windows should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-test">
+<h3><a class="toc-backref" href="#id13" role="doc-backlink">Split dataset into train and test</a><a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset BCIC IV 2a dataset using additional
+info stored in the description attribute, in this case the <code class="docutils literal notranslate"><span class="pre">session</span></code>
+column. We select <code class="docutils literal notranslate"><span class="pre">0train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">0test</span></code> for testing.
+For other datasets, you might have to choose another column and/or column.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>No matter which of the three schemes you use, this initial
+two-fold split into train_set and test_set always remains the same.
+Remember that you are not allowed to use the test_set during any
+stage of training or tuning.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id14" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>In this tutorial, ShallowFBCSPNet classifier <a class="footnote-reference brackets" href="#id7" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> is explored. The model
+training is performed on GPU if it exists, otherwise on CPU.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-train-and-evaluate-your-model">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">How to train and evaluate your model</a><a class="headerlink" href="#how-to-train-and-evaluate-your-model" title="Link to this heading">#</a></h2>
+<section id="option-1-simple-train-test-split">
+<h3><a class="toc-backref" href="#id16" role="doc-backlink">Option 1: Simple Train-Test Split</a><a class="headerlink" href="#option-1-simple-train-test-split" title="Link to this heading">#</a></h3>
+<p>This is the easiest training scheme to use as the dataset is only
+split into two distinct sets (<code class="docutils literal notranslate"><span class="pre">train_set</span></code> and <code class="docutils literal notranslate"><span class="pre">test_set</span></code>).
+This scheme uses no separate validation split and should only be
+used for the final evaluation of the (previously!) found
+hyperparameters configuration.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>If you make any use of the <code class="docutils literal notranslate"><span class="pre">test_set</span></code> during training
+(e.g. by using EarlyStopping) there will be data leakage
+which will make the reported generalization capability/decoding
+performance of your model less credible.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
+<span class="c1"># in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># evaluated the model after training</span>
+<span class="n">y_test</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2500        1.5950  0.0006  1.2795
+      2            0.2500        1.3661  0.0000  1.3909
+Test acc: 25.35%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-first-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id17" role="doc-backlink">Let’s visualize the first option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-first-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<p>The following figure illustrates split of entire dataset into the
+training and testing subsets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">plot_simple_train_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training-testing split.&quot;&quot;&quot;</span>
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Test split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower center&#39;</span><span class="p">,</span>
+              <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">plot_simple_train_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+                       <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_001.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_001.png" alt="Train-Test split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;Train-Test split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+<section id="option-2-train-val-test-split">
+<h3><a class="toc-backref" href="#id18" role="doc-backlink">Option 2: Train-Val-Test Split</a><a class="headerlink" href="#option-2-train-val-test-split" title="Link to this heading">#</a></h3>
+<p>When evaluating different settings hyperparameters for your model,
+there is still a risk of overfitting on the test set because the
+parameters can be tweaked until the estimator performs optimally.
+For more information visit <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html">sklearns Cross-Validation Guide</a>.
+This second option splits the original <code class="docutils literal notranslate"><span class="pre">train_set</span></code> into two distinct
+sets, the training set and the validation set to avoid overfitting
+the hyperparameters to the test set.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If your dataset is really small, the validation split can become
+quite small. This may lead to unreliable tuning results. To
+avoid this, either use Option 3 or adjust the split ratio.</p>
+</div>
+<p>To split the <code class="docutils literal notranslate"><span class="pre">train_set</span></code> we will make use of the
+<code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument of <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code>. If you leave this empty
+(not None!), skorch will make an 80-20 train-validation split.
+If you want to control the split manually you can do that by using
+<code class="docutils literal notranslate"><span class="pre">Subset</span></code> from torch and <code class="docutils literal notranslate"><span class="pre">predefined_split</span></code> from skorch.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span><span class="p">,</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+
+<span class="n">X_train</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The parameter <code class="docutils literal notranslate"><span class="pre">shuffle</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>. For time-series
+data this should always be the case as shuffling might take
+advantage of correlated samples, which would make the validation
+performance less meaningful.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># evaluate the model after training and validation</span>
+<span class="n">y_test</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.3435        1.3505       0.2759            0.2759        2.2664  0.0006  1.0087
+      2            0.3478        1.1789       0.2759            0.2759        2.0683  0.0000  0.8395
+Test acc: 27.43%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-second-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id19" role="doc-backlink">Let’s visualize the second option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-second-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<p>The following figure illustrates split of entire dataset into the
+training, validation and testing subsets.
+<code class="docutils literal notranslate"><span class="pre">Making</span> <span class="pre">more</span> <span class="pre">compact</span> <span class="pre">plot_train_valid_test</span> <span class="pre">function.</span></code></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_train_valid_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training, validation, testing.&quot;&quot;&quot;</span>
+
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="p">]</span>
+
+    <span class="n">n_train</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Test-Valid split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
+              <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower center&quot;</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">))</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plot_train_valid_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+                      <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_002.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_002.png" alt="Train-Test-Valid split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;Train-Test-Valid split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+<section id="option-3-k-fold-cross-validation">
+<h3><a class="toc-backref" href="#id20" role="doc-backlink">Option 3: k-Fold Cross Validation</a><a class="headerlink" href="#option-3-k-fold-cross-validation" title="Link to this heading">#</a></h3>
+<p>As mentioned above, using only one validation split might not be
+sufficient, as there might be a shift in the data distribution.
+To compensate for this, one can run a k-fold Cross Validation,
+where every sample of the training set is in the validation set once.
+After averaging over the k validation scores afterwards, you get a
+very reliable estimate of how the model would perform on unseen
+data (test set).</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This k-Fold Cross Validation can be used without a separate
+(holdout) test set. If there is no test set available, e.g. in a
+competition, this scheme is highly recommended to get a reliable
+estimate of the generalization performance.</p>
+</div>
+<p>To implement this, we will make use of sklearn function
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html">cross_val_score</a> and the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">KFold</a>. CV splitter.
+The <code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument has to be set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, as sklearn
+will take care of the splitting.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="c1"># By setting n_jobs=-1, cross-validation is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a><span class="p">(</span>
+    <span class="n">clf</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Validation accuracy: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;+-</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.3870        1.2082  0.0006  0.8834
+      2            0.3957        1.1560  0.0000  1.0309
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2870        1.2641  0.0006  0.9693
+      2            0.2870        1.2266  0.0000  0.8010
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.3957        1.3003  0.0006  0.7499
+      2            0.4087        1.1477  0.0000  0.8126
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2727        1.2076  0.0006  0.8311
+      2            0.2900        1.1233  0.0000  0.7725
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4242        1.1577  0.0006  0.7434
+      2            0.4286        1.0504  0.0000  0.6764
+Validation accuracy: 31.94+-5.59%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-third-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id21" role="doc-backlink">Let’s visualize the third option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-third-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_k_fold</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">cv</span><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training, validation, testing.&quot;&quot;&quot;</span>
+
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">Dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="c1"># Generate the training/validation/testing data fraction visualizations for each CV split</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr_idx</span><span class="p">,</span> <span class="n">val_idx</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)):</span>
+        <span class="n">n_train</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tr_idx</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+        <span class="n">n_train2</span> <span class="o">=</span> <span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span> <span class="o">-</span> <span class="nb">max</span><span class="p">(</span><span class="n">val_idx</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_train2</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">val_idx</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;KFold Train-Test-Valid split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)],</span>
+              <span class="p">[</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
+              <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower center&quot;</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">text</span></a><span class="p">(</span><span class="o">-</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">,</span> <span class="s1">&#39;Train-Valid-Test split&#39;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
+            <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/transformations.html#matplotlib.transforms.BboxTransformTo" title="matplotlib.transforms.BboxTransformTo" class="sphx-glr-backref-module-matplotlib-transforms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span></a><span class="p">)</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">plot_k_fold</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+            <span class="n">X_train</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_003.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_003.png" alt="KFold Train-Test-Valid split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;KFold Train-Test-Valid split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-tune-your-hyperparameters">
+<h2><a class="toc-backref" href="#id22" role="doc-backlink">How to tune your hyperparameters</a><a class="headerlink" href="#how-to-tune-your-hyperparameters" title="Link to this heading">#</a></h2>
+<p>One way to do hyperparameter tuning is to run each configuration
+manually (via Option 2 or 3 from above) and compare the validation
+performance afterwards. In the early stages of your development
+process this might be sufficient to get a rough understanding of
+how your hyperparameter should look like for your model to converge.
+However, this manual tuning process quickly becomes messy as the
+number of hyperparameters you want to (jointly) tune increases.
+Therefore you should, automate this process. We will present two
+different options, analogous to Option 2 and 3 from above.</p>
+<section id="option-1-train-val-test-split">
+<h3><a class="toc-backref" href="#id23" role="doc-backlink">Option 1: Train-Val-Test Split</a><a class="headerlink" href="#option-1-train-val-test-split" title="Link to this heading">#</a></h3>
+<p>We will again make use of the <a class="reference external" href="https://scikit-learn.org/stable/">sklearn</a>
+library to do the hyperparameter search. <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a>
+will perform a Grid Search over the parameters specified in <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>.
+We use grid search for the model selection as a simple example, but you can use other strategies
+as well.
+(<a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection">List of the sklearn classes for model selection</a>.)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="nb">tuple</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
+<span class="p">]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;optimizer__lr&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.000625</span><span class="p">],</span>
+<span class="p">}</span>
+
+<span class="c1"># By setting n_jobs=-1, grid search is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+
+<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s2">&quot;rank_test_score&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">best_parameters</span></a> <span class="o">=</span> <span class="n">best_run</span><span class="p">[</span><span class="s2">&quot;params&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 1 folds for each of 2 candidates, totalling 2 fits
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2826        1.5258  0.0063  0.8067
+      2            0.4000        1.4053  0.0000  0.7428
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4826        1.2205  0.0006  0.7418
+      2            0.5130        1.1218  0.0000  0.7235
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4375        1.2991  0.0006  1.1401
+      2            0.4688        1.1161  0.0000  0.9319
+</pre></div>
+</div>
+</section>
+<section id="option-2-k-fold-cross-validation">
+<h3><a class="toc-backref" href="#id24" role="doc-backlink">Option 2: k-Fold Cross Validation</a><a class="headerlink" href="#option-2-k-fold-cross-validation" title="Link to this heading">#</a></h3>
+<p>To perform a full k-Fold CV just replace <code class="docutils literal notranslate"><span class="pre">train_val_split</span></code> from
+above with the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">KFold</a> cross-validator from sklearn.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id25" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
+</aside>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-test">Split dataset into train and test</a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-simple-train-test-split">Option 1: Simple Train-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-first-option-with-a-util-function">Let’s visualize the first option with a util function.</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-train-val-test-split">Option 2: Train-Val-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-second-option-with-a-util-function">Let’s visualize the second option with a util function.</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-3-k-fold-cross-validation">Option 3: k-Fold Cross Validation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-third-option-with-a-util-function">Let’s visualize the third option with a util function.</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-tune-your-hyperparameters">How to tune your hyperparameters</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-train-val-test-split">Option 1: Train-Val-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-k-fold-cross-validation">Option 2: k-Fold Cross Validation</a></li>
+</ul>
+</li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
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+    
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Basic model building and training</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Hyperparameter tuning with scikit-learn</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="hyperparameter-tuning-with-scikit-learn">
+<span id="sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"></span><h1>Hyperparameter tuning with scikit-learn<a class="headerlink" href="#hyperparameter-tuning-with-scikit-learn" title="Link to this heading">#</a></h1>
+<p>The braindecode provides some compatibility with
+<a class="reference external" href="https://scikit-learn.org/stable/">scikit-learn</a>. This allows us
+to use scikit-learn functionality to find the best hyperparameters for our
+model. This is especially useful to tune hyperparameters or
+parameters for one decoding task or a specific dataset.</p>
+<aside class="topic">
+<p class="topic-title">Why do you need to tune the neural networks model?</p>
+<p>Deep learning models are often sensitive to the choice of hyperparameters
+and parameters. Hyperparameters are the parameters set before
+training the model. The hyeperparameters determine (1) the capacity of the model,
+e.g. its depth (the number of layers) and its width (the number of
+convolutional kernels, sizes of fully connected layers) and (2) the
+learning process via the choice of optimizer and its learning rate,
+the number of epochs, the batch size, the choice of non-linearities,
+the strategies to initialize the learning weights, etc.
+On the other hand, parameters are learned during training,
+such as the neural network weights. The choice of these can have a
+significant impact on the performance of the model.
+Therefore, it is important to tune these to maximize the discriminative
+power of the model, in the case of decoding tasks (classification,
+regression, etc.), such as sleep staging, BCI, pathology detection, etc.
+We recommend the Deep Learning Tuning Playbook by Google to learn more
+about hyperparameters and parameters tuning <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+</aside>
+<p>In this tutorial, we will use the standard decoding approach to show the impact
+of the learning rate and dropout probability on the model’s performance.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id9">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id10">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id11">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-compute-windows" id="id12">Extraction of the Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-windows" id="id13">Extraction of the Windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id14">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#create-model" id="id15">Create model</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id16">Training</a></p></li>
+<li><p><a class="reference internal" href="#plotting-the-results" id="id17">Plotting the results</a></p></li>
+<li><p><a class="reference internal" href="#get-the-best-hyperparameters" id="id18">Get the best hyperparameters</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id19">References</a></p></li>
+</ul>
+</nav>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of
+braindecode to load datasets via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB</a> <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> to load the BCI
+Competition IV 2a data <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To load your own datasets either via mne or from
+preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
+Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
+Tutorial</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.moabb</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>In this example, preprocessing includes signal rescaling, the bandpass filtering
+(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+the exponential moving mean and variance.
+You can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>These prepocessings are now directly applied to the loaded
+data, and not on-the-fly applied as transformations in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>
+    <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                 <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Preprocess the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f42175a56a0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extraction-of-the-compute-windows">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Extraction of the Compute Windows</a><a class="headerlink" href="#extraction-of-the-compute-windows" title="Link to this heading">#</a></h3>
+</section>
+<section id="extraction-of-the-windows">
+<h3><a class="toc-backref" href="#id13" role="doc-backlink">Extraction of the Windows</a><a class="headerlink" href="#extraction-of-the-windows" title="Link to this heading">#</a></h3>
+<p>Extraction of the trials (windows) from the time series is based on the
+events inside the dataset. One event is the demarcation of the stimulus or
+the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+before the corresponding event and the duration of the event itself.
+#Therefore, we set the <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> to -0.5 [s] and the
+<code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> to 0 [s].</p>
+<p>We extract from the dataset the sampling frequency, which is the same for
+all datasets in this case, and we tested it.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> and <code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> are
+defined in seconds and need to be converted into samples (multiplication
+with the sampling frequency), relative to the event.
+This variable is dataset dependent.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id14" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
+<code class="docutils literal notranslate"><span class="pre">0train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">1test</span></code> for evaluation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-model">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the ShallowFBCSPNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
+learning with convolutional neural networks for EEG decoding and
+visualization</a> <a class="footnote-reference brackets" href="#id8" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">partial</span></a>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>  <span class="c1"># random seed to make results reproducible</span>
+<span class="c1"># Set random seed to be able to reproduce results</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># To analyze the impact of the different parameters inside the torch model, we</span>
+<span class="c1"># need to create partial initialisations. This is because the</span>
+<span class="c1"># GridSearchCV of scikit-learn will try to initialize the model with the</span>
+<span class="c1"># parameters we want to tune. If we do not do this, the GridSearchCV will</span>
+<span class="c1"># try to initialize the model with the parameters we want to tune but</span>
+<span class="c1"># without the parameters we do not want to tune. This will result in an</span>
+<span class="c1"># error.</span>
+<a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">partial</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+                <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+                <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id16" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>Now we train the network! EEGClassifier is a Braindecode object
+responsible for managing the training of neural networks. It inherits
+from <a class="reference external" href="https://skorch.readthedocs.io/en/latest/classifier.html">skorch.NeuralNetClassifier</a>,
+so the training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">16</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><span class="p">[],</span>  <span class="c1"># This will be handled by GridSearchCV</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">),</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We use scikit-learn <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a> to tune hyperparameters.
+To be able to do this, we slice the braindecode datasets that by default
+return a 3-tuple to return X and y, respectively.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The KFold object splits the datasets based on their
+length which corresponds to the number of compute windows. In
+this (trialwise) example this is fine to do. In a cropped setting
+this is not advisable since this might split compute windows
+of a single trial into both train and valid set.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">train_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">learning_rates</span></a> <span class="o">=</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.0000625</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">drop_probs</span></a> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;optimizer__lr&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">learning_rates</span></a><span class="p">,</span>
+    <span class="s1">&#39;module__drop_prob&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">drop_probs</span></a>
+<span class="p">}</span>
+
+<span class="c1"># By setting n_jobs=-1, grid search is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
+
+<span class="c1"># Extract the results into a DataFrame</span>
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 6 candidates, totalling 12 fits
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.5304        2.0774       0.1724            0.1724        3.1024  0.0063  0.4879
+      2            0.6174        1.1537       0.2069            0.2069        2.8703  0.0000  0.5034
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.3565        2.5936       0.3448            0.3448        2.3156  0.0063  0.5707
+      2            0.5130        1.1784       0.3448            0.3448        1.8568  0.0000  0.5759
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2348        1.5185       0.2759            0.2759        3.0333  0.0001  0.4766
+      2            0.2348        1.4272       0.2759            0.2759        2.3804  0.0000  0.4738
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2000        1.6046       0.3103            0.3103        4.8912  0.0001  0.4870
+      2            0.2000        1.3911       0.3103            0.3103        3.7741  0.0000  0.4852
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.5304        1.7828       0.1724            0.1724        2.9772  0.0063  0.4706
+      2            0.5826        1.1250       0.1724            0.1724        2.5764  0.0000  0.5568
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2609        2.5664       0.3793            0.3793        3.1107  0.0063  0.4900
+      2            0.4696        1.4854       0.4483            0.4483        1.7441  0.0000  0.4785
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2261        1.7070       0.3103            0.3103        2.1473  0.0001  0.4818
+      2            0.2435        1.6492       0.3103            0.3103        1.8568  0.0000  0.5188
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2783        1.5837       0.2069            0.2069        2.4035  0.0001  0.4722
+      2            0.2696        1.5464       0.2069            0.2069        2.1272  0.0000  0.4948
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2609        2.1099       0.3103            0.3103        3.2230  0.0063  0.5018
+      2            0.4783        1.8860       0.3448            0.3448        1.9010  0.0000  0.5334
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.4087        2.1420       0.4138            0.4138        1.5107  0.0063  0.4648
+      2            0.5130        1.5210       0.3793            0.3793        1.3905  0.0000  0.5912
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2348        2.1793       0.2759            0.2759        9.8493  0.0001  0.5489
+      2            0.2348        1.9960       0.2759            0.2759        7.3377  0.0000  0.5186
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2000        2.2516       0.3103            0.3103        2.5049  0.0001  0.4750
+      2            0.1913        2.1263       0.3448            0.3448        2.0233  0.0000  0.5064
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2739        2.2403       0.2586            0.2586        6.1633  0.0063  1.0886
+      2            0.5304        1.7535       0.3448            0.3448        1.9387  0.0000  0.9936
+</pre></div>
+</div>
+</section>
+<section id="plotting-the-results">
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">Plotting the results</a><a class="headerlink" href="#plotting-the-results" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+
+<span class="c1"># Create a pivot table for the heatmap</span>
+<span class="n">pivot_table</span> <span class="o">=</span> <span class="n">search_results</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s1">&#39;param_optimizer__lr&#39;</span><span class="p">,</span>
+                                   <span class="n">columns</span><span class="o">=</span><span class="s1">&#39;param_module__drop_prob&#39;</span><span class="p">,</span>
+                                   <span class="n">values</span><span class="o">=</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">)</span>
+<span class="c1"># Create the heatmap</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pivot_table</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;.3f&quot;</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;YlGnBu&quot;</span><span class="p">,</span> <span class="n">cbar</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s1">&#39;Grid Search Mean Test Scores&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">&#39;Learning Rate&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">&#39;Dropout Probability&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_001.png" srcset="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_001.png" alt="Grid Search Mean Test Scores" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py:332: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
+  pivot_table = search_results.pivot(index=&#39;param_optimizer__lr&#39;,
+/home/bru/PycharmProjects/braindecode-new/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py:332: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
+  pivot_table = search_results.pivot(index=&#39;param_optimizer__lr&#39;,
+</pre></div>
+</div>
+</section>
+<section id="get-the-best-hyperparameters">
+<h2><a class="toc-backref" href="#id18" role="doc-backlink">Get the best hyperparameters</a><a class="headerlink" href="#get-the-best-hyperparameters" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Best hyperparameters were </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> which gave a validation &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">% (training &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%).&quot;</span><span class="p">)</span>
+
+<span class="n">eval_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">eval_y</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><span class="n">eval_X</span><span class="p">,</span> <span class="n">eval_y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best hyperparameters were {&#39;module__drop_prob&#39;: 0.2, &#39;optimizer__lr&#39;: 0.00625} which gave a validation accuracy of 32.64% (training accuracy of 50.69%).
+Eval accuracy is 34.72%.
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,
+Zachary Nado (2022). Deep Learning Tuning Playbook.
+Github <a class="github reference external" href="https://github.com/google-research/tuning_playbook">google-research/tuning_playbook</a></p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id8" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">4</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 28.782 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  164 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py">
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+</div>
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+<p><a class="reference download internal" download="" href="../../_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_hyperparameter_tuning_with_scikit-learn.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
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+</nav></div>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Basic model building and training</a></li>
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+    <li class="breadcrumb-item active" aria-current="page">Convolutional neural network regression model on fake data.</li>
+  </ul>
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+      
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+</div>
+              
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+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="convolutional-neural-network-regression-model-on-fake-data">
+<span id="sphx-glr-auto-examples-model-building-plot-regression-py"></span><h1>Convolutional neural network regression model on fake data.<a class="headerlink" href="#convolutional-neural-network-regression-model-on-fake-data" title="Link to this heading">#</a></h1>
+<p>This example shows how to create a CNN regressor from a CNN classifier by removing <cite>softmax</cite>
+function from the classifier’s output layer and how to train it on a fake regression dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#          Sara Sedlar &lt;sara.sedlar@gmail.com&gt;</span>
+<span class="c1"># License: BSD-3</span>
+</pre></div>
+</div>
+<section id="fake-regression-data">
+<h2>Fake regression data<a class="headerlink" href="#fake-regression-data" title="Link to this heading">#</a></h2>
+<p>Function for generation of the fake regression dataset generates <cite>n_fake_recs</cite> recordings,
+each containing sinusoidal signals with Gaussian noise. Each fake recording signal has
+<cite>n_fake_chs</cite> channels, it lasts <cite>fake_duration</cite> [s] and it is sampled with <cite>fake_sfreq</cite> [Hz].
+The recordings are split into train, validation and testing sessions.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <span class="n">create_mne_dummy_raw</span>
+</pre></div>
+</div>
+<p>Function for generating fake regression data</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fake_regression_dataset</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">,</span> <span class="n">n_fake_chs</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                            <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                            <span class="n">fake_data_split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a fake regression dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_fake_recs : int</span>
+<span class="sd">        Number of fake recordings.</span>
+<span class="sd">    n_fake_chs : int</span>
+<span class="sd">        Number of fake EEG channels.</span>
+<span class="sd">    fake_sfreq : float</span>
+<span class="sd">        Fake sampling frequency in Hz.</span>
+<span class="sd">    fake_duration : float</span>
+<span class="sd">        Fake recording duration in seconds.</span>
+<span class="sd">    n_fake_targets : int</span>
+<span class="sd">        Number of targets.</span>
+<span class="sd">    fake_data_split : list</span>
+<span class="sd">        List of train/valid/test subset fractions.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dataset : BaseConcatDataset object</span>
+<span class="sd">        The generated dataset object.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">datasets</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">int</span><span class="p">(</span><span class="n">fake_data_split</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_fake_recs</span><span class="p">):</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;train&quot;</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">int</span><span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">fake_data_split</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">n_fake_recs</span><span class="p">):</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;valid&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;test&quot;</span>
+        <span class="n">raw</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <span class="n">create_mne_dummy_raw</span><span class="p">(</span><span class="n">n_channels</span><span class="o">=</span><span class="n">n_fake_chs</span><span class="p">,</span>
+                                      <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                                      <span class="n">sfreq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">)</span>
+
+        <span class="n">target</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">*</span> <span class="n">target</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">raw</span><span class="o">.</span><span class="n">times</span><span class="p">)</span>
+            <span class="n">raw</span><span class="o">.</span><span class="n">_data</span> <span class="o">+=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.expand_dims.html#numpy.expand_dims" title="numpy.expand_dims" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">fake_description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">target</span><span class="p">,</span> <span class="n">target_subset</span><span class="p">],</span>
+                                     <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;target&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">])</span>
+        <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <a href="../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">fake_description</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">))</span>
+
+    <span class="k">return</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="generating-fake-regression-dataset">
+<h2>Generating fake regression dataset<a class="headerlink" href="#generating-fake-regression-dataset" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_rec</span></a> <span class="o">=</span> <span class="mi">20</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">=</span> <span class="mi">21</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a> <span class="o">=</span> <span class="mi">1</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <span class="n">fake_regression_dataset</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_rec</span></a><span class="p">,</span>
+                                  <span class="n">n_fake_chs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+</pre></div>
+</div>
+</section>
+<section id="defining-a-cnn-regression-model">
+<h2>Defining a CNN regression model<a class="headerlink" href="#defining-a-cnn-regression-model" title="Link to this heading">#</a></h2>
+<p>Choosing and defining a CNN classifier, <cite>ShallowFBCSPNet</cite> or <cite>Deep4Net</cite>, introduced in <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+To convert a classifier to a regressor, <cite>softmax</cite> function is removed from its output layer.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="c1"># Choosing a CNN model</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">=</span> <span class="s2">&quot;shallow&quot;</span>  <span class="c1"># &#39;shallow&#39; or &#39;deep&#39;</span>
+
+<span class="c1"># Defining a CNN model</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;shallow&quot;</span><span class="p">,</span> <span class="s2">&quot;Shallow&quot;</span><span class="p">,</span> <span class="s2">&quot;ShallowConvNet&quot;</span><span class="p">]:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span><span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span>
+                            <span class="n">n_classes</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                            <span class="n">input_window_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                            <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+                            <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                            <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,)</span>
+<span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;deep&quot;</span><span class="p">,</span> <span class="s2">&quot;Deep&quot;</span><span class="p">,</span> <span class="s2">&quot;DeepConvNet&quot;</span><span class="p">]:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a><span class="p">(</span><span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                     <span class="n">input_window_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                     <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+                     <span class="n">stride_before_pool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                     <span class="n">n_filters_2</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span>
+                     <span class="n">n_filters_3</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span>
+                     <span class="n">n_filters_4</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span>
+                     <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                     <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a><span class="si">}</span><span class="s1"> unknown&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;in_chans&#39; is depreciated. Use &#39;n_chans&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;n_classes&#39; is depreciated. Use &#39;n_outputs&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+</pre></div>
+</div>
+</section>
+<section id="choosing-between-gpu-and-cpu-processors">
+<h2>Choosing between GPU and CPU processors<a class="headerlink" href="#choosing-between-gpu-and-cpu-processors" title="Link to this heading">#</a></h2>
+<p>By default, model’s training and evaluation take place at GPU if it exists, otherwise on CPU.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Setting a random seed</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="data-windowing">
+<h2>Data windowing<a class="headerlink" href="#data-windowing" title="Link to this heading">#</a></h2>
+<p>Windowing data with a sliding window into the epochs of the size <cite>window_size_samples</cite>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models.util</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span><span class="p">,</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">//</span> <span class="mi">3</span>
+<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">)[</span>
+    <span class="mi">2</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+                                              <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                                              <span class="n">stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                                              <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+                                              <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+                                              <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                              <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Splitting windowed data into train, valid and test subsets.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function to_dense_prediction_model is deprecated; will be removed in version 1.0. Use EEGModuleMixin.to_dense_prediction_model method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function get_output_shape is deprecated; will be removed in version 1.0. Use EEGModuleMixin.get_output_shape method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/windowers.py:610: UserWarning: Meaning of `trial_stop_offset_samples`=0 has changed, use `None` to indicate end of trial/recording. Using `None`.
+  warnings.warn(
+</pre></div>
+</div>
+</section>
+<section id="model-training">
+<h2>Model training<a class="headerlink" href="#model-training" title="Link to this heading">#</a></h2>
+<p>Model is trained by minimizing MSE loss between ground truth and estimated value averaged over
+a period of time using AdamW optimizer <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>. Learning rate is managed by CosineAnnealingLR
+learning rate scheduler.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training.losses</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a> <span class="o">=</span> <span class="mf">0.001</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
+                         <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span></a><span class="p">,</span>
+                         <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+                         <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a><span class="p">,</span>
+                         <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a><span class="p">,</span>
+                         <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+                         <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+                         <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;neg_root_mean_squared_error&quot;</span><span class="p">,</span>
+                                    <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span>
+                                     <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span>
+                                                 <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">))],</span>
+                         <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span> <span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_neg_root_mean_squared_error    valid_loss    valid_neg_root_mean_squared_error      lr     dur
+-------  ------------  -----------------------------------  ------------  -----------------------------------  ------  ------
+      1        8.6188                              -1.3442        0.3962                              -0.6291  0.0010  0.8232
+      2        1.3336                              -0.9205        0.8489                              -0.9209  0.0005  0.9232
+      3        1.9584                              -0.6824        0.3750                              -0.6087  0.0000  0.8321
+</pre></div>
+</div>
+</section>
+<section id="model-evaluation">
+<h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Link to this heading">#</a></h2>
+<p>Plotting training and validation losses and negative root mean square error</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Train and valid losses&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;train_loss&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;valid_loss&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Cropped MSE loss&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Train&quot;</span><span class="p">,</span> <span class="s2">&quot;Valid&quot;</span><span class="p">])</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Train and valid errors&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;train_neg_root_mean_squared_error&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;valid_neg_root_mean_squared_error&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Negative RMSE&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Train&quot;</span><span class="p">,</span> <span class="s2">&quot;Valid&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_regression_001.png" srcset="../../_images/sphx_glr_plot_regression_001.png" alt="Train and valid losses, Train and valid errors" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x7f8b79736940&gt;
+</pre></div>
+</div>
+</section>
+<section id="model-testing">
+<h2>Model testing<a class="headerlink" href="#model-testing" title="Link to this heading">#</a></h2>
+<p>Plotting a scatter plot of estimated versus target values and corresponding trend line.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ravel.html#numpy.ravel" title="numpy.ravel" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ravel</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">))</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()[</span><span class="s2">&quot;target&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Estimated targets.&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Ground truth targets.&quot;</span><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">z</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html#numpy.polyfit" title="numpy.polyfit" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">polyfit</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">p</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class"><span class="n">np</span><span class="o">.</span><span class="n">poly1d</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">z</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">p</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">),</span> <span class="s2">&quot;r--&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_regression_002.png" srcset="../../_images/sphx_glr_plot_regression_002.png" alt="plot regression" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id4" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., … &amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Human brain mapping, 38(11), 5391-5420.</p>
+</aside>
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kingma, Diederik P., and Jimmy Ba.
+“Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.
+“On the convergence of adam and beyond.” arXiv preprint arXiv:1904.09237 (2019).</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.678 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  78 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-regression-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_regression.py</span></code></a></p>
+</div>
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+<p><a class="reference download internal" download="" href="../../_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_regression.ipynb</span></code></a></p>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#fake-regression-data">Fake regression data</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#generating-fake-regression-dataset">Generating fake regression dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#defining-a-cnn-regression-model">Defining a CNN regression model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#choosing-between-gpu-and-cpu-processors">Choosing between GPU and CPU processors</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-windowing">Data windowing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-training">Model training</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+      
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+              
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="training-a-braindecode-model-in-pytorch">
+<span id="sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"></span><h1>Training a Braindecode model in PyTorch<a class="headerlink" href="#training-a-braindecode-model-in-pytorch" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train a Braindecode model with PyTorch. The data
+preparation and model instantiation steps are identical to that of the tutorial
+<a class="reference external" href="./plot_how_train_test_and_tune.html">How to train, test and tune your model</a></p>
+<p>We will use the BCIC IV 2a dataset as a showcase example.</p>
+<p>The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing,
+defining a model, and other details which are not exclusive to this page (compare
+<a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Cropped Decoding Tutorial</a>). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.</p>
+<p>The goal of this tutorial is to present braindecode in the PyTorch perceptive.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#why-should-i-care-about-model-evaluation" id="id1">Why should I care about model evaluation?</a></p></li>
+<li><p><a class="reference internal" href="#loading-preprocessing-defining-a-model-etc" id="id2">Loading, preprocessing, defining a model, etc.</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-the-dataset-structure" id="id3">Loading the Dataset Structure</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing-the-offline-transformation-of-the-raw-dataset" id="id4">Preprocessing, the offline transformation of the raw dataset</a></p></li>
+<li><p><a class="reference internal" href="#cut-compute-windows" id="id5">Cut Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#create-pytorch-model" id="id6">Create Pytorch model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-train-and-evaluate-your-model" id="id7">How to train and evaluate your model</a></p>
+<ul>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-test" id="id8">Split dataset into train and test</a></p></li>
+<li><p><a class="reference internal" href="#option-1-pure-pytorch-training-loop" id="id9">Option 1: Pure PyTorch training loop</a></p></li>
+<li><p><a class="reference internal" href="#option-2-train-it-with-pytorch-lightning" id="id10">Option 2: Train it with PyTorch Lightning</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<section id="why-should-i-care-about-model-evaluation">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Why should I care about model evaluation?</a><a class="headerlink" href="#why-should-i-care-about-model-evaluation" title="Link to this heading">#</a></h2>
+<p>Short answer: To produce reliable results!</p>
+<p>In machine learning, we usually follow the scheme of splitting the
+data into two parts, training and testing sets. It sounds like a
+simple division, right? But the story does not end here.</p>
+<p>While developing a ML model you usually have to adjust and tune
+hyperparameters of your model or pipeline (e.g., number of layers,
+learning rate, number of epochs). Deep learning models usually have
+many free parameters; they could be considered complex models with
+many degrees of freedom. If you kept using the test dataset to
+evaluate your adjustmentyou would run into data leakage.</p>
+<p>This means that if you use the test set to adjust the hyperparameters
+of your model, the model implicitly learns or memorizes the test set.
+Therefore, the trained model is no longer independent of the test set
+(even though it was never used for training explicitly!).
+If you perform any hyperparameter tuning, you need a third split,
+the so-called validation set.</p>
+<p>This tutorial shows the three basic schemes for training and evaluating
+the model as well as two methods to tune your hyperparameters.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>You might recognize that the accuracy gets better throughout
+the experiments of this tutorial. The reason behind that is that
+we always use the same model with the same parameters in every
+segment to keep the tutorial short and readable. If you do your
+own experiments you always have to reinitialize the model before
+training.</p>
+</div>
+</section>
+<section id="loading-preprocessing-defining-a-model-etc">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Loading, preprocessing, defining a model, etc.</a><a class="headerlink" href="#loading-preprocessing-defining-a-model-etc" title="Link to this heading">#</a></h2>
+<section id="loading-the-dataset-structure">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Loading the Dataset Structure</a><a class="headerlink" href="#loading-the-dataset-structure" title="Link to this heading">#</a></h3>
+<p>Here, we have a data structure with equal behavior to the Pytorch Dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing-the-offline-transformation-of-the-raw-dataset">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Preprocessing, the offline transformation of the raw dataset</a><a class="headerlink" href="#preprocessing-the-offline-transformation-of-the-raw-dataset" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.0</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.0</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;pick_types&quot;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+        <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
+    <span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;filter&quot;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f42181b4160&gt;
+</pre></div>
+</div>
+</section>
+<section id="cut-compute-windows">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Cut Compute Windows</a><a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="create-pytorch-model">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Create Pytorch model</a><a class="headerlink" href="#create-pytorch-model" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># The ShallowFBCSPNet is a `nn.Sequential` model</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/bru/PycharmProjects/braindecode-new/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-train-and-evaluate-your-model">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">How to train and evaluate your model</a><a class="headerlink" href="#how-to-train-and-evaluate-your-model" title="Link to this heading">#</a></h2>
+<section id="split-dataset-into-train-and-test">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Split dataset into train and test</a><a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case the <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We
+select <code class="docutils literal notranslate"><span class="pre">Train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">test</span></code> for testing.
+For other datasets, you might have to choose another column.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>No matter which of the three schemes you use, this initial
+two-fold split into train_set and test_set always remains the same.
+Remember that you are not allowed to use the test_set during any
+stage of training or tuning.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="option-1-pure-pytorch-training-loop">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Option 1: Pure PyTorch training loop</a><a class="headerlink" href="#option-1-pure-pytorch-training-loop" title="Link to this heading">#</a></h3>
+<img alt="Pytorch logo" src="https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png" />
+<p><cite>model</cite> is an instance of <cite>torch.nn.Module</cite>, and can as such be trained
+using PyTorch optimization capabilities.
+The following training scheme is simple as the dataset is only
+split into two distinct sets (<code class="docutils literal notranslate"><span class="pre">train_set</span></code> and <code class="docutils literal notranslate"><span class="pre">test_set</span></code>).
+This scheme uses no separate validation split and should only be
+used for the final evaluation of the (previously!) found
+hyperparameters configuration.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>If you make any use of the <code class="docutils literal notranslate"><span class="pre">test_set</span></code> during training
+(e.g. by using EarlyStopping) there will be data leakage
+which will make the reported generalization capability/decoding
+performance of your model less credible.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The parameter values showcased here for optimizing the network are
+chosen to make this tutorial fast to run and build. Real-world values
+would be higher, especially when it comes to n_epochs.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a>
+<span class="kn">from</span> <span class="nn">torch.optim.lr_scheduler</span> <span class="kn">import</span> <span class="n">LRScheduler</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+</pre></div>
+</div>
+<p>The following method runs one training epoch over the dataloader for the
+given model. It needs a loss function, optimization algorithm, and
+learning rate updating callback.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="c1"># Define a method for training one epoch</span>
+
+
+<span class="k">def</span> <span class="nf">train_one_epoch</span><span class="p">(</span>
+        <span class="n">dataloader</span><span class="p">:</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">:</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">:</span> <span class="n">LRScheduler</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span> <span class="n">print_batch_stats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">):</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train" title="torch.nn.Module.train" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">train</span></a><span class="p">()</span>  <span class="c1"># Set the model to training mode</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="p">,</span> <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+
+    <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
+                        <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">print_batch_stats</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">:</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW.zero_grad" title="torch.optim.AdamW.zero_grad" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-method"><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span></a><span class="p">()</span>
+        <span class="n">pred</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="o">.</span><span class="n">step</span><span class="p">()</span>  <span class="c1"># update the model weights</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW.zero_grad" title="torch.optim.AdamW.zero_grad" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-method"><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span></a><span class="p">()</span>
+
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+            <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="si">}</span><span class="s2">/</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Batch </span><span class="si">{</span><span class="n">batch_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Loss: </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <span class="c1"># Update the learning rate</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+
+    <span class="n">correct</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+    <span class="k">return</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">correct</span>
+</pre></div>
+</div>
+<p>Very similarly, the evaluation function loops over the entire dataloader
+and accumulate the metrics, but doesn’t update the model weights.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
+<span class="k">def</span> <span class="nf">test_model</span><span class="p">(</span>
+    <span class="n">dataloader</span><span class="p">:</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">:</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <span class="n">print_batch_stats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">):</span>
+    <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+    <span class="n">n_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval" title="torch.nn.Module.eval" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">eval</span></a><span class="p">()</span>  <span class="c1"># Switch to evaluation mode</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+
+    <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+        <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">progress_bar</span> <span class="o">=</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">:</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+        <span class="n">pred</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">batch_loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a> <span class="o">+=</span> <span class="n">batch_loss</span>
+        <span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">float</span></a><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+            <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Batch </span><span class="si">{</span><span class="n">batch_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Loss: </span><span class="si">{</span><span class="n">batch_loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a> <span class="o">/=</span> <span class="n">n_batches</span>
+    <span class="n">correct</span> <span class="o">/=</span> <span class="n">size</span>
+
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;Test Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">correct</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%, Test Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <span class="n">correct</span>
+
+
+<span class="c1"># Define the optimization</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters" title="torch.nn.Module.parameters" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">parameters</span></a><span class="p">(),</span>
+                              <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">CosineAnnealingLR</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+                                                       <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Define the loss function</span>
+<span class="c1"># We used the NNLoss function, which expects log probabilities as input</span>
+<span class="c1"># (which is the case for our model output)</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">()</span>
+
+<span class="c1"># train_set and test_set are instances of torch Datasets, and can seamlessly be</span>
+<span class="c1"># wrapped in data loaders.</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">)</span>
+
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="si">}</span><span class="s2">/</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="si">}</span><span class="s2">: &quot;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_accuracy</span></a> <span class="o">=</span> <span class="n">train_one_epoch</span><span class="p">(</span>
+        <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_accuracy</span></a> <span class="o">=</span> <span class="n">test_model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;Train Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_accuracy</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Average Train Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Test Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_accuracy</span></a><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Average Test Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/2:
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 1/2, Batch 1/5, Loss: 1.784639:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 1/2, Batch 1/5, Loss: 1.784639:  20%|██        | 1/5 [00:00&lt;00:01,  3.78it/s]
+Epoch 1/2, Batch 2/5, Loss: 1.497504:  20%|██        | 1/5 [00:00&lt;00:01,  3.78it/s]
+Epoch 1/2, Batch 2/5, Loss: 1.497504:  40%|████      | 2/5 [00:00&lt;00:00,  3.62it/s]
+Epoch 1/2, Batch 3/5, Loss: 1.544960:  40%|████      | 2/5 [00:00&lt;00:00,  3.62it/s]
+Epoch 1/2, Batch 3/5, Loss: 1.544960:  60%|██████    | 3/5 [00:00&lt;00:00,  3.64it/s]
+Epoch 1/2, Batch 4/5, Loss: 1.552846:  60%|██████    | 3/5 [00:01&lt;00:00,  3.64it/s]
+Epoch 1/2, Batch 4/5, Loss: 1.552846:  80%|████████  | 4/5 [00:01&lt;00:00,  3.66it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800:  80%|████████  | 4/5 [00:01&lt;00:00,  3.66it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800: 100%|██████████| 5/5 [00:01&lt;00:00,  4.51it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800: 100%|██████████| 5/5 [00:01&lt;00:00,  4.09it/s]
+
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 1/5, Loss: 4.067235:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 4.475664:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 4.475664:  40%|████      | 2/5 [00:00&lt;00:00, 17.63it/s]
+Batch 3/5, Loss: 4.224986:  40%|████      | 2/5 [00:00&lt;00:00, 17.63it/s]
+Batch 4/5, Loss: 4.333445:  40%|████      | 2/5 [00:00&lt;00:00, 17.63it/s]
+Batch 4/5, Loss: 4.333445:  80%|████████  | 4/5 [00:00&lt;00:00, 16.38it/s]
+Batch 5/5, Loss: 4.520739:  80%|████████  | 4/5 [00:00&lt;00:00, 16.38it/s]
+Batch 5/5, Loss: 4.520739: 100%|██████████| 5/5 [00:00&lt;00:00, 17.92it/s]
+Test Accuracy: 25.3%, Test Loss: 4.324413
+
+Train Accuracy: 30.90%, Average Train Loss: 1.605750, Test Accuracy: 25.3%, Average Test Loss: 4.324413
+
+Epoch 2/2:
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 2/2, Batch 1/5, Loss: 1.460681:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 2/2, Batch 1/5, Loss: 1.460681:  20%|██        | 1/5 [00:00&lt;00:01,  3.96it/s]
+Epoch 2/2, Batch 2/5, Loss: 1.178580:  20%|██        | 1/5 [00:00&lt;00:01,  3.96it/s]
+Epoch 2/2, Batch 2/5, Loss: 1.178580:  40%|████      | 2/5 [00:00&lt;00:00,  4.16it/s]
+Epoch 2/2, Batch 3/5, Loss: 1.232657:  40%|████      | 2/5 [00:00&lt;00:00,  4.16it/s]
+Epoch 2/2, Batch 3/5, Loss: 1.232657:  60%|██████    | 3/5 [00:00&lt;00:00,  4.21it/s]
+Epoch 2/2, Batch 4/5, Loss: 1.294503:  60%|██████    | 3/5 [00:00&lt;00:00,  4.21it/s]
+Epoch 2/2, Batch 4/5, Loss: 1.294503:  80%|████████  | 4/5 [00:00&lt;00:00,  4.20it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602:  80%|████████  | 4/5 [00:01&lt;00:00,  4.20it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602: 100%|██████████| 5/5 [00:01&lt;00:00,  5.13it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602: 100%|██████████| 5/5 [00:01&lt;00:00,  4.65it/s]
+
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 1/5, Loss: 3.412786:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 3.751324:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 3.751324:  40%|████      | 2/5 [00:00&lt;00:00, 17.68it/s]
+Batch 3/5, Loss: 3.565480:  40%|████      | 2/5 [00:00&lt;00:00, 17.68it/s]
+Batch 4/5, Loss: 3.667802:  40%|████      | 2/5 [00:00&lt;00:00, 17.68it/s]
+Batch 4/5, Loss: 3.667802:  80%|████████  | 4/5 [00:00&lt;00:00, 17.73it/s]
+Batch 5/5, Loss: 3.836635:  80%|████████  | 4/5 [00:00&lt;00:00, 17.73it/s]
+Batch 5/5, Loss: 3.836635: 100%|██████████| 5/5 [00:00&lt;00:00, 19.57it/s]
+Test Accuracy: 24.7%, Test Loss: 3.646806
+
+Train Accuracy: 44.10%, Average Train Loss: 1.262205, Test Accuracy: 24.7%, Average Test Loss: 3.646806
+</pre></div>
+</div>
+</section>
+<section id="option-2-train-it-with-pytorch-lightning">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Option 2: Train it with PyTorch Lightning</a><a class="headerlink" href="#option-2-train-it-with-pytorch-lightning" title="Link to this heading">#</a></h3>
+<img alt="Pytorch Lightning logo" src="https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png" />
+<p>Alternatively, lightning provides a nice interface around torch modules
+which integrates the previous logic.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">lightning</span> <span class="k">as</span> <span class="nn">L</span>
+<span class="kn">from</span> <span class="nn">torchmetrics.functional</span> <span class="kn">import</span> <span class="n">accuracy</span>
+
+
+<span class="k">class</span> <span class="nc">LitModule</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">L</span><span class="o">.</span><span class="n">LightningModule</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">):</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">batch</span>
+        <span class="n">y_hat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="s2">&quot;train_loss&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span>
+
+    <span class="k">def</span> <span class="nf">test_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">):</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">batch</span>
+        <span class="n">y_hat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+        <span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;test_acc&quot;</span><span class="p">:</span> <span class="n">acc</span><span class="p">,</span> <span class="s2">&quot;test_loss&quot;</span><span class="p">:</span> <span class="n">loss</span><span class="p">}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log_dict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">metrics</span>
+
+    <span class="k">def</span> <span class="nf">configure_optimizers</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters" title="torch.nn.Module.parameters" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">parameters</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+                                      <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">)</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">CosineAnnealingLR</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+                                                               <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">],</span> <span class="p">[</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">]</span>
+
+
+<span class="c1"># Creating the trainer with max_epochs=2 for demonstration purposes</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">L</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+<span class="c1"># Create and train the LightningModule</span>
+<span class="n">lit_model</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">LitModule</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">lit_model</span><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a><span class="p">)</span>
+
+<span class="c1"># After training, you can test the model using the test DataLoader</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">dataloaders</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GPU available: False, used: False
+TPU available: False, using: 0 TPU cores
+IPU available: False, using: 0 IPUs
+HPU available: False, using: 0 HPUs
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
+  warning_cache.warn(
+
+  | Name   | Type            | Params
+-------------------------------------------
+0 | module | ShallowFBCSPNet | 47.4 K
+1 | loss   | NLLLoss         | 0
+-------------------------------------------
+47.4 K    Trainable params
+0         Non-trainable params
+47.4 K    Total params
+0.189     Total estimated model params size (MB)
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
+  rank_zero_warn(
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/lightning/pytorch/loops/fit_loop.py:281: PossibleUserWarning: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
+  rank_zero_warn(
+
+Training: 0it [00:00, ?it/s]
+Training:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 0:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 0:  20%|██        | 1/5 [00:00&lt;00:01,  2.73it/s]
+Epoch 0:  20%|██        | 1/5 [00:00&lt;00:01,  2.73it/s, v_num=3]
+Epoch 0:  40%|████      | 2/5 [00:00&lt;00:00,  3.41it/s, v_num=3]
+Epoch 0:  40%|████      | 2/5 [00:00&lt;00:00,  3.41it/s, v_num=3]
+Epoch 0:  60%|██████    | 3/5 [00:00&lt;00:00,  3.68it/s, v_num=3]
+Epoch 0:  60%|██████    | 3/5 [00:00&lt;00:00,  3.68it/s, v_num=3]
+Epoch 0:  80%|████████  | 4/5 [00:01&lt;00:00,  3.86it/s, v_num=3]
+Epoch 0:  80%|████████  | 4/5 [00:01&lt;00:00,  3.86it/s, v_num=3]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  4.34it/s, v_num=3]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  4.34it/s, v_num=3]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  4.34it/s, v_num=3]
+Epoch 0:   0%|          | 0/5 [00:00&lt;?, ?it/s, v_num=3]
+Epoch 1:   0%|          | 0/5 [00:00&lt;?, ?it/s, v_num=3]
+Epoch 1:  20%|██        | 1/5 [00:00&lt;00:00,  4.54it/s, v_num=3]
+Epoch 1:  20%|██        | 1/5 [00:00&lt;00:00,  4.54it/s, v_num=3]
+Epoch 1:  40%|████      | 2/5 [00:00&lt;00:00,  4.35it/s, v_num=3]
+Epoch 1:  40%|████      | 2/5 [00:00&lt;00:00,  4.35it/s, v_num=3]
+Epoch 1:  60%|██████    | 3/5 [00:00&lt;00:00,  4.17it/s, v_num=3]
+Epoch 1:  60%|██████    | 3/5 [00:00&lt;00:00,  4.17it/s, v_num=3]
+Epoch 1:  80%|████████  | 4/5 [00:00&lt;00:00,  4.24it/s, v_num=3]
+Epoch 1:  80%|████████  | 4/5 [00:00&lt;00:00,  4.24it/s, v_num=3]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  4.72it/s, v_num=3]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  4.72it/s, v_num=3]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  4.72it/s, v_num=3]`Trainer.fit` stopped: `max_epochs=2` reached.
+
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  4.70it/s, v_num=3]
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/checkpoint_connector.py:149: UserWarning: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.test(ckpt_path=&#39;best&#39;)` to use the best model or `.test(ckpt_path=&#39;last&#39;)` to use the last model. If you pass a value, this warning will be silenced.
+  rank_zero_warn(
+Restoring states from the checkpoint path at /home/bru/PycharmProjects/braindecode-new/examples/model_building/lightning_logs/version_3/checkpoints/epoch=1-step=10.ckpt
+Loaded model weights from the checkpoint at /home/bru/PycharmProjects/braindecode-new/examples/model_building/lightning_logs/version_3/checkpoints/epoch=1-step=10.ckpt
+/home/bru/PycharmProjects/braindecode-2023/venv/lib/python3.9/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:442: PossibleUserWarning: The dataloader, test_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
+  rank_zero_warn(
+
+Testing: 0it [00:00, ?it/s]
+Testing:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Testing DataLoader 0:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Testing DataLoader 0:  20%|██        | 1/5 [00:00&lt;00:00, 15.08it/s]
+Testing DataLoader 0:  40%|████      | 2/5 [00:00&lt;00:00, 15.29it/s]
+Testing DataLoader 0:  60%|██████    | 3/5 [00:00&lt;00:00, 14.97it/s]
+Testing DataLoader 0:  80%|████████  | 4/5 [00:00&lt;00:00, 14.88it/s]
+Testing DataLoader 0: 100%|██████████| 5/5 [00:00&lt;00:00, 16.07it/s]
+Testing DataLoader 0: 100%|██████████| 5/5 [00:00&lt;00:00, 15.96it/s]
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃        Test metric        ┃       DataLoader 0        ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│         test_acc          │          0.34375          │
+│         test_loss         │     1.510026454925537     │
+└───────────────────────────┴───────────────────────────┘
+
+[{&#39;test_acc&#39;: 0.34375, &#39;test_loss&#39;: 1.510026454925537}]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.874 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  546 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-should-i-care-about-model-evaluation">Why should I care about model evaluation?</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-preprocessing-defining-a-model-etc">Loading, preprocessing, defining a model, etc.</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-dataset-structure">Loading the Dataset Structure</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing-the-offline-transformation-of-the-raw-dataset">Preprocessing, the offline transformation of the raw dataset</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-compute-windows">Cut Compute Windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pytorch-model">Create Pytorch model</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-train-and-evaluate-your-model">How to train and evaluate your model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-test">Split dataset into train and test</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-pure-pytorch-training-loop">Option 1: Pure PyTorch training loop</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-train-it-with-pytorch-lightning">Option 2: Train it with PyTorch Lightning</a></li>
+</ul>
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new file mode 100644
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+              
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+  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-model-building-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>01:55.889</strong> total execution time for <strong>auto_examples_model_building</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_how_train_test_and_tune.py</span></code>)</p></td>
+<td><p>00:35.248</p></td>
+<td><p>536.4 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_hyperparameter_tuning_with_scikit-learn.py</span></code>)</p></td>
+<td><p>00:28.782</p></td>
+<td><p>164.1 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_cropped.py</span></code>)</p></td>
+<td><p>00:24.412</p></td>
+<td><p>789.6 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.py</span></code>)</p></td>
+<td><p>00:12.874</p></td>
+<td><p>545.6 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code>)</p></td>
+<td><p>00:12.360</p></td>
+<td><p>502.1 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic_training_epochs.py</span></code>)</p></td>
+<td><p>00:02.213</p></td>
+<td><p>43.6 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_regression.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
+<td><p>0.0 MB</p></td>
+</tr>
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+        
+      </div>
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+    <li class="breadcrumb-item active" aria-current="page">How to cite Braindecode</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="how-to-cite-braindecode">
+<span id="cite"></span><h1>How to cite Braindecode<a class="headerlink" href="#how-to-cite-braindecode" title="Link to this heading">#</a></h1>
+<section id="citing-the-braindecode">
+<h2>Citing the Braindecode<a class="headerlink" href="#citing-the-braindecode" title="Link to this heading">#</a></h2>
+<p>If you are using the library in a scientific context, to cite the braindecode software,
+you can use the canonical journal article reference</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="w"> </span><span class="p">{</span><span class="nl">HBM:HBM23730</span><span class="p">,</span>
+<span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,</span>
+<span class="s">  Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and</span>
+<span class="s">  Hutter, Frank and Burgard, Wolfram and Ball, Tonio}</span><span class="p">,</span>
+<span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Deep learning with convolutional neural networks for EEG decoding and visualization}</span><span class="p">,</span>
+<span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Human Brain Mapping}</span><span class="p">,</span>
+<span class="na">issn</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1097-0193}</span><span class="p">,</span>
+<span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{http://dx.doi.org/10.1002/hbm.23730}</span><span class="p">,</span>
+<span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.1002/hbm.23730}</span><span class="p">,</span>
+<span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{aug}</span><span class="p">,</span>
+<span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2017}</span><span class="p">,</span>
+<span class="na">keywords</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,</span>
+<span class="s">  brain–computer interface, model interpretability, brain mapping}</span><span class="p">,</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="citing-the-mne">
+<h2>Citing the MNE<a class="headerlink" href="#citing-the-mne" title="Link to this heading">#</a></h2>
+<p>Braindecode is built on top of the <a class="reference external" href="https://mne.tools">MNE-Python</a> software.
+you can use the DOIs provided by <a class="reference external" href="https://doi.org/10.5281/zenodo.592483">Zenodo</a>.
+Additionally, we also ask that when citing the MNE-Python package,
+you cite the canonical journal article reference below:</p>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="p">{</span><span class="nl">10.3389/fnins.2013.00267</span><span class="p">,</span>
+<span class="na">author</span><span class="p">=</span><span class="s">{Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann,</span>
+<span class="s">        Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas,</span>
+<span class="s">        Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti}</span><span class="p">,</span>
+<span class="na">title</span><span class="p">=</span><span class="s">{{MEG and EEG data analysis with MNE-Python}}</span><span class="p">,</span>
+<span class="na">journal</span><span class="p">=</span><span class="s">{Frontiers in Neuroscience}</span><span class="p">,</span>
+<span class="na">volume</span><span class="p">=</span><span class="s">{7}</span><span class="p">,</span>
+<span class="na">pages</span><span class="p">=</span><span class="s">{267}</span><span class="p">,</span>
+<span class="na">year</span><span class="p">=</span><span class="s">{2013}</span><span class="p">,</span>
+<span class="na">url</span><span class="p">=</span><span class="s">{https://www.frontiersin.org/article/10.3389/fnins.2013.00267}</span><span class="p">,</span>
+<span class="na">doi</span><span class="p">=</span><span class="s">{10.3389/fnins.2013.00267}</span><span class="p">,</span>
+<span class="na">issn</span><span class="p">=</span><span class="s">{1662-453X}</span><span class="p">,</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="citing-other-algorithms">
+<h2>Citing other algorithms<a class="headerlink" href="#citing-other-algorithms" title="Link to this heading">#</a></h2>
+<p>Depending on your research topic, it may also be appropriate to cite related
+method papers, some of which are listed in the documentation strings of the
+relevant functions or methods. We recommend always check the documentation
+of the function.</p>
+</section>
+</section>
+
+
+  
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-the-braindecode">Citing the Braindecode</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-the-mne">Citing the MNE</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-other-algorithms">Citing other algorithms</a></li>
+</ul>
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+    
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+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
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diff --git a/0.8/generated/braindecode.augmentation.AugmentedDataLoader.html b/0.8/generated/braindecode.augmentation.AugmentedDataLoader.html
new file mode 100644
index 00000000..b0dbf74a
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@@ -0,0 +1,671 @@
+
+<!DOCTYPE html>
+
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+<h1>braindecode.augmentation.AugmentedDataLoader<a class="headerlink" href="#braindecode-augmentation-augmenteddataloader" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L186-L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader" title="Link to this definition">#</a></dt>
+<dd><p>A base dataloader class customized to applying augmentation Transforms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><em>BaseDataset</em></a>) – The dataset containing the signals.</p></li>
+<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference internal" href="../source/braindecode.augmentation.html#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><em>Transform</em></a><em>, </em><em>optional</em>) – Transform or sequence of Transform to be applied to each batch.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Device on which to transform the data. Defaults to None.</p></li>
+<li><p><strong>**kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – keyword arguments to pass to standard DataLoader class.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-augmenteddataloader">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.AugmentedDataLoader</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-augmenteddataloader" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
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+<h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindecode-augmentation-bandstopfilter" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">BandstopFilter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L516-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at a randomly selected
+frequency position between 0 and <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r4ef33a8aa142-1" id="id1">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r4ef33a8aa142-2" id="id2">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</p></li>
+<li><p><strong>max_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Maximal admissible frequency. The low cut frequency will be sampled so
+that the corresponding high cut frequency + transition (=1Hz) are below
+<code class="docutils literal notranslate"><span class="pre">max_freq</span></code>. If omitted or <cite>None</cite>, will default to the Nyquist
+frequency (<code class="docutils literal notranslate"><span class="pre">sfreq</span> <span class="pre">/</span> <span class="pre">2</span></code>).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4ef33a8aa142-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r4ef33a8aa142-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L586-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be filtered.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>bandwidth<span class="classifier">float</span></dt><dd><p>Bandwidth of the filter, i.e. distance between the low and high
+cut frequencies.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>freqs_to_notch<span class="classifier">array-like | None</span></dt><dd><p>Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center
+of the frequency band to filter out for each sample in the
+batch. Frequencies should be greater than
+<code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where
+<code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.operation" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
+position.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r4e2c3378db18-1" id="id3">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r4e2c3378db18-2" id="id4">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
+frequency band to filter out for each sample in the batch. Frequencies
+should be greater than <code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4e2c3378db18-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r4e2c3378db18-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
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+              
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+  
+  <section id="braindecode-augmentation-channelsdropout">
+<h1>braindecode.augmentation.ChannelsDropout<a class="headerlink" href="#braindecode-augmentation-channelsdropout" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L154-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#ra3d0be2d719b-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>proba_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of dropping each channel.
+Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument and to sample channels to erase. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra3d0be2d719b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L191-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_drop<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of dropping each
+channel.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r40820df5a429-1" id="id2">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r40820df5a429-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-channelsdropout">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-channelsdropout" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-channelsdropout">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout</span></code></a></li>
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+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L218-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r021aa678995d-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permuted channels. Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument, to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r021aa678995d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L256-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_shuffle<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of including the
+channel in the set of permuted channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r97a313e9d2d3-1" id="id2">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permutted channels.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r97a313e9d2d3-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
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+<h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braindecode-augmentation-channelssymmetry" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsSymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L351-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels inverting left and right-side sensors.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r5999d4af6ac6-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Ordered list of strings containing the names (in 10-20
+nomenclature) of the EEG channels that will be transformed. The
+first name should correspond the data in the first row of X, the
+second name in the second row and so on.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r5999d4af6ac6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L410-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>permutation<span class="classifier">float</span></dt><dd><p>List of integers defining the new channels order.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.operation" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels according to fixed permutation matrix.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#rae865eae5a1e-1" id="id2">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rae865eae5a1e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
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+  <section id="braindecode-augmentation-compose">
+<h1>braindecode.augmentation.Compose<a class="headerlink" href="#braindecode-augmentation-compose" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Compose">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Compose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L147-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose" title="Link to this definition">#</a></dt>
+<dd><p>Transform composition.</p>
+<p>Callable class allowing to cast a sequence of Transform objects into a
+single one.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Sequence of Transforms to be composed.</p>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Compose.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L163-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-compose">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Compose</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-compose" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
+  
+</div>
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+  <section id="braindecode-augmentation-ftsurrogate">
+<h1>braindecode.augmentation.FTSurrogate<a class="headerlink" href="#braindecode-augmentation-ftsurrogate" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FTSurrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L78-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#ra7c6c14d9bd9-1" id="id1">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+<code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">phase_noise_magnitude</span> <span class="pre">*</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">pi]</span></code>. Defaults to 1.</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI. Default False.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra7c6c14d9bd9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L127-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>phase_noise_magnitude<span class="classifier">float</span></dt><dd><p>The magnitude of the transformation.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.operation" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r0a58a8f194ca-1" id="id2">[1]</a>.</p>
+<p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
+and modified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+[0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0a58a8f194ca-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-ftsurrogate">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-ftsurrogate" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
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+  <section id="braindecode-augmentation-frequencyshift">
+<h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindecode-augmentation-frequencyshift" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FrequencyShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_delta_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L631-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift" title="Link to this definition">#</a></dt>
+<dd><p>Add a random shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>max_delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Maximum shift in Hz that can be sampled (in absolute value).
+Defaults to 2 (shift sampled between -2 and 2 Hz).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L668-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>delta_freq<span class="classifier">float</span></dt><dd><p>The amplitude of the frequency shift (in Hz).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be transformed.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.operation" title="Link to this definition">#</a></dt>
+<dd><p>Adds a shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-frequencyshift">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FrequencyShift</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-frequencyshift" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+  <section id="braindecode-augmentation-gaussiannoise">
+<h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindecode-augmentation-gaussiannoise" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">GaussianNoise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L283-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r8004aa1aead9-1" id="id1">[1]</a>, <a class="reference internal" href="../source/braindecode.augmentation.html#r8004aa1aead9-2" id="id2">[2]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r8004aa1aead9-3" id="id3">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation to use for the additive noise. Defaults to 0.1.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8004aa1aead9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r8004aa1aead9-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r8004aa1aead9-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L325-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>std<span class="classifier">float</span></dt><dd><p>Standard deviation to use for the additive noise.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white Gaussian noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r0afcdefddcf0-1" id="id4">[1]</a>, <a class="reference internal" href="../source/braindecode.augmentation.html#r0afcdefddcf0-2" id="id5">[2]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r0afcdefddcf0-3" id="id6">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0afcdefddcf0-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r0afcdefddcf0-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r0afcdefddcf0-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
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+<h1>braindecode.augmentation.IdentityTransform<a class="headerlink" href="#braindecode-augmentation-identitytransform" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">IdentityTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L139-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform" title="Link to this definition">#</a></dt>
+<dd><p>Identity transform.</p>
+<p>Transform that does not change the input.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform.operation" title="Link to this definition">#</a></dt>
+<dd><p>Identity operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
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+  <section id="braindecode-augmentation-mixup">
+<h1>braindecode.augmentation.Mixup<a class="headerlink" href="#braindecode-augmentation-mixup" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_per_sample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1030-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup" title="Link to this definition">#</a></dt>
+<dd><p>Implements Iterator for Mixup for EEG data. See <a class="reference internal" href="../source/braindecode.augmentation.html#r8b127a6b4d44-1" id="id1">[1]</a>.
+Implementation based on <a class="reference internal" href="../source/braindecode.augmentation.html#r8b127a6b4d44-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Mixup hyperparameter.</p></li>
+<li><p><strong>beta_per_sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – By default, one mixing coefficient per batch is drawn from a beta
+distribution. If True, one mixing coefficient per sample is drawn.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8b127a6b4d44-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r8b127a6b4d44-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1068-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>params</strong> – Contains the values sampled uniformly between 0 and 1 setting the
+linear interpolation between examples (lam) and the shuffled
+indices of examples that are mixed into original examples
+(idx_perm).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.operation" title="Link to this definition">#</a></dt>
+<dd><p>Mixes two channels of EEG data.</p>
+<p>See <a class="reference internal" href="../source/braindecode.augmentation.html#r6a9a88664e19-1" id="id3">[1]</a> for details.
+Implementation based on <a class="reference internal" href="../source/braindecode.augmentation.html#r6a9a88664e19-2" id="id4">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
+3 containing the labels of the two mixed channels and the mixing
+coefficient.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6a9a88664e19-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r6a9a88664e19-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
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+  <section id="braindecode-augmentation-sensorsrotation">
+<h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#braindecode-augmentation-sensorsrotation" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'z'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L733-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#r6bd6859a1fa8-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<cite>mne</cite> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em><em>, </em><em>optional</em>) – Axis around which to rotate. Defaults to ‘z’.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6bd6859a1fa8-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L809-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains four elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sensors_positions_matrix<span class="classifier">numpy.ndarray</span></dt><dd><p>Matrix giving the positions of each sensor in a 3D cartesian
+coordinate system. Should have shape (3, n_channels), where
+n_channels is the number of channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>axis<span class="classifier">’x’ | ‘y’ | ‘z’</span></dt><dd><p>Axis around which to rotate.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>angles<span class="classifier">array-like</span></dt><dd><p>Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the
+rotation angles (in degrees) for each element of the input
+batch, sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degrees``and</span>
+<span class="pre">``max_degrees</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>spherical_splines<span class="classifier">bool</span></dt><dd><p>Whether to use spherical splines for the interpolation or not.
+When <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic
+kernel) will be used (as in the original paper).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.operation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with the desired angle.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#rd4626e24d252-1" id="id2">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
+<li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
+angles (in degrees) for each element of the input batch.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
+used (as in the original paper).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd4626e24d252-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
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+<h1>braindecode.augmentation.SensorsXRotation<a class="headerlink" href="#braindecode-augmentation-sensorsxrotation" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsXRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsXRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L974-L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsXRotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the X axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#r44460ea0e68a-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r44460ea0e68a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
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+  <section id="braindecode-augmentation-sensorsyrotation">
+<h1>braindecode.augmentation.SensorsYRotation<a class="headerlink" href="#braindecode-augmentation-sensorsyrotation" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsYRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsYRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L918-L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsYRotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the Y axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#r05cec9135bb3-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r05cec9135bb3-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
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+<h1>braindecode.augmentation.SensorsZRotation<a class="headerlink" href="#braindecode-augmentation-sensorszrotation" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsZRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsZRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L862-L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsZRotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the Z axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#r8d1fae5fc86d-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8d1fae5fc86d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
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+<h1>braindecode.augmentation.SignFlip<a class="headerlink" href="#braindecode-augmentation-signflip" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SignFlip">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SignFlip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L53-L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SignFlip.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-signflip">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SignFlip</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-signflip" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SignFlip.operation"><code class="docutils literal notranslate"><span class="pre">SignFlip.operation()</span></code></a></li>
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+  <section id="braindecode-augmentation-smoothtimemask">
+<h1>braindecode.augmentation.SmoothTimeMask<a class="headerlink" href="#braindecode-augmentation-smoothtimemask" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SmoothTimeMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L431-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a randomly chosen contiguous part of all channels by
+zeros.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#rdc48fbfa356b-1" id="id1">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#rdc48fbfa356b-2" id="id2">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Number of consecutive samples to zero out. Will be ignored if
+magnitude is not set to None. Defaults to 100.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rdc48fbfa356b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rdc48fbfa356b-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L476-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains two elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>mask_start_per_sample<span class="classifier">torch.tensor</span></dt><dd><p>Tensor of integers containing the position (in last dimension)
+where to start masking the signal. Should have the same size as
+the first dimension of X (i.e. one start position per example
+in the batch).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>mask_len_samples<span class="classifier">int</span></dt><dd><p>Number of consecutive samples to zero out.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.operation" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
+<p>Originally proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#rbbea5cf64590-1" id="id3">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#rbbea5cf64590-2" id="id4">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
+start masking the signal. Should have the same size as the first
+dimension of X (i.e. one start position per example in the batch).</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rbbea5cf64590-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rbbea5cf64590-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-smoothtimemask">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SmoothTimeMask</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-smoothtimemask" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-smoothtimemask">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SmoothTimeMask</span></code></a></li>
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+<h1>braindecode.augmentation.TimeReverse<a class="headerlink" href="#braindecode-augmentation-timereverse" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">TimeReverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L28-L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
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+<h1>braindecode.augmentation.Transform<a class="headerlink" href="#braindecode-augmentation-transform" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L36-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform" title="Link to this definition">#</a></dt>
+<dd><p>Basic transform class used for implementing data augmentation
+operations.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operation</strong> (<em>callable</em>) – A function taking arrays X, y (inputs and targets resp.) and
+other required arguments, and returning the transformed X and y.</p></li>
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 defining the uniform probability of applying the
+operation. Set to 1.0 by default (e.g always apply the operation).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L71-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L68-L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-augmentation-transform">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Transform</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-transform" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
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+<h1>braindecode.augmentation.functional.bandstop_filter<a class="headerlink" href="#braindecode-augmentation-functional-bandstop-filter" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.bandstop_filter">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">bandstop_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.bandstop_filter" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
+position.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r8d0f6c52c5ba-1" id="id1">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r8d0f6c52c5ba-2" id="id2">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
+frequency band to filter out for each sample in the batch. Frequencies
+should be greater than <code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8d0f6c52c5ba-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r8d0f6c52c5ba-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
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+<h1>braindecode.augmentation.functional.channels_dropout<a class="headerlink" href="#braindecode-augmentation-functional-channels-dropout" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_dropout">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_dropout" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r993501bc6f56-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r993501bc6f56-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_dropout"><code class="docutils literal notranslate"><span class="pre">channels_dropout()</span></code></a></li>
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+  <section id="braindecode-augmentation-functional-channels-permute">
+<h1>braindecode.augmentation.functional.channels_permute<a class="headerlink" href="#braindecode-augmentation-functional-channels-permute" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_permute">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_permute" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels according to fixed permutation matrix.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#rcdffecb1125b-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcdffecb1125b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_permute"><code class="docutils literal notranslate"><span class="pre">channels_permute()</span></code></a></li>
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+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_shuffle">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_shuffle" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#rc9211ddb2257-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permutted channels.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc9211ddb2257-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
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+<h1>braindecode.augmentation.functional.frequency_shift<a class="headerlink" href="#braindecode-augmentation-functional-frequency-shift" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.frequency_shift">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">frequency_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.frequency_shift" title="Link to this definition">#</a></dt>
+<dd><p>Adds a shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.frequency_shift"><code class="docutils literal notranslate"><span class="pre">frequency_shift()</span></code></a></li>
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+  <section id="braindecode-augmentation-functional-ft-surrogate">
+<h1>braindecode.augmentation.functional.ft_surrogate<a class="headerlink" href="#braindecode-augmentation-functional-ft-surrogate" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.ft_surrogate">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">ft_surrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.ft_surrogate" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#r52a4658fffa7-1" id="id1">[1]</a>.</p>
+<p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
+and modified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+[0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r52a4658fffa7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
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+  <section id="braindecode-augmentation-functional-gaussian-noise">
+<h1>braindecode.augmentation.functional.gaussian_noise<a class="headerlink" href="#braindecode-augmentation-functional-gaussian-noise" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.gaussian_noise">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.gaussian_noise" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white Gaussian noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="../source/braindecode.augmentation.html#r6e3153c55efd-1" id="id1">[1]</a>, <a class="reference internal" href="../source/braindecode.augmentation.html#r6e3153c55efd-2" id="id2">[2]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#r6e3153c55efd-3" id="id3">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6e3153c55efd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r6e3153c55efd-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r6e3153c55efd-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.gaussian_noise"><code class="docutils literal notranslate"><span class="pre">gaussian_noise()</span></code></a></li>
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+<h1>braindecode.augmentation.functional.identity<a class="headerlink" href="#braindecode-augmentation-functional-identity" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.identity">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.identity" title="Link to this definition">#</a></dt>
+<dd><p>Identity operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
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+<h1>braindecode.augmentation.functional.mixup<a class="headerlink" href="#braindecode-augmentation-functional-mixup" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.mixup">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.mixup" title="Link to this definition">#</a></dt>
+<dd><p>Mixes two channels of EEG data.</p>
+<p>See <a class="reference internal" href="../source/braindecode.augmentation.html#r3a833d0ab9c7-1" id="id1">[1]</a> for details.
+Implementation based on <a class="reference internal" href="../source/braindecode.augmentation.html#r3a833d0ab9c7-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
+3 containing the labels of the two mixed channels and the mixing
+coefficient.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r3a833d0ab9c7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r3a833d0ab9c7-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
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+  <section id="braindecode-augmentation-functional-sensors-rotation">
+<h1>braindecode.augmentation.functional.sensors_rotation<a class="headerlink" href="#braindecode-augmentation-functional-sensors-rotation" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.sensors_rotation">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sensors_rotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sensors_rotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with the desired angle.</p>
+<p>Suggested in <a class="reference internal" href="../source/braindecode.augmentation.html#r228e8514004c-1" id="id1">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
+<li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
+angles (in degrees) for each element of the input batch.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
+used (as in the original paper).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r228e8514004c-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
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+<h1>braindecode.augmentation.functional.sign_flip<a class="headerlink" href="#braindecode-augmentation-functional-sign-flip" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.sign_flip">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sign_flip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sign_flip" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.sign_flip"><code class="docutils literal notranslate"><span class="pre">sign_flip()</span></code></a></li>
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+  <section id="braindecode-augmentation-functional-smooth-time-mask">
+<h1>braindecode.augmentation.functional.smooth_time_mask<a class="headerlink" href="#braindecode-augmentation-functional-smooth-time-mask" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.smooth_time_mask">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_time_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.smooth_time_mask" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
+<p>Originally proposed in <a class="reference internal" href="../source/braindecode.augmentation.html#rbf2d67638580-1" id="id1">[1]</a> and <a class="reference internal" href="../source/braindecode.augmentation.html#rbf2d67638580-2" id="id2">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
+start masking the signal. Should have the same size as the first
+dimension of X (i.e. one start position per example in the batch).</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rbf2d67638580-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rbf2d67638580-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.smooth_time_mask"><code class="docutils literal notranslate"><span class="pre">smooth_time_mask()</span></code></a></li>
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+<h1>braindecode.augmentation.functional.time_reverse<a class="headerlink" href="#braindecode-augmentation-functional-time-reverse" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.time_reverse">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">time_reverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.time_reverse" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.time_reverse"><code class="docutils literal notranslate"><span class="pre">time_reverse()</span></code></a></li>
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+  <section id="braindecode-classifier-eegclassifier">
+<h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode-classifier-eegclassifier" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.classifier.</span></span><span class="sig-name descname"><span class="pre">EEGClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion=&lt;class</span> <span class="pre">'torch.nn.modules.loss.CrossEntropyLoss'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L22-L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier" title="Link to this definition">#</a></dt>
+<dd><p>Classifier that does not assume softmax activation.
+Calls loss function directly without applying log or anything.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.NLLLoss</em><em>)</em>) – Negative log likelihood loss. Note that the module should return
+probabilities, the log is applied during <code class="docutils literal notranslate"><span class="pre">get_loss</span></code>.</p></li>
+<li><p><strong>classes</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default=None</em><em>)</em>) – If None, the <code class="docutils literal notranslate"><span class="pre">classes_</span></code> attribute will be inferred from the
+<code class="docutils literal notranslate"><span class="pre">y</span></code> data passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If a non-empty list is passed,
+that list will be returned as <code class="docutils literal notranslate"><span class="pre">classes_</span></code>. If the initial
+skorch behavior should be restored, i.e. raising an
+<code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>, pass an empty list.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.classes_">
+<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.classes_" title="Link to this definition">#</a></dt>
+<dd><p>A list of class labels known to the classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_classes, )</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L90-L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_loss">
+<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L142-L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_loss" title="Link to this definition">#</a></dt>
+<dd><p>Return the loss for this batch by calling NeuralNet get_loss.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_pred</strong> (<em>torch tensor</em>) – Predicted target values</p></li>
+<li><p><strong>y_true</strong> (<em>torch tensor</em>) – True target values.</p></li>
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L174-L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict" title="Link to this definition">#</a></dt>
+<dd><p>Return class labels for samples in X.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_pred</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L97-L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L200-L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
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+  <section id="braindecode-datasets-bcicompetitionivdataset4">
+<h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#braindecode-datasets-bcicompetitionivdataset4" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BCICompetitionIVDataset4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L23-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4" title="Link to this definition">#</a></dt>
+<dd><p>BCI competition IV dataset 4.</p>
+<p>Contains ECoG recordings for three patients moving fingers during the experiment.
+Targets correspond to the time courses of the flexion of each of five fingers.
+See <a class="reference external" href="http://www.bbci.de/competition/iv/desc_4.pdf">http://www.bbci.de/competition/iv/desc_4.pdf</a> and
+<a class="reference external" href="http://www.bbci.de/competition/iv/">http://www.bbci.de/competition/iv/</a> for the dataset and competition description.
+ECoG library containing the dataset: <a class="reference external" href="https://searchworks.stanford.edu/view/zk881ps0522">https://searchworks.stanford.edu/view/zk881ps0522</a></p>
+<p class="rubric">Notes</p>
+<p>When using this dataset please cite <a class="reference internal" href="../source/braindecode.datasets.html#rb33c1a7bbf7d-1" id="id1">[1]</a> .</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects. Should be in range 1-3.</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb33c1a7bbf7d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Miller, Kai J. “A library of human electrocorticographic data and analyses.”</p>
+</div>
+</div>
+<p>Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.download">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L74-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.download" title="Link to this definition">#</a></dt>
+<dd><p>Download the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>location.</strong> (<em>path</em><em>  (</em><em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>) </em><em>– Location</em><em> of </em><em>where to look for the data storing</em>) – </p></li>
+<li><p><strong>None</strong> (<em>or None</em><em>) </em><em>– If not</em>) – </p></li>
+<li><p><strong>parameter</strong> (<em>the environment variable</em><em> or </em><em>config</em>) – </p></li>
+<li><p><strong>exist</strong> (<em>MNE_DATASETS_</em><em>(</em><em>dataset</em><em>)</em><em>_PATH is used. If it doesn’t</em>) – </p></li>
+<li><p><strong>“~/mne_data”</strong> (<em>the</em>) – </p></li>
+<li><p><strong>path</strong> (<em>directory is used. If the dataset is not found under the given</em>) – </p></li>
+<li><p><strong>data</strong> (<em>the</em>) – </p></li>
+<li><p><strong>folder.</strong> (<em>will be automatically downloaded to the specified</em>) – </p></li>
+<li><p><strong>exists.</strong> (<em>force_update</em><em> (</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>) </em><em>– Force update</em><em> of </em><em>the dataset even if a local copy</em>) – </p></li>
+<li><p><strong>(</strong><strong>bool</strong> (<em>verbose</em>) – </p></li>
+<li><p><strong>str</strong> – </p></li>
+<li><p><strong>int</strong> – </p></li>
+<li><p><strong>None</strong> – </p></li>
+<li><p><strong>level</strong> (<em>override default verbose</em>) – </p></li>
+<li><p><strong>mne.verbose</strong><strong>(</strong><strong>)</strong><strong>)</strong> (<em>(</em><em>see</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-bcicompetitionivdataset4">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BCICompetitionIVDataset4</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-bcicompetitionivdataset4" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
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+<h1>braindecode.datasets.BNCI2014001<a class="headerlink" href="#braindecode-datasets-bnci2014001" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L114-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BNCI2014001" title="Link to this definition">#</a></dt>
+<dd><p>BNCI 2014-001 Motor Imagery dataset.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <span id="id1">[R382d436f3223-1]</span>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.bnci.BNCI2014001</p>
+</dd></dl>
+
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+  <section id="braindecode-datasets-baseconcatdataset">
+<h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindecode-datasets-baseconcatdataset" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L386-L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset" title="Link to this definition">#</a></dt>
+<dd><p>A base class for concatenated datasets. Holds either mne.Raw or
+mne.Epoch in self.datasets and has a pandas DataFrame with additional
+description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_ds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – list of BaseDataset, BaseConcatDataset or WindowsDataset</p></li>
+<li><p><strong>target_transform</strong> (<em>callable</em><em> | </em><em>None</em>) – Optional function to call on targets before returning them.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.get_metadata">
+<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L490-L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.get_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate the metadata and description of the wrapped Epochs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>metadata</strong> – DataFrame containing as many rows as there are windows in the
+BaseConcatDataset, with the metadata and description information
+for each window.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L628-L709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.save" title="Link to this definition">#</a></dt>
+<dd><p>Save datasets to files by creating one subdirectory for each dataset:
+path/</p>
+<blockquote>
+<div><dl class="simple">
+<dt>0/</dt><dd><p>0-raw.fif | 0-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+<dt>1/</dt><dd><p>1-raw.fif | 1-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – <dl class="simple">
+<dt>Directory in which subdirectories are created to store</dt><dd><p>-raw.fif | -epo.fif and .json files to.</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to delete old subdirectories that will be saved to in this
+call.</p></li>
+<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – If provided, the integer is added to the id of the dataset in the
+concat. This is useful in the setting of very large datasets, where
+one dataset has to be processed and saved at a time to account for
+its original position.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L611-L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.DataFrame</em>) – Description in the form key: value where the length of the value
+has to match the number of datasets.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.split">
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">property</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L436-L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.split" title="Link to this definition">#</a></dt>
+<dd><p>Split the dataset based on information listed in its description
+DataFrame or based on indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a string, splitting is performed based on the
+description DataFrame column with this name.
+If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a (list of) list of integers, the position in the first
+list corresponds to the split id and the integers to the
+datapoints of that split.
+If a dict then each key will be used in the returned
+splits dict and each value should be a list of int.</p></li>
+<li><p><strong>property</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Some property which is listed in info DataFrame.</p></li>
+<li><p><strong>split_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – List of indices to be combined in a subset.
+It can be a list of int or a list of list of int.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>splits</strong> – A dictionary with the name of the split (a string) as key and the
+dataset as value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-baseconcatdataset">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-baseconcatdataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-baseconcatdataset">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a></li>
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+  <section id="braindecode-datasets-basedataset">
+<h1>braindecode.datasets.BaseDataset<a class="headerlink" href="#braindecode-datasets-basedataset" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L36-L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset" title="Link to this definition">#</a></dt>
+<dd><p>Returns samples from an mne.io.Raw object along with a target.</p>
+<p>Dataset which serves samples from an mne.io.Raw object along with a target.
+The target is unique for the dataset, and is obtained through the
+<cite>description</cite> attribute.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – Continuous data.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional description about the continuous signal / subject.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> | </em><em>None</em>) – Name(s) of the index in <cite>description</cite> that should be used to provide the
+target (e.g., to be used in a prediction task later on).</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to the example before it is returned.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L92-L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-basedataset">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-basedataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
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+<h1>braindecode.datasets.HGD<a class="headerlink" href="#braindecode-datasets-hgd" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">HGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L133-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD" title="Link to this definition">#</a></dt>
+<dd><p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <span id="id1">[R5e478952091a-1]</span></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.schirrmeister2017.Schirrmeister2017</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="braindecode-datasets-moabbdataset">
+<h1>braindecode.datasets.MOABBDataset<a class="headerlink" href="#braindecode-datasets-moabbdataset" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.MOABBDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">MOABBDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L93-L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.MOABBDataset" title="Link to this definition">#</a></dt>
+<dd><p>A class for moabb datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – name of dataset included in moabb to be fetched</p></li>
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p></li>
+<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
+to pass to the moabb dataset when instantiating it.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-moabbdataset">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.MOABBDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-moabbdataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="braindecode-datasets-sleepphysionet">
+<h1>braindecode.datasets.SleepPhysionet<a class="headerlink" href="#braindecode-datasets-sleepphysionet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.SleepPhysionet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">SleepPhysionet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_eeg_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_wake_mins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/sleep_physionet.py#L16-L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.SleepPhysionet" title="Link to this definition">#</a></dt>
+<dd><p>Sleep Physionet dataset.</p>
+<p>Sleep dataset from <a class="reference external" href="https://physionet.org/content/sleep-edfx/1.0.0/">https://physionet.org/content/sleep-edfx/1.0.0/</a>.
+Contains overnight recordings from 78 healthy subjects.</p>
+<p>See [MNE example](<a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html</a>).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><em>None</em>) – Recordings to load per subject (each subject except 13 has two
+recordings). Can be [1], [2] or [1, 2] (same as None).</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>load_eeg_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, only load the EEG channels and discard the others (EOG, EMG,
+temperature, respiration) to avoid resampling the other signals.</p></li>
+<li><p><strong>crop_wake_mins</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Number of minutes of wake time to keep before the first sleep event
+and after the last sleep event. Used to reduce the imbalance in this
+dataset. Default of 30 mins.</p></li>
+<li><p><strong>crop</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – If not None crop the raw files (e.g. to use only the first 3h).
+Example: <code class="docutils literal notranslate"><span class="pre">crop=(0,</span> <span class="pre">3600*3)</span></code> to keep only the first 3h.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-sleepphysionet">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-sleepphysionet" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a></li>
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+  <section id="braindecode-datasets-tuh">
+<h1>braindecode.datasets.TUH<a class="headerlink" href="#braindecode-datasets-tuh" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.TUH">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L26-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUH" title="Link to this definition">#</a></dt>
+<dd><p>Temple University Hospital (TUH) EEG Corpus
+(www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-tuh">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUH</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuh" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
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+<dt class="sig sig-object py" id="braindecode.datasets.TUHAbnormal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUHAbnormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pathological'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L274-L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUHAbnormal" title="Link to this definition">#</a></dt>
+<dd><p>Temple University Hospital (TUH) Abnormal EEG Corpus.
+see www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tuab</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘pathological’, ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-tuhabnormal">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUHAbnormal</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuhabnormal" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-tuhabnormal">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUHAbnormal</span></code></a></li>
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+  <section id="braindecode-datasets-windowsdataset">
+<h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-datasets-windowsdataset" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">WindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L271-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset" title="Link to this definition">#</a></dt>
+<dd><p>Returns windows from an mne.Epochs object along with a target.</p>
+<p>Dataset which serves windows from an mne.Epochs object along with their
+target and additional information. The <cite>metadata</cite> attribute of the Epochs
+object must contain a column called <cite>target</cite>, which will be used to return
+the target that corresponds to a window. Additional columns
+<cite>i_window_in_trial</cite>, <cite>i_start_in_trial</cite>, <cite>i_stop_in_trial</cite> are also
+required to serve information about the windowing (e.g., useful for cropped
+training).
+See <cite>braindecode.datautil.windowers</cite> to directly create a <cite>WindowsDataset</cite>
+from a <cite>BaseDataset</cite> object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>windows</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><em>mne.Epochs</em></a>) – Windows obtained through the application of a windower to a BaseDataset
+(see <cite>braindecode.datautil.windowers</cite>).</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to a window before it is returned.</p></li>
+<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs <cite>misc</cite>
+channels (time series targets). It can be <cite>metadata</cite> (default) or <cite>channels</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L365-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<h1>braindecode.datasets.create_from_X_y<a class="headerlink" href="#braindecode-datasets-create-from-x-y" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.create_from_X_y">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/xy.py#L16-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_X_y" title="Link to this definition">#</a></dt>
+<dd><p>Create a BaseConcatDataset of WindowsDatasets from X and y to be used for
+decoding with skorch and braindecode, where X is a list of pre-cut trials
+and y are corresponding targets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em>) – list of pre-cut trials as n_trials x n_channels x n_times</p></li>
+<li><p><strong>y</strong> (<em>array-like</em>) – targets corresponding to the trials</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows/windows do not equally divide the continuous signal</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of signals.</p></li>
+<li><p><strong>ch_names</strong> (<em>array-like</em>) – Names of the channels.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-create-from-x-y">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_X_y</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-x-y" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
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+<h1>braindecode.datasets.create_from_mne_epochs<a class="headerlink" href="#braindecode-datasets-create-from-mne-epochs" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_epochs">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_epochs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_epochs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L87-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_epochs" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.Epochs</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_epochs</strong> (<em>array-like</em>) – list of mne.Epochs</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-create-from-mne-epochs">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_epochs</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-epochs" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-create-from-mne-epochs">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_epochs</span></code></a></li>
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+<dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_raw">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raws</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">descriptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L13-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_raw" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.RawArrays</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raws</strong> (<em>array-like</em>) – list of mne.RawArrays</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – start offset from original trial onsets in samples</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stop offset from original trial stop in samples</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+<li><p><strong>descriptions</strong> (<em>array-like</em>) – list of dicts or pandas.Series with additional information about the raws</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – mapping from event description to target value</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials withinconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datasets-create-from-mne-raw">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_raw</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-raw" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
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+  <section id="braindecode-datautil-load-concat-dataset">
+<h1>braindecode.datautil.load_concat_dataset<a class="headerlink" href="#braindecode-datautil-load-concat-dataset" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datautil.load_concat_dataset">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.</span></span><span class="sig-name descname"><span class="pre">load_concat_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids_to_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/serialization.py#L157-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.load_concat_dataset" title="Link to this definition">#</a></dt>
+<dd><p>Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from
+files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><em>pathlib.Path</em></a>) – Path to the directory of the .fif / -epo.fif and .json files.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to preload the data.</p></li>
+<li><p><strong>ids_to_load</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Ids of specific files to load.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><em>None</em>) – Load specific description column as target. If not given, take saved
+target name.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>concat_dataset</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a> of BaseDatasets or WindowsDatasets</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-datautil-load-concat-dataset">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datautil.load_concat_dataset</span></code><a class="headerlink" href="#examples-using-braindecode-datautil-load-concat-dataset" title="Link to this heading">#</a></h2>
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-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">scale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#scale"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.scale" title="Permalink to this definition">#</a></dt>
-<dd><p>DEPRECATED: will be removed in 0.8.0. Use numpy.multiply inside a lambda function instead.</p>
-<p>Scale continuous or windowed data in-place</p>
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-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels x n_times</em><em>) or </em><em>(</em><em>n_windows x n_channels x</em>) – </p></li>
-<li><p><strong>n_times</strong><strong>)</strong> – continuous or windowed signal</p></li>
-<li><p><strong>factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – multiplication factor</p></li>
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-<dd class="field-even"><p><ul class="simple">
-<li><p><strong>scaled</strong> (<em>np.ndarray (n_channels x n_times) or (n_windows x n_channels x</em>)</p></li>
-<li><p><em>n_times)</em> – normalized continuous or windowed data</p></li>
-<li><p><em>..note</em> – If this function is supposed to preprocess continuous data, it should be
-given to raw.apply_function().</p></li>
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-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-scale" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
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+  <section id="braindecode-models-atcnet">
+<h1>braindecode.models.ATCNet<a class="headerlink" href="#braindecode-models-atcnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.ATCNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">ATCNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_depth_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_head_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_num_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm_const</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L14-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ATCNet" title="Link to this definition">#</a></dt>
+<dd><p>ATCNet model from <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id1">[1]</a></p>
+<p>Pytorch implementation based on official tensorflow code <a class="reference internal" href="#r2ecdb73d6ab9-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Time length of inputs, in seconds. Defaults to 4.5 s, as in BCI-IV 2a
+dataset.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Sampling frequency of the inputs, in Hz. Default to 250 Hz, as in
+BCI-IV 2a dataset.</p></li>
+<li><p><strong>conv_block_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number temporal filters in the first convolutional layer of the
+convolutional block, denoted F1 in figure 2 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id3">[1]</a>. Defaults
+to 16 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id4">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the first convolutional layer of the
+convolutional block, denoted Kc in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id5">[1]</a>. Defaults
+to 64 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id6">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the last convolutional layer of the
+convolutional block. Defaults to 16 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id7">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block.
+Defaults to 8 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id8">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block,
+denoted P2 in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id9">[1]</a>. Defaults to 7 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id10">[1]</a>.</p></li>
+<li><p><strong>conv_block_depth_mult</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier of depthwise convolution in the convolutional block,
+denoted D in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id11">[1]</a>. Defaults to 2 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id12">[1]</a>.</p></li>
+<li><p><strong>conv_block_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the convolution block, denoted pc in
+table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id13">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id14">[1]</a>.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sliding windows, denoted n in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id15">[1]</a>. Defaults to 5 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id16">[1]</a>.</p></li>
+<li><p><strong>att_head_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Embedding dimension used in each self-attention head, denoted dh in
+table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id17">[1]</a>. Defaults to 8 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id18">[1]</a>.</p></li>
+<li><p><strong>att_num_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads, denoted H in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id19">[1]</a>.
+Defaults to 2 as in [<a href="#id32"><span class="problematic" id="id33">1_</span></a>.</p></li>
+<li><p><strong>att_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the attention block, denoted pa in table 1
+of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id20">[1]</a>. Defaults to 0.5 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id21">[1]</a>.</p></li>
+<li><p><strong>tcn_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth of Temporal Convolutional Network block (i.e. number of TCN
+Residual blocks), denoted L in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id22">[1]</a>. Defaults to 2
+as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id23">[1]</a>.</p></li>
+<li><p><strong>tcn_kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Temporal kernel size used in TCN block, denoted Kt in table 1 of the
+paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id24">[1]</a>. Defaults to 4 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id25">[1]</a>.</p></li>
+<li><p><strong>tcn_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of filters used in TCN convolutional layers (Ft). Defaults to
+32 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id26">[1]</a>.</p></li>
+<li><p><strong>tcn_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the TCN block, denoted pt in table 1
+of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id27">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id28">[1]</a>.</p></li>
+<li><p><strong>tcn_activation</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – Nonlinear activation to use. Defaults to nn.ELU().</p></li>
+<li><p><strong>concat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <code class="docutils literal notranslate"><span class="pre">True</span></code>, concatenates each slidding window embedding before
+feeding it to a fully-connected layer, as done in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id29">[1]</a>. When <code class="docutils literal notranslate"><span class="pre">False</span></code>,
+maps each slidding window to <cite>n_outputs</cite> logits and average them.
+Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code> contrary to what is reported in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id30">[1]</a>, but
+matching what the official code does <a class="reference internal" href="#r2ecdb73d6ab9-2" id="id31">[2]</a>.</p></li>
+<li><p><strong>max_norm_const</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Maximum L2-norm constraint imposed on weights of the last
+fully-connected layer. Defaults to 0.25.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2ecdb73d6ab9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id3">2</a>,<a role="doc-backlink" href="#id4">3</a>,<a role="doc-backlink" href="#id5">4</a>,<a role="doc-backlink" href="#id6">5</a>,<a role="doc-backlink" href="#id7">6</a>,<a role="doc-backlink" href="#id8">7</a>,<a role="doc-backlink" href="#id9">8</a>,<a role="doc-backlink" href="#id10">9</a>,<a role="doc-backlink" href="#id11">10</a>,<a role="doc-backlink" href="#id12">11</a>,<a role="doc-backlink" href="#id13">12</a>,<a role="doc-backlink" href="#id14">13</a>,<a role="doc-backlink" href="#id15">14</a>,<a role="doc-backlink" href="#id16">15</a>,<a role="doc-backlink" href="#id17">16</a>,<a role="doc-backlink" href="#id18">17</a>,<a role="doc-backlink" href="#id19">18</a>,<a role="doc-backlink" href="#id20">19</a>,<a role="doc-backlink" href="#id21">20</a>,<a role="doc-backlink" href="#id22">21</a>,<a role="doc-backlink" href="#id23">22</a>,<a role="doc-backlink" href="#id24">23</a>,<a role="doc-backlink" href="#id25">24</a>,<a role="doc-backlink" href="#id26">25</a>,<a role="doc-backlink" href="#id27">26</a>,<a role="doc-backlink" href="#id28">27</a>,<a role="doc-backlink" href="#id29">28</a>,<a role="doc-backlink" href="#id30">29</a>)</span>
+<p>H. Altaheri, G. Muhammad and M. Alsulaiman, “Physics-informed
+attention temporal convolutional network for EEG-based motor imagery
+classification,” in IEEE Transactions on Industrial Informatics,
+2022, doi: 10.1109/TII.2022.3197419.</p>
+</div>
+<div class="citation" id="r2ecdb73d6ab9-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id31">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/Altaheri/EEG-ATCNet/blob/main/models.py">Altaheri/EEG-ATCNet</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.ATCNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L239-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ATCNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.ATCNet.forward"><code class="docutils literal notranslate"><span class="pre">ATCNet.forward()</span></code></a></li>
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+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.Deep4Net">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">Deep4Net</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_2=50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_2=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_3=100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_3=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_4=200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_4=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_before_pool=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deep4.py#L15-L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.Deep4Net" title="Link to this definition">#</a></dt>
+<dd><p>Deep ConvNet model from Schirrmeister et al 2017.</p>
+<p>Model described in <a class="reference internal" href="#rb8ef6c2733ce-schirrmeister2017" id="id1">[Schirrmeister2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, n_times must not be None. Default: “auto”.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 1.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>n_filters_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 2.</p></li>
+<li><p><strong>filter_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 2.</p></li>
+<li><p><strong>n_filters_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 3.</p></li>
+<li><p><strong>filter_length_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 3.</p></li>
+<li><p><strong>n_filters_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 4.</p></li>
+<li><p><strong>filter_length_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 4.</p></li>
+<li><p><strong>first_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in layer 1.</p></li>
+<li><p><strong>first_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in layer 1. “max” or “mean”.</p></li>
+<li><p><strong>first_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in layer 1.</p></li>
+<li><p><strong>later_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in later layers.</p></li>
+<li><p><strong>later_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in later layers. “max” or “mean”.</p></li>
+<li><p><strong>later_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in later layers.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+There would be no non-linearity between the split layers.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>stride_before_pool</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Stride before pooling.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb8ef6c2733ce-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+<section id="examples-using-braindecode-models-deep4net">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.Deep4Net</span></code><a class="headerlink" href="#examples-using-braindecode-models-deep4net" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div></div><div style='clear:both'></div></section>
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+  <section id="braindecode-models-deepsleepnet">
+<h1>braindecode.models.DeepSleepNet<a class="headerlink" href="#braindecode-models-deepsleepnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.DeepSleepNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">DeepSleepNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L167-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.DeepSleepNet" title="Link to this definition">#</a></dt>
+<dd><p>Sleep staging architecture from Supratak et al 2017.</p>
+<p>Convolutional neural network and bidirectional-Long Short-Term
+for single channels sleep staging described in <a class="reference internal" href="#r28b528aca953-supratak2017" id="id1">[Supratak2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r28b528aca953-supratak2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Supratak2017</a><span class="fn-bracket">]</span></span>
+<p>Supratak, A., Dong, H., Wu, C., &amp; Guo, Y. (2017).
+DeepSleepNet: A model for automatic sleep stage scoring based
+on raw single-channel EEG. IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 25(11), 1998-2008.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.DeepSleepNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L232-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.DeepSleepNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.DeepSleepNet.forward"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet.forward()</span></code></a></li>
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+<h1>braindecode.models.EEGConformer<a class="headerlink" href="#braindecode-models-eegconformer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGConformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_fc_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2440</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L14-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer" title="Link to this definition">#</a></dt>
+<dd><p>EEG Conformer.</p>
+<p>Convolutional Transformer for EEG decoding.</p>
+<p>The paper and original code with more details about the methodological
+choices are available at the <a class="reference internal" href="#rd6c0fefc356a-song2022" id="id1">[Song2022]</a> and <a class="reference internal" href="#rd6c0fefc356a-conformercode" id="id2">[ConformerCode]</a>.</p>
+<p>This neural network architecture receives a traditional braindecode input.
+The input shape should be three-dimensional matrix representing the EEG
+signals.</p>
+<blockquote>
+<div><p><cite>(batch_size, n_channels, n_timesteps)</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>The EEG Conformer architecture is composed of three modules:</dt><dd><ul class="simple">
+<li><p>PatchEmbedding</p></li>
+<li><p>TransformerEncoder</p></li>
+<li><p>ClassificationHead</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters, defines also embedding size.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the convolutional layer.</p></li>
+<li><p><strong>att_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of self-attention layers.</p></li>
+<li><p><strong>att_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads.</p></li>
+<li><p><strong>att_drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the self-attention layer.</p></li>
+<li><p><strong>final_fc_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The dimension of the fully connected layer.</p></li>
+<li><p><strong>return_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the forward method returns the features before the
+last classification layer. Defaults to False.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The authors recommend using data augmentation before using Conformer,
+e.g. segmentation and recombination,
+Please refer to the original paper and code for more details.</p>
+<p>The model was initially tuned on 4 seconds of 250 Hz data.
+Please adjust the scale of the temporal convolutional layer,
+and the pooling layer for better performance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.8.</span></p>
+</div>
+<p>We aggregate the parameters based on the parts of the models, or
+when the parameters were used first, e.g. n_filters_time.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd6c0fefc356a-song2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Song2022</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering,
+31, pp.710-719. <a class="reference external" href="https://ieeexplore.ieee.org/document/9991178">https://ieeexplore.ieee.org/document/9991178</a></p>
+</div>
+<div class="citation" id="rd6c0fefc356a-conformercode" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">ConformerCode</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+<a class="github reference external" href="https://github.com/eeyhsong/EEG-Conformer">eeyhsong/EEG-Conformer</a>.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L163-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer.get_fc_size">
+<span class="sig-name descname"><span class="pre">get_fc_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L171-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer.get_fc_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGConformer"><code class="docutils literal notranslate"><span class="pre">EEGConformer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGConformer.forward"><code class="docutils literal notranslate"><span class="pre">EEGConformer.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGConformer.get_fc_size"><code class="docutils literal notranslate"><span class="pre">EEGConformer.get_fc_size()</span></code></a></li>
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+  <section id="braindecode-models-eegitnet">
+<h1>braindecode.models.EEGITNet<a class="headerlink" href="#braindecode-models-eegitnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGITNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGITNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegitnet.py#L89-L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGITNet" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>EEG-ITNet: An Explainable Inception Temporal</dt><dd><p>Convolutional Network for motor imagery classification from
+Salami et. al 2022.</p>
+</dd>
+</dl>
+<p>See <a class="reference internal" href="#r7fe571f46200-salami2022" id="id1">[Salami2022]</a> for details.</p>
+<p>Code adapted from <a class="github reference external" href="https://github.com/abbassalami/eeg-itnet">abbassalami/eeg-itnet</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on author implementation.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r7fe571f46200-salami2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Salami2022</a><span class="fn-bracket">]</span></span>
+<ol class="upperalpha simple">
+<li><p>Salami, J. Andreu-Perez and H. Gillmeister, “EEG-ITNet: An Explainable</p></li>
+</ol>
+</div>
+</div>
+<p>Inception Temporal Convolutional Network for motor imagery classification,” in IEEE Access,
+doi: 10.1109/ACCESS.2022.3161489.</p>
+</dd></dl>
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+  <section id="braindecode-models-eeginception">
+<h1>braindecode.models.EEGInception<a class="headerlink" href="#braindecode-models-eeginception" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGInception">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception.py#L17-L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInception" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for ERP-based classification</p>
+<p>–&gt; DEPRECATED &lt;–
+THIS CLASS IS DEPRECATED AND WILL BE REMOVED IN THE RELEASE 0.9 OF
+BRAINDECODE. PLEASE USE braindecode.models.EEGInceptionERP INSTEAD IN THE
+FUTURE.</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id1">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#r15fdf33b022f-2" id="id2">[2]</a>.</p>
+<p>The model is strongly based on the original InceptionNet for an image. The main goal is
+to extract features in parallel with different scales. The authors extracted three scales
+proportional to the window sample size. The network had three parts:
+1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
+for classification.</p>
+<p>One advantage of the EEG-Inception block is that it allows a network
+to learn simultaneous components of low and high frequency associated with the signal.
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id3">[R15fdf33b022f-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id4">[Santamaria2020]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate inside all the network.</p></li>
+<li><p><strong>scales_samples_s</strong> – The description is missing.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 8 in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id5">[Santamaria2020]</a>.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function, default: ELU activation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier for the depthwise convolution.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Pooling sizes for the inception block.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r15fdf33b022f-2" id="id6">[2]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r15fdf33b022f-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
+EEG-inception: A novel deep convolutional neural network for assistive
+ERP-based brain-computer interfaces.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
+Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
+</div>
+<div class="citation" id="r15fdf33b022f-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id6">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
+Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
+</div>
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+  <section id="braindecode-models-eeginceptionerp">
+<h1>braindecode.models.EEGInceptionERP<a class="headerlink" href="#braindecode-models-eeginceptionerp" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionERP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionERP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_erp.py#L16-L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionERP" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for ERP-based classification</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id1">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#r37c4761d4e92-2" id="id2">[2]</a>.</p>
+<p>The model is strongly based on the original InceptionNet for an image. The main goal is
+to extract features in parallel with different scales. The authors extracted three scales
+proportional to the window sample size. The network had three parts:
+1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
+for classification.</p>
+<p>One advantage of the EEG-Inception block is that it allows a network
+to learn simultaneous components of low and high frequency associated with the signal.
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id3">[R37c4761d4e92-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id4">[Santamaria2020]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Size of the input, in number of samples. Set to 128 (1s) as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id5">[Santamaria2020]</a>.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency. Defaults to 128 as in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id6">[Santamaria2020]</a>.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Dropout rate inside all the network. Defaults to 0.5 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id7">[Santamaria2020]</a>.</p></li>
+<li><p><strong>scales_samples_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>)</em><em>, </em><em>optional</em>) – Windows for inception block. Temporal scale (s) of the convolutions on
+each Inception module. This parameter determines the kernel sizes of
+the filters. Defaults to 0.5, 0.25, 0.125 seconds, as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id8">[Santamaria2020]</a>.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Initial number of convolutional filters. Defaults to 8 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id9">[Santamaria2020]</a>.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – Activation function. Defaults to ELU activation as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id10">[Santamaria2020]</a>.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Momentum for BatchNorm2d. Defaults to 0.01.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Depth multiplier for the depthwise convolution. Defaults to 2 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id11">[Santamaria2020]</a>.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em><em>, </em><em>optional</em>) – Pooling sizes for the inception blocks. Defaults to 4, 2, 2 and 2, as
+in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id12">[Santamaria2020]</a>.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r37c4761d4e92-2" id="id13">[2]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r37c4761d4e92-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>,<a role="doc-backlink" href="#id10">8</a>,<a role="doc-backlink" href="#id11">9</a>,<a role="doc-backlink" href="#id12">10</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
+EEG-inception: A novel deep convolutional neural network for assistive
+ERP-based brain-computer interfaces.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
+Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
+</div>
+<div class="citation" id="r37c4761d4e92-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id13">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
+Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
+</div>
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+  <section id="braindecode-models-eeginceptionmi">
+<h1>braindecode.models.EEGInceptionMI<a class="headerlink" href="#braindecode-models-eeginceptionmi" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionMI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionMI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_convs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">48</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_unit_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ReLU()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L13-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionMI" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for Motor Imagery, as proposed in <a class="reference internal" href="#rc36ed781f4f5-1" id="id1">[1]</a></p>
+<p>The model is strongly based on the original InceptionNet for computer
+vision. The main goal is to extract features in parallel with different
+scales. The network has two blocks made of 3 inception modules with a skip
+connection.</p>
+<p>The model is fully described in <a class="reference internal" href="#rc36ed781f4f5-1" id="id2">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size of the input, in seconds. Set to 4.5 s as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id3">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency in Hz. Defaults to 250 Hz as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id4">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>n_convs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolution per inception wide branching. Defaults to 5 as
+in <a class="reference internal" href="#rc36ed781f4f5-1" id="id5">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolutional filters for all layers of this type. Set to 48
+as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id6">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>kernel_unit_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size in seconds of the basic 1D convolutional kernel used in inception
+modules. Each convolutional layer in such modules have kernels of
+increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7,
+0.9 here for <a href="#id7"><span class="problematic" id="id8">`</span></a>n_convs`=5). Defaults to 0.1 s.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function. Defaults to ReLU activation.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented bosed on the paper <a class="reference internal" href="#rc36ed781f4f5-1" id="id9">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc36ed781f4f5-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>,<a role="doc-backlink" href="#id6">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Zhang, C., Kim, Y. K., &amp; Eskandarian, A. (2021).
+EEG-inception: an accurate and robust end-to-end neural network
+for EEG-based motor imagery classification.
+Journal of Neural Engineering, 18(4), 046014.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionMI.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L185-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionMI.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGInceptionMI"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGInceptionMI.forward"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI.forward()</span></code></a></li>
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+  <section id="braindecode-models-eegmodulemixin">
+<h1>braindecode.models.EEGModuleMixin<a class="headerlink" href="#braindecode-models-eegmodulemixin" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><span class="pre">Dict</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L34-L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin" title="Link to this definition">#</a></dt>
+<dd><p>Mixin class for all EEG models in braindecode.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.get_output_shape">
+<span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L189-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns shape of neural network output for batch size equal 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Tuple[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.get_torchinfo_statistics">
+<span class="sig-name descname"><span class="pre">get_torchinfo_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('input_size',</span> <span class="pre">'output_size',</span> <span class="pre">'num_params',</span> <span class="pre">'kernel_size')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_settings</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('var_names',</span> <span class="pre">'depth')</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ModelStatistics</span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L277-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.get_torchinfo_statistics" title="Link to this definition">#</a></dt>
+<dd><p>Generate table describing the model using torchinfo.summary.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>col_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which columns to show in the output, see torchinfo for details, by default
+(“input_size”, “output_size”, “num_params”, “kernel_size”)</p></li>
+<li><p><strong>row_settings</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which features to show in a row, see torchinfo for details, by default
+(“var_names”, “depth”)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ModelStatistics generated by torchinfo.summary.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torchinfo.ModelStatistics</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.load_state_dict">
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L222-L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.load_state_dict" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.to_dense_prediction_model">
+<span class="sig-name descname"><span class="pre">to_dense_prediction_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(2,</span> <span class="pre">3)</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L234-L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.to_dense_prediction_model" title="Link to this definition">#</a></dt>
+<dd><p>Transform a sequential model with strides to a model that outputs
+dense predictions by removing the strides and instead inserting dilations.
+Modifies model in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>axis</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Axis to transform (in terms of intermediate output axes)
+can either be 2, 3, or (2,3).</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Does not yet work correctly for average pooling.
+Prior to version 0.1.7, there had been a bug that could move strides
+backwards one layer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-eegmodulemixin">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.EEGModuleMixin</span></code><a class="headerlink" href="#examples-using-braindecode-models-eegmodulemixin" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.get_output_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_output_shape()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.get_torchinfo_statistics"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_torchinfo_statistics()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.load_state_dict"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.load_state_dict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.to_dense_prediction_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-models-eegmodulemixin">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.EEGModuleMixin</span></code></a></li>
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+  <section id="braindecode-models-eegnetv1">
+<h1>braindecode.models.EEGNetv1<a class="headerlink" href="#braindecode-models-eegnetv1" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGNetv1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">32)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L217-L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv1" title="Link to this definition">#</a></dt>
+<dd><p>EEGNet model from Lawhern et al. 2016.</p>
+<p>See details in <a class="reference internal" href="#rcbe3da2652d4-eegnet" id="id1">[EEGNet]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> – The description is missing.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>second_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper description.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcbe3da2652d4-eegnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">EEGNet</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+S. M., Hung, C. P., &amp; Lance, B. J. (2016).
+EEGNet: A Compact Convolutional Network for EEG-based
+Brain-Computer Interfaces.
+arXiv preprint arXiv:1611.08024.</p>
+</div>
+</div>
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+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="braindecode-models-eegnetv4">
+<h1>braindecode.models.EEGNetv4<a class="headerlink" href="#braindecode-models-eegnetv4" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGNetv4">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L27-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv4" title="Link to this definition">#</a></dt>
+<dd><p>EEGNet v4 model from Lawhern et al 2018.</p>
+<p>See details in <a class="reference internal" href="#rbd7837e27d7f-eegnet4" id="id1">[EEGNet4]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>&quot;auto&quot;</em>) – If int, final length of convolutional filters.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>F1</strong> – The description is missing.</p></li>
+<li><p><strong>D</strong> – The description is missing.</p></li>
+<li><p><strong>F2</strong> – The description is missing.</p></li>
+<li><p><strong>kernel_length</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper description.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rbd7837e27d7f-eegnet4" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">EEGNet4</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+S. M., Hung, C. P., &amp; Lance, B. J. (2018).
+EEGNet: A Compact Convolutional Network for EEG-based
+Brain-Computer Interfaces.
+arXiv preprint arXiv:1611.08024.</p>
+</div>
+</div>
+</dd></dl>
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+<dt class="sig sig-object py" id="braindecode.models.EEGResNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGResNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_pool_length=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_first_filters=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layers_per_block=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_filter_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_epsilon=0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_weight_init_fn=&lt;function</span> <span class="pre">EEGResNet.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegresnet.py#L19-L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGResNet" title="Link to this definition">#</a></dt>
+<dd><p>Residual Network for EEG.</p>
+<p>XXX missing reference</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_pool_length</strong> – The description is missing.</p></li>
+<li><p><strong>n_first_filters</strong> – The description is missing.</p></li>
+<li><p><strong>n_layers_per_block</strong> – The description is missing.</p></li>
+<li><p><strong>first_filter_length</strong> – The description is missing.</p></li>
+<li><p><strong>nonlinearity</strong> – The description is missing.</p></li>
+<li><p><strong>split_first_layer</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_alpha</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_epsilon</strong> – The description is missing.</p></li>
+<li><p><strong>conv_weight_init_fn</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – <p>Alias for <cite>n_chans</cite>.
+n_classes :</p>
+<blockquote>
+<div><p>Alias for <cite>n_outputs</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>input_window_samples :</dt><dd><p>Alias for <cite>n_times</cite>.</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+</dd></dl>
+
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+  <section id="braindecode-models-hybridnet">
+<h1>braindecode.models.HybridNet<a class="headerlink" href="#braindecode-models-hybridnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.HybridNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">HybridNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L15-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet" title="Link to this definition">#</a></dt>
+<dd><p>Hybrid ConvNet model from Schirrmeister et al 2017.</p>
+<p>See <a class="reference internal" href="#r4d1af390a17e-schirrmeister2017" id="id1">[Schirrmeister2017]</a> for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – The description is missing.</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4d1af390a17e-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.HybridNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L117-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="braindecode-models-shallowfbcspnet">
+<h1>braindecode.models.ShallowFBCSPNet<a class="headerlink" href="#braindecode-models-shallowfbcspnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.ShallowFBCSPNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">ShallowFBCSPNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_nonlin=&lt;function</span> <span class="pre">square&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode='mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_nonlin=&lt;function</span> <span class="pre">safe_log&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/shallow_fbcsp.py#L14-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ShallowFBCSPNet" title="Link to this definition">#</a></dt>
+<dd><p>Shallow ConvNet model from Schirrmeister et al 2017.</p>
+<p>Model described in <a class="reference internal" href="#r9432a19f6121-schirrmeister2017" id="id1">[Schirrmeister2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, length of the input signal must be specified.</p></li>
+<li><p><strong>conv_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after convolution layers.</p></li>
+<li><p><strong>pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Method to use on pooling layers. “max” or “mean”.</p></li>
+<li><p><strong>pool_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after pooling layers.</p></li>
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+There would be no non-linearity between the split layers.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Alias for <cite>n_times</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r9432a19f6121-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+<section id="examples-using-braindecode-models-shallowfbcspnet">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.ShallowFBCSPNet</span></code><a class="headerlink" href="#examples-using-braindecode-models-shallowfbcspnet" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<dt class="sig sig-object py" id="braindecode.models.SleepStagerBlanco2020">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerBlanco2020</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L11-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020" title="Link to this definition">#</a></dt>
+<dd><p>Sleep staging architecture from Blanco et al 2020.</p>
+<p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id1">[Blanco2020]</a>.
+A series of seven convolutional layers with kernel sizes running down from 7 to 3,
+in an attempt to extract more general features at the beginning, while more specific
+and complex features were extracted in the final stages.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 20 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id2">[Blanco2020]</a>.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of groups for the convolution. Set to 2 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id3">[Blanco2020]</a> for 2 Channel EEG.
+controls the connections between inputs and outputs. n_channels and n_conv_chans must be
+divisible by n_groups.</p></li>
+<li><p><strong>max_pool_size</strong> – The description is missing.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+layers.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb3eee9d9e81a-blanco2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Blanco2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>)</span>
+<p>Fernandez-Blanco, E., Rivero, D. &amp; Pazos, A. Convolutional
+neural networks for sleep stage scoring on a two-channel EEG signal.
+Soft Comput 24, 4067–4079 (2020). <a class="reference external" href="https://doi.org/10.1007/s00500-019-04174-1">https://doi.org/10.1007/s00500-019-04174-1</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.SleepStagerBlanco2020.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L144-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerBlanco2020"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerBlanco2020.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020.forward()</span></code></a></li>
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+  <section id="braindecode-models-sleepstagerchambon2018">
+<h1>braindecode.models.SleepStagerChambon2018<a class="headerlink" href="#braindecode-models-sleepstagerchambon2018" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.SleepStagerChambon2018">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerChambon2018</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L11-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018" title="Link to this definition">#</a></dt>
+<dd><p>Sleep staging architecture from Chambon et al 2018.</p>
+<p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id1">[Chambon2018]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 8 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id2">[Chambon2018]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of filters in temporal convolution layers, in seconds. Set to 0.5
+in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id3">[Chambon2018]</a> (64 samples at sfreq=128).</p></li>
+<li><p><strong>max_pool_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Max pooling size, in seconds. Set to 0.125 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id4">[Chambon2018]</a> (16
+samples at sfreq=128).</p></li>
+<li><p><strong>pad_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Padding size, in seconds. Set to 0.25 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id5">[Chambon2018]</a> (half the
+temporal convolution kernel size).</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+layers.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>n_classes</strong> – Alias for <cite>n_outputs</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r89163c5eab6a-chambon2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Chambon2018<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>)</span>
+<p>Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &amp;
+Gramfort, A. (2018). A deep learning architecture for temporal sleep
+stage classification using multivariate and multimodal time series.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering,
+26(4), 758-769.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.SleepStagerChambon2018.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L135-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-sleepstagerchambon2018">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerChambon2018</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagerchambon2018" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerChambon2018"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a><ul class="nav section-nav flex-column">
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+  <section id="braindecode-models-sleepstagereldele2021">
+<h1>braindecode.models.SleepStagerEldele2021<a class="headerlink" href="#braindecode-models-sleepstagereldele2021" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.SleepStagerEldele2021">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerEldele2021</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">80</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_ff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_attn_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">after_reduced_cnn_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L16-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021" title="Link to this definition">#</a></dt>
+<dd><p>Sleep Staging Architecture from Eldele et al 2021.</p>
+<p>Attention based Neural Net for sleep staging as described in <a class="reference internal" href="#r63c3502458ce-eldele2021" id="id1">[Eldele2021]</a>.
+The code for the paper and this model is also available at <a class="reference internal" href="#r63c3502458ce-1" id="id2">[1]</a>.
+Takes single channel EEG as input.
+Feature extraction module based on multi-resolution convolutional neural network (MRCNN)
+and adaptive feature recalibration (AFR).
+The second module is the temporal context encoder (TCE) that leverages a multi-head attention
+mechanism to capture the temporal dependencies among the extracted features.</p>
+<p>Warning - This model was designed for signals of 30 seconds at 100Hz or 125Hz (in which case
+the reference architecture from <a class="reference internal" href="#r63c3502458ce-1" id="id3">[1]</a> which was validated on SHHS dataset <a class="reference internal" href="#r63c3502458ce-2" id="id4">[2]</a> will be used)
+to use any other input is likely to make the model perform in unintended ways.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_tce</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of TCE clones.</p></li>
+<li><p><strong>d_model</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Input dimension for the TCE.
+Also the input dimension of the first FC layer in the feed forward
+and the output of the second FC layer in the same.
+Increase for higher sampling rate/signal length.
+It should be divisible by n_attn_heads</p></li>
+<li><p><strong>d_ff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output dimension of the first FC layer in the feed forward and the
+input dimension of the second FC layer in the same.</p></li>
+<li><p><strong>n_attn_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads. It should be a factor of d_model</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate in the PositionWiseFeedforward layer and the TCE layers.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>after_reduced_cnn_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of output channels produced by the convolution in the AFR module.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r63c3502458ce-eldele2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Eldele2021</a><span class="fn-bracket">]</span></span>
+<p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
+Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
+Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
+</div>
+<div class="citation" id="r63c3502458ce-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id3">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/emadeldeen24/AttnSleep">emadeldeen24/AttnSleep</a></p>
+</div>
+<div class="citation" id="r63c3502458ce-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://sleepdata.org/datasets/shhs">https://sleepdata.org/datasets/shhs</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.SleepStagerEldele2021.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L145-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-sleepstagereldele2021">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagereldele2021" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerEldele2021"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerEldele2021.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.forward()</span></code></a></li>
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+  <section id="braindecode-models-tcn">
+<h1>braindecode.models.TCN<a class="headerlink" href="#braindecode-models-tcn" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.TCN">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TCN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L15-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN" title="Link to this definition">#</a></dt>
+<dd><p>Temporal Convolutional Network (TCN) from Bai et al 2018.</p>
+<p>See <a class="reference internal" href="#rcfca6ae7a220-bai2018" id="id1">[Bai2018]</a> for details.</p>
+<p>Code adapted from <a class="github reference external" href="https://github.com/locuslab/TCN/blob/master/TCN/tcn.py">locuslab/TCN</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_blocks</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of temporal blocks in the network</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of output filters of each convolution</p></li>
+<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – kernel size of the convolutions</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – dropout probability</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcfca6ae7a220-bai2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Bai2018</a><span class="fn-bracket">]</span></span>
+<p>Bai, S., Kolter, J. Z., &amp; Koltun, V. (2018).
+An empirical evaluation of generic convolutional and recurrent networks
+for sequence modeling.
+arXiv preprint arXiv:1803.01271.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.TCN.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L105-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
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+  <section id="braindecode-models-tidnet">
+<h1>braindecode.models.TIDNet<a class="headerlink" href="#braindecode-models-tidnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.TIDNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TIDNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_growth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spat_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_span</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bottleneck</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">summary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L169-L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet" title="Link to this definition">#</a></dt>
+<dd><p>Thinker Invariance DenseNet model from Kostas et al 2020.</p>
+<p>See <a class="reference internal" href="#re74dd80418c9-tidnet" id="id1">[TIDNet]</a> for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>s_growth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – DenseNet-style growth factor (added filters per DenseFilter)</p></li>
+<li><p><strong>t_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability</p></li>
+<li><p><strong>pooling</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Max temporal pooling (width and stride)</p></li>
+<li><p><strong>temp_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal layers</p></li>
+<li><p><strong>spat_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of DenseFilters</p></li>
+<li><p><strong>temp_span</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Percentage of n_times that defines the temporal filter length:
+temp_len = ceil(temp_span * n_times)
+e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal
+filter of length 75.</p></li>
+<li><p><strong>bottleneck</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Bottleneck factor within Densefilter</p></li>
+<li><p><strong>summary</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated
+automatically (n_times // pooling).</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Code adapted from: <a class="github reference external" href="https://github.com/SPOClab-ca/ThinkerInvariance/">SPOClab-ca/ThinkerInvariance</a></p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="re74dd80418c9-tidnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">TIDNet</a><span class="fn-bracket">]</span></span>
+<p>Kostas, D. &amp; Rudzicz, F.
+Thinker invariance: enabling deep neural networks for BCI across more
+people.
+J. Neural Eng. 17, 056008 (2020).
+doi: 10.1088/1741-2552/abb7a7.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.TIDNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L267-L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="braindecode-models-timedistributed">
+<h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-models-timedistributed" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.TimeDistributed">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TimeDistributed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L153-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed" title="Link to this definition">#</a></dt>
+<dd><p>Apply module on multiple windows.</p>
+<p>Apply the provided module on a sequence of windows and return their
+concatenation.
+Useful with sequence-to-prediction models (e.g. sleep stager which must map
+a sequence of consecutive windows to the label of the middle window in the
+sequence).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>nn.Module</em>) – Module to be applied to the input windows. Must accept an input of
+shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.TimeDistributed.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L173-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Sequence of windows, of shape (batch_size, seq_len, n_channels,
+n_times).</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Shape (batch_size, seq_len, output_size).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)">torch.Tensor</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-timedistributed">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.TimeDistributed</span></code><a class="headerlink" href="#examples-using-braindecode-models-timedistributed" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
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+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.USleep">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">USleep</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_time_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">complexity_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.67</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_skip_connection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0703125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_odd_conv_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L108-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep" title="Link to this definition">#</a></dt>
+<dd><p>Sleep staging architecture from Perslev et al 2021.</p>
+<p>U-Net (autoencoder with skip connections) feature-extractor for sleep
+staging described in <a class="reference internal" href="#r58a5e8182f0a-1" id="id1">[1]</a>.</p>
+<dl>
+<dt>For the encoder (‘down’):</dt><dd><p>– the temporal dimension shrinks (via maxpooling in the time-domain)
+– the spatial dimension expands (via more conv1d filters in the</p>
+<blockquote>
+<div><p>time-domain)</p>
+</div></blockquote>
+</dd>
+<dt>For the decoder (‘up’):</dt><dd><p>– the temporal dimension expands (via upsampling in the time-domain)
+– the spatial dimension shrinks (via fewer conv1d filters in the</p>
+<blockquote>
+<div><p>time-domain)</p>
+</div></blockquote>
+</dd>
+</dl>
+<p>Both do so at exponential rates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG or EOG channels. Set to 2 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id2">[1]</a> (1 EEG, 1 EOG).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – EEG sampling frequency. Set to 128 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id3">[1]</a>.</p></li>
+<li><p><strong>depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of conv blocks in encoding layer (number of 2x2 max pools)
+Note: each block halve the spatial dimensions of the features.</p></li>
+<li><p><strong>n_time_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 5 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id4">[1]</a>.</p></li>
+<li><p><strong>complexity_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Multiplicative factor for number of channels at each layer of the U-Net.
+Set to 2 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id5">[1]</a>.</p></li>
+<li><p><strong>with_skip_connection</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, use skip connections in decoder blocks.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs/classes. Set to 5.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the input, in seconds. Set to 30 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id6">[1]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the temporal convolution kernel, in seconds. Set to 9 / 128 in
+<a class="reference internal" href="#r58a5e8182f0a-1" id="id7">[1]</a>.</p></li>
+<li><p><strong>ensure_odd_conv_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True and the size of the convolutional kernel is an even number, one
+will be added to it to ensure it is odd, so that the decoder blocks can
+work. This can ne useful when using different sampling rates from 128
+or 100 Hz.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r58a5e8182f0a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>,<a role="doc-backlink" href="#id6">6</a>,<a role="doc-backlink" href="#id7">7</a>)</span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.USleep.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L297-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep.forward" title="Link to this definition">#</a></dt>
+<dd><p>If input x has shape (B, S, C, T), return y_pred of shape (B, n_classes, S).
+If input x has shape (B, C, T), return y_pred of shape (B, n_classes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-usleep">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code><a class="headerlink" href="#examples-using-braindecode-models-usleep" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.USleep.forward"><code class="docutils literal notranslate"><span class="pre">USleep.forward()</span></code></a></li>
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+<h1>braindecode.models.get_output_shape<a class="headerlink" href="#braindecode-models-get-output-shape" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.get_output_shape">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py#L65-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns shape of neural network output for batch size equal 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-models-get-output-shape">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.get_output_shape</span></code><a class="headerlink" href="#examples-using-braindecode-models-get-output-shape" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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+<h1>braindecode.preprocessing.Crop<a class="headerlink" href="#braindecode-preprocessing-crop" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Crop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Crop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Crop" title="Link to this definition">#</a></dt>
+<dd><p>Crop raw data file.</p>
+<blockquote>
+<div><p>Limit the data from the raw file to go between specific times. Note
+that the new <code class="docutils literal notranslate"><span class="pre">tmin</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">t=0</span></code> for all subsequently
+called functions (e.g., <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.time_as_index" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">time_as_index()</span></code></a>, or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Epochs</span></code></a>). New <a class="reference external" href="https://mne.tools/stable/glossary.html#term-first_samp" title="(in MNE v1.5)"><span class="xref std std-term">first_samp</span></a> and <a class="reference external" href="https://mne.tools/stable/glossary.html#term-last_samp" title="(in MNE v1.5)"><span class="xref std std-term">last_samp</span></a>
+are set accordingly.</p>
+<p>Thus function operates in-place on the instance.
+Use <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.copy" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.copy()</span></code></a> if operation on a copy is desired.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>tmin</strong><span class="classifier">float</span></dt><dd><p>Start time of the raw data to use in seconds (must be &gt;= 0).</p>
+</dd>
+<dt><strong>tmax</strong><span class="classifier">float</span></dt><dd><p>End time of the raw data to use in seconds (cannot exceed data duration).</p>
+</dd>
+<dt><strong>include_tmax</strong><span class="classifier">bool</span></dt><dd><p>If True (default), include tmax. If False, exclude tmax (similar to how
+Python indexing typically works).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>raw</strong><span class="classifier">instance of Raw</span></dt><dd><p>The cropped raw object, modified in-place.</p>
+</dd>
+<dt>See more details in mne.io.base.crop</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="braindecode-preprocessing-dropchannels">
+<h1>braindecode.preprocessing.DropChannels<a class="headerlink" href="#braindecode-preprocessing-dropchannels" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.DropChannels">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">DropChannels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.DropChannels" title="Link to this definition">#</a></dt>
+<dd><p>Drop channel(s).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ch_names</strong><span class="classifier">iterable or str</span></dt><dd><p>Iterable (e.g. list) of channel name(s) or channel name to remove.</p>
+</dd>
+<dt><strong>on_missing</strong><span class="classifier">‘raise’ | ‘warn’ | ‘ignore’</span></dt><dd><p>Can be <code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (default) to raise an error, <code class="docutils literal notranslate"><span class="pre">'warn'</span></code> to emit a
+warning, or <code class="docutils literal notranslate"><span class="pre">'ignore'</span></code> to ignore when entries in ch_names are not present in the raw instance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.23.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">reorder_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_types</span></code></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.channels.channels.drop_channels</p>
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+  <section id="braindecode-preprocessing-filter">
+<h1>braindecode.preprocessing.Filter<a class="headerlink" href="#braindecode-preprocessing-filter" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Filter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">l_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fir'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iir_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hamming'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_design</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'firwin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_by_annotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Filter" title="Link to this definition">#</a></dt>
+<dd><p>Filter a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>l_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the lower pass-band edge; for IIR filters, the lower
+cutoff frequency. If None the data are only low-passed.</p>
+</dd>
+<dt><strong>h_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the upper pass-band edge; for IIR filters, the upper
+cutoff frequency. If None the data are only high-passed.</p>
+</dd>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all data channels. Note that channels
+in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>filter_length</strong><span class="classifier">str | int</span></dt><dd><p>Length of the FIR filter to use (if applicable):</p>
+<ul class="simple">
+<li><p><strong>‘auto’ (default)</strong>: The filter length is chosen based
+on the size of the transition regions (6.6 times the reciprocal
+of the shortest transition band for fir_window=’hamming’
+and fir_design=”firwin2”, and half that for “firwin”).</p></li>
+<li><p><strong>str</strong>: A human-readable time in
+units of “s” or “ms” (e.g., “10s” or “5500ms”) will be
+converted to that number of samples if <code class="docutils literal notranslate"><span class="pre">phase=&quot;zero&quot;</span></code>, or
+the shortest power-of-two length at least that duration for
+<code class="docutils literal notranslate"><span class="pre">phase=&quot;zero-double&quot;</span></code>.</p></li>
+<li><p><strong>int</strong>: Specified length in samples. For fir_design=”firwin”,
+this should not be used.</p></li>
+</ul>
+</dd>
+<dt><strong>l_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the low cut-off frequency in Hz
+(high pass or cutoff 1 in bandpass). Can be “auto”
+(default) to use a multiple of <code class="docutils literal notranslate"><span class="pre">l_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">l_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">l_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>h_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the high cut-off frequency in Hz
+(low pass or cutoff 2 in bandpass). Can be “auto”
+(default in 0.14) to use a multiple of <code class="docutils literal notranslate"><span class="pre">h_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">h_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">2.</span><span class="p">),</span> <span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">2.</span> <span class="o">-</span> <span class="n">h_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly and method=’fir’.</p>
+</dd>
+<dt><strong>method</strong><span class="classifier">str</span></dt><dd><p>‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR
+forward-backward filtering (via filtfilt).</p>
+</dd>
+<dt><strong>iir_params</strong><span class="classifier">dict | None</span></dt><dd><p>Dictionary of parameters to use for IIR filtering.
+If iir_params is None and method=”iir”, 4th order Butterworth will be used.
+For more information, see <a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter()</span></code></a>.</p>
+</dd>
+<dt><strong>phase</strong><span class="classifier">str</span></dt><dd><p>Phase of the filter.
+When <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>, symmetric linear-phase FIR filters are constructed,
+and if <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default), the delay of this filter is compensated
+for, making it non-causal. If <code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code>,
+then this filter is applied twice, once forward, and once backward
+(also making it non-causal). If <code class="docutils literal notranslate"><span class="pre">'minimum'</span></code>, then a minimum-phase filter
+will be constructed and applied, which is causal but has weaker stop-band
+suppression.
+When <code class="docutils literal notranslate"><span class="pre">method='iir'</span></code>, <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default) or
+<code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code> constructs and applies IIR filter twice, once
+forward, and once backward (making it non-causal) using filtfilt.
+If <code class="docutils literal notranslate"><span class="pre">phase='forward'</span></code>, it constructs and applies forward IIR filter using
+lfilter.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.13.</span></p>
+</div>
+</dd>
+<dt><strong>fir_window</strong><span class="classifier">str</span></dt><dd><p>The window to use in FIR design, can be “hamming” (default),
+“hann” (default in 0.13), or “blackman”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>fir_design</strong><span class="classifier">str</span></dt><dd><p>Can be “firwin” (default) to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin.html#scipy.signal.firwin" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin()</span></code></a>,
+or “firwin2” to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin2.html#scipy.signal.firwin2" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin2()</span></code></a>. “firwin” uses
+a time-domain design technique that generally gives improved
+attenuation using fewer samples than “firwin2”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>skip_by_annotation</strong><span class="classifier">str | list of str</span></dt><dd><p>If a string (or list of str), any annotation segment that begins
+with the given string will not be included in filtering, and
+segments on either side of the given excluded annotated segment
+will be filtered separately (i.e., as independent signals).
+The default (<code class="docutils literal notranslate"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></code> will separately filter
+any segments that were concatenated by <a class="reference external" href="https://mne.tools/stable/generated/mne.concatenate_raws.html#mne.concatenate_raws" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.concatenate_raws()</span></code></a>
+or <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.append" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.append()</span></code></a>, or separated during acquisition.
+To disable, provide an empty list. Only used if <code class="docutils literal notranslate"><span class="pre">inst</span></code> is raw.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.16..</span></p>
+</div>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.</p>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Epochs, Evoked, or Raw</span></dt><dd><p>The filtered data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.Evoked.html#mne.Evoked.savgol_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.Evoked.savgol_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.notch_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.notch_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.resample" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.resample</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.filter_data.html#mne.filter.filter_data" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.filter_data</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.io.base.filter</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<h1>braindecode.preprocessing.Pick<a class="headerlink" href="#braindecode-preprocessing-pick" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Pick">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Pick</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">picks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Pick" title="Link to this definition">#</a></dt>
+<dd><p>Pick a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all channels. Note that channels in
+<code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>exclude</strong><span class="classifier">list | str</span></dt><dd><p>Set of channels to exclude, only used when picking based on
+types (e.g., exclude=”bads” when picks=”meg”).</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.24.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+<dt>See more details in mne.channels.channels.pick</dt><dd></dd>
+</dl>
+</dd>
+</dl>
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+  <section id="braindecode-preprocessing-preprocessor">
+<h1>braindecode.preprocessing.Preprocessor<a class="headerlink" href="#braindecode-preprocessing-preprocessor" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L27-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor" title="Link to this definition">#</a></dt>
+<dd><p>Preprocessor for an MNE Raw or Epochs object.</p>
+<p>Applies the provided preprocessing function to the data of a Raw or Epochs
+object.
+If the function is provided as a string, the method with that name will be
+used (e.g., ‘pick_channels’, ‘filter’, etc.).
+If it is provided as a callable and <cite>apply_on_array</cite> is True, the
+<cite>apply_function</cite> method of Raw and Epochs object will be used to apply the
+function on the internal arrays of Raw and Epochs.
+If <cite>apply_on_array</cite> is False, the callable must directly modify the Raw or
+Epochs object (e.g., by calling its method(s) or modifying its attributes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
+If callable, directly apply the callable to the object.</p></li>
+<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
+and Epochs object will be used to run <cite>fn</cite> on the underlying arrays
+directly. If False, <cite>fn</cite> must directly modify the Raw or Epochs object.</p></li>
+<li><p><strong>kwargs</strong> – Keyword arguments to be forwarded to the MNE function.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L66-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor.apply" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-preprocessing-preprocessor">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.Preprocessor</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocessor" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
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+  <section id="braindecode-preprocessing-resample">
+<h1>braindecode.preprocessing.Resample<a class="headerlink" href="#braindecode-preprocessing-resample" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Resample">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">npad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'boxcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Resample" title="Link to this definition">#</a></dt>
+<dd><p>Resample an array.</p>
+<blockquote>
+<div><p>Operates along the last dimension of the array.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>x</strong><span class="classifier">ndarray</span></dt><dd><p>Signal to resample.</p>
+</dd>
+<dt><strong>up</strong><span class="classifier">float</span></dt><dd><p>Factor to upsample by.</p>
+</dd>
+<dt><strong>down</strong><span class="classifier">float</span></dt><dd><p>Factor to downsample by.</p>
+</dd>
+<dt><strong>npad</strong><span class="classifier">int | str</span></dt><dd><p>Amount to pad the start and end of the data.
+Can also be “auto” to use a padding that will result in
+a power-of-two size (can be much faster).</p>
+</dd>
+<dt><strong>axis</strong><span class="classifier">int</span></dt><dd><p>Axis along which to resample (default is the last axis).</p>
+</dd>
+<dt><strong>window</strong><span class="classifier">str | tuple</span></dt><dd><p>Frequency-domain window to use in resampling.
+See <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html#scipy.signal.resample" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.resample()</span></code></a>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly.</p>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.
+The default is <code class="docutils literal notranslate"><span class="pre">'reflect_limited'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>The x array resampled.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See more details in mne.filter.resample</p>
+</dd></dl>
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+<h1>braindecode.preprocessing.SetEEGReference<a class="headerlink" href="#braindecode-preprocessing-seteegreference" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.SetEEGReference">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">SetEEGReference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">joint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.SetEEGReference" title="Link to this definition">#</a></dt>
+<dd><p>Specify which reference to use for EEG data.</p>
+<blockquote>
+<div><p>Use this function to explicitly specify the desired reference for EEG.
+This can be either an existing electrode or a new virtual channel.
+This function will re-reference the data according to the desired
+reference.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ref_channels</strong><span class="classifier">list of str | str</span></dt><dd><p>Can be:</p>
+<ul class="simple">
+<li><p>The name(s) of the channel(s) used to construct the reference.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'average'</span></code> to apply an average reference (default)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'REST'</span></code> to use the Reference Electrode Standardization Technique
+infinity reference <a href="#id1"><span class="problematic" id="id2">:footcite:`Yao2001`</span></a>.</p></li>
+<li><p>An empty list, in which case MNE will not attempt any re-referencing of
+the data</p></li>
+</ul>
+</dd>
+<dt><strong>projection</strong><span class="classifier">bool</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> this argument specifies if the
+average reference should be computed as a projection (True) or not
+(False; default). If <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>, the average reference is
+added as a projection and is not applied to the data (it can be
+applied afterwards with the <code class="docutils literal notranslate"><span class="pre">apply_proj</span></code> method). If
+<code class="docutils literal notranslate"><span class="pre">projection=False</span></code>, the average reference is directly applied to
+the data. If <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> is not <code class="docutils literal notranslate"><span class="pre">'average'</span></code>, <code class="docutils literal notranslate"><span class="pre">projection</span></code>
+must be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> (the default in this case).</p>
+</dd>
+<dt><strong>ch_type</strong><span class="classifier">list of str | str</span></dt><dd><p>The name of the channel type to apply the reference to.
+Valid channel types are <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, <code class="docutils literal notranslate"><span class="pre">'eeg'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ecog'</span></code>, <code class="docutils literal notranslate"><span class="pre">'seeg'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'dbs'</span></code>. If <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, the first channel type of eeg, ecog, seeg or dbs
+that is found (in that order) will be selected.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">list-of-str</span></code> is now supported with <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+</div>
+</dd>
+<dt><strong>forward</strong><span class="classifier">instance of Forward | None</span></dt><dd><p>Forward solution to use. Only used with <code class="docutils literal notranslate"><span class="pre">ref_channels='REST'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.21.</span></p>
+</div>
+</dd>
+<dt><strong>joint</strong><span class="classifier">bool</span></dt><dd><p>How to handle list-of-str <code class="docutils literal notranslate"><span class="pre">ch_type</span></code>. If False (default), one projector
+is created per channel type. If True, one projector is created across
+all channel types. This is only used when <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.2.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw | Epochs | Evoked</span></dt><dd><p>Data with EEG channels re-referenced. If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code> a projection will be added instead of
+directly re-referencing the data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.set_bipolar_reference.html#mne.set_bipolar_reference" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.set_bipolar_reference</span></code></a></dt><dd><p>Convenience function for creating bipolar references.</p>
+</dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Some common referencing schemes and the corresponding value for the
+<code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> parameter:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>Average reference:</dt><dd><p>A new virtual reference electrode is created by averaging the current
+EEG signal by setting <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>. Bad EEG channels are
+automatically excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>A single electrode:</dt><dd><p>Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list containing the name of the channel that
+will act as the new reference, for example <code class="docutils literal notranslate"><span class="pre">ref_channels=['Cz']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>The mean of multiple electrodes:</dt><dd><p>A new virtual reference electrode is created by computing the average
+of the current EEG signal recorded from two or more selected channels.
+Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list of channel names, indicating which
+channels to use. For example, to apply an average mastoid reference,
+when using the 10-20 naming scheme, set <code class="docutils literal notranslate"><span class="pre">ref_channels=['M1',</span> <span class="pre">'M2']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>REST</dt><dd><p>The given EEG electrodes are referenced to a point at infinity using the
+lead fields in <code class="docutils literal notranslate"><span class="pre">forward</span></code>, which helps standardize the signals.</p>
+</dd>
+</dl>
+</li>
+</ul>
+<ol class="arabic simple">
+<li><p>If a reference is requested that is not the average reference, this
+function removes any pre-existing average reference projections.</p></li>
+<li><p>During source localization, the EEG signal should have an average
+reference.</p></li>
+<li><p>In order to apply a reference, the data must be preloaded. This is not
+necessary if <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p></li>
+<li><p>For an average or REST reference, bad EEG channels are automatically
+excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p></li>
+</ol>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.9.0.</span></p>
+</div>
+<p>See more details in mne.channels.channels.set_eeg_reference</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.create_fixed_length_windows">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_fixed_length_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L146-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_fixed_length_windows" title="Link to this definition">#</a></dt>
+<dd><p>Windower that creates sliding windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<em>ConcatDataset</em>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from beginning of recording in samples.</p></li>
+<li><p><strong>stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stop offset from beginning of recording in samples. If None, set to be
+the end of the recording.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size in samples. If None, set to be the maximum possible window size, ie length of
+the recording, once offsets are accounted for.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
+windows will not overlap.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><em>None</em>) – Whether or not have a last overlapping window, when windows do not
+equally divide the continuous signal. Must be set to a bool if window size and stride are
+not None.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-preprocessing-create-fixed-length-windows">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-fixed-length-windows" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+    <li class="breadcrumb-item"><a href="../api.html" class="nav-link">API Reference</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">braindecode.preprocessing.create_windows_from_events</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="braindecode-preprocessing-create-windows-from-events">
+<h1>braindecode.preprocessing.create_windows_from_events<a class="headerlink" href="#braindecode-preprocessing-create-windows-from-events" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.create_windows_from_events">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L29-L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_events" title="Link to this definition">#</a></dt>
+<dd><p>Create windows based on events in mne.Raw.</p>
+<p>This function extracts windows of size window_size_samples in the interval
+[trial onset + trial_start_offset_samples, trial onset + trial duration +
+trial_stop_offset_samples] around each trial, with a separation of
+window_stride_samples between consecutive windows. If the last window
+around an event does not end at trial_stop_offset_samples and
+drop_last_window is set to False, an additional overlapping window that
+ends at trial_stop_offset_samples is created.</p>
+<p>Windows are extracted from the interval defined by the following:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>                                        <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+                <span class="n">trial</span> <span class="n">onset</span>                <span class="n">duration</span>
+<span class="o">|--------------------|------------------------|-----------------------|</span>
+<span class="n">trial</span> <span class="n">onset</span> <span class="o">-</span>                                             <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+<span class="n">trial_start_offset_samples</span>                                   <span class="n">duration</span> <span class="o">+</span>
+                                            <span class="n">trial_stop_offset_samples</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from original trial onsets, in samples. Defaults to zero.</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Stop offset from original trial stop, in samples. Defaults to zero.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size. If None, the window size is inferred from the original
+trial size of the first trial and trial_start_offset_samples and
+trial_stop_offset_samples.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows, in samples. If None, the window stride is
+inferred from the original trial size of the first trial and
+trial_start_offset_samples and trial_stop_offset_samples.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If False, an additional overlapping window that ends at
+trial_stop_offset_samples will be extracted around each event when the
+last window does not end exactly at trial_stop_offset_samples.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to numerical target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects. This is useful to
+reduce disk reading overhead when returning windows in a training
+scenario, however very large data might not fit into memory.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials with inconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-preprocessing-create-windows-from-events">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-events" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
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+<dd></dd></dl>
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+<section id="examples-using-braindecode-preprocessing-create-windows-from-target-channels">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-target-channels" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
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+  <section id="braindecode-preprocessing-exponential-moving-demean">
+<h1>braindecode.preprocessing.exponential_moving_demean<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-demean" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_demean">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_demean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L314-L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_demean" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving demeanining.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Deman the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Demean data before to this index with regular demeaning.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>demeaned</strong> – Demeaned data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
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+  <section id="braindecode-preprocessing-exponential-moving-standardize">
+<h1>braindecode.preprocessing.exponential_moving_standardize<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-standardize" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_standardize">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L264-L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_standardize" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving standardization.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Then, compute exponential moving variance <span class="math notranslate nohighlight">\(v_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}\)</span>.</p>
+<p>Finally, standardize the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t) / max(\sqrt{-&gt;v_t}, eps)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Standardize data before to this index with regular standardization.</p></li>
+<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Stabilizer for division by zero variance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>standardized</strong> – Standardized data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-preprocessing-exponential-moving-standardize">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.exponential_moving_standardize</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-exponential-moving-standardize" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
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+<h1>braindecode.preprocessing.filterbank<a class="headerlink" href="#braindecode-preprocessing-filterbank" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.filterbank">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">filterbank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_bands</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_original_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by_frequency_band</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">mne_filter_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L349-L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.filterbank" title="Link to this definition">#</a></dt>
+<dd><p>Applies multiple bandpass filters to the signals in raw. The raw will be
+modified in-place and number of channels in raw will be updated to
+len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if
+drop_original_signals).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – The raw signals to be filtered.</p></li>
+<li><p><strong>frequency_bands</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>)</em>) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</p></li>
+<li><p><strong>drop_original_signals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to drop the original unfiltered signals</p></li>
+<li><p><strong>order_by_frequency_band</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True will return channels ordered by frequency bands, so if there
+are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned
+channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order
+will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].</p></li>
+<li><p><strong>mne_filter_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Keyword arguments for filtering supported by mne.io.Raw.filter().
+Please refer to mne for a detailed explanation.</p></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
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+  <section id="braindecode-preprocessing-preprocess">
+<h1>braindecode.preprocessing.preprocess<a class="headerlink" href="#braindecode-preprocessing-preprocess" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">preprocess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L88-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess" title="Link to this definition">#</a></dt>
+<dd><p>Apply preprocessors to a concat dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of BaseDataset or WindowsDataset datasets to be preprocessed.</p></li>
+<li><p><strong>preprocessors</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference internal" href="../source/braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><em>Preprocessor</em></a><em>)</em>) – List of Preprocessor objects to apply to the dataset.</p></li>
+<li><p><strong>save_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – If a string, the preprocessed data will be saved under the specified
+directory and the datasets in <code class="docutils literal notranslate"><span class="pre">concat_ds</span></code> will be reloaded with
+<cite>preload=False</cite>.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <cite>save_dir</cite> is provided, controls whether to delete the old
+subdirectories that will be written to under <cite>save_dir</cite>. If False and
+the corresponding subdirectories already exist, a <code class="docutils literal notranslate"><span class="pre">FileExistsError</span></code>
+will be raised.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Number of jobs for parallel execution. See <cite>joblib.Parallel</cite> for
+a more detailed explanation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Preprocessed dataset.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="examples-using-braindecode-preprocessing-preprocess">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocess" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
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+      
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+  
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+</div>
+              
+              
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+  
+  <section id="braindecode-preprocessing-scale">
+<h1>braindecode.preprocessing.scale<a class="headerlink" href="#braindecode-preprocessing-scale" title="Link to this heading">#</a></h1>
+<section id="examples-using-braindecode-preprocessing-scale">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-scale" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-preprocessing-scale">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale</span></code></a></li>
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\ No newline at end of file
diff --git a/0.8/generated/braindecode.regressor.EEGRegressor.html b/0.8/generated/braindecode.regressor.EEGRegressor.html
new file mode 100644
index 00000000..a931f67a
--- /dev/null
+++ b/0.8/generated/braindecode.regressor.EEGRegressor.html
@@ -0,0 +1,1055 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>braindecode.regressor.EEGRegressor &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
+  </script>
+  
+  <!-- Loaded before other Sphinx assets -->
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+<link href="../_static/styles/pydata-sphinx-theme.css?digest=e353d410970836974a52" rel="stylesheet" />
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+  <link href="../_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=e353d410970836974a52" rel="stylesheet" />
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+<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
+
+    <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
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+    <link rel="stylesheet" type="text/css" href="../_static/style.css?v=09706775" />
+  
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+                function gtag(){ dataLayer.push(arguments); }
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+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>DOCUMENTATION_OPTIONS.pagename = 'generated/braindecode.regressor.EEGRegressor';</script>
+    <script>
+        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        </script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="braindecode.models.EEGModuleMixin" href="braindecode.models.EEGModuleMixin.html" />
+    <link rel="prev" title="braindecode.classifier.EEGClassifier" href="braindecode.classifier.EEGClassifier.html" />
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+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
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+  
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+  <section id="braindecode-regressor-eegregressor">
+<h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-regressor-eegregressor" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.regressor.</span></span><span class="sig-name descname"><span class="pre">EEGRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L19-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor" title="Link to this definition">#</a></dt>
+<dd><p>Regressor that calls loss function directly.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.MSELoss</em><em>)</em>) – Mean squared error loss.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L171-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.fit" title="Link to this definition">#</a></dt>
+<dd><p>Initialize and fit the module.</p>
+<p>If the module was already initialized, by calling fit, the
+module will be re-initialized (unless <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> is True).
+If possible, signal-related parameters are inferred from the
+data and passed to the module at initialisation.
+Depending on the type of input passed, the following parameters
+are inferred:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>mne.Epochs: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code>, <code class="docutils literal notranslate"><span class="pre">chs_info</span></code>,
+<code class="docutils literal notranslate"><span class="pre">sfreq</span></code>, <code class="docutils literal notranslate"><span class="pre">input_window_seconds</span></code></p></li>
+<li><p>numpy array: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>WindowsDataset with <code class="docutils literal notranslate"><span class="pre">targets_from='metadata'</span></code>
+(or BaseConcatDataset of such datasets): <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>other Dataset: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code></p></li>
+<li><p>other types: no parameters are inferred.</p></li>
+</ul>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>mne.Epochs</p></li>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>y</strong> (<em>target data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – The same data types as for <code class="docutils literal notranslate"><span class="pre">X</span></code> are supported. If your X is
+a Dataset that contains the target, <code class="docutils literal notranslate"><span class="pre">y</span></code> may be set to
+None.</p></li>
+<li><p><strong>**fit_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Additional parameters passed to the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of
+the module and to the <code class="docutils literal notranslate"><span class="pre">self.train_split</span></code> call.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L75-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L82-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+</dl>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Regressors predict regression targets, so output of this method
+can’t be interpreted as probabilities. We advise you to use
+<cite>predict</cite> method instead of <cite>predict_proba</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L132-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
+</ul>
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+++ b/0.8/generated/braindecode.samplers.BalancedSequenceSampler.html
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+    <title>braindecode.samplers.BalancedSequenceSampler &#8212; Braindecode 0.8 documentation</title>
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+<h1>braindecode.samplers.BalancedSequenceSampler<a class="headerlink" href="#braindecode-samplers-balancedsequencesampler" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">BalancedSequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sequences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L174-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Balanced sampling of sequences of consecutive windows with categorical
+targets.</p>
+<p>Balanced sampling of sequences inspired by the approach of <a class="reference internal" href="../source/braindecode.samplers.html#r3d821f159c06-perslev2021" id="id1">[Perslev2021]</a>:
+1. Uniformly sample a recording out of the available ones.
+2. Uniformly sample one of the classes.
+3. Sample a window of the corresponding class in the selected recording.
+4. Extract a sequence of windows around the sampled window.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.
+Must contain a column <cite>target</cite> with categorical targets.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_sequences</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sequences to sample.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r3d821f159c06-perslev2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Perslev2021</a><span class="fn-bracket">]</span></span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,
+Igel C. U-Sleep: resilient high-frequency sleep staging. npj Digit.
+Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler.sample_class">
+<span class="sig-name descname"><span class="pre">sample_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L210-L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler.sample_class" title="Link to this definition">#</a></dt>
+<dd><p>Return a random class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>rec_ind</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Index to the recording to sample from. If None, the recording will
+be uniformly sampled across available recordings.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>int</em> – Sampled class.</p></li>
+<li><p><em>int</em> – Index to the recording the class was sampled from.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
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+  <section id="braindecode-samplers-recordingsampler">
+<h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecode-samplers-recordingsampler" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RecordingSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L15-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler" title="Link to this definition">#</a></dt>
+<dd><p>Base sampler simplifying sampling from recordings.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – <p>DataFrame with at least one of {subject, session, run} columns for each
+window in the BaseConcatDataset to sample examples from. Normally
+obtained with <cite>BaseConcatDataset.get_metadata()</cite>. For instance,
+<cite>metadata.head()</cite> might look like this:</p>
+<blockquote>
+<div><p>i_window_in_trial  i_start_in_trial  i_stop_in_trial  target  subject    session    run</p>
+</div></blockquote>
+<p>0                  0                 0              500      -1        4  session_T  run_0
+1                  1               500             1000      -1        4  session_T  run_0
+2                  2              1000             1500      -1        4  session_T  run_0
+3                  3              1500             2000      -1        4  session_T  run_0
+4                  4              2000             2500      -1        4  session_T  run_0</p>
+</p></li>
+<li><p><strong>random_state</strong> (<em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>Series with MultiIndex index which contains the subject, session, run
+and window indices information in an easily accessible structure for
+quick sampling of windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.n_recordings">
+<span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.n_recordings" title="Link to this definition">#</a></dt>
+<dd><p>Number of recordings available.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_recording">
+<span class="sig-name descname"><span class="pre">sample_recording</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L86-L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_recording" title="Link to this definition">#</a></dt>
+<dd><p>Return a random recording index.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_window">
+<span class="sig-name descname"><span class="pre">sample_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L92-L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_window" title="Link to this definition">#</a></dt>
+<dd><p>Return a specific window.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-samplers-recordingsampler">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RecordingSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-recordingsampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
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+  
+  <section id="braindecode-samplers-relativepositioningsampler">
+<h1>braindecode.samplers.RelativePositioningSampler<a class="headerlink" href="#braindecode-samplers-relativepositioningsampler" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RelativePositioningSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">same_rec_neg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L14-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler" title="Link to this definition">#</a></dt>
+<dd><p>Sample examples for the relative positioning task from <a class="reference internal" href="../source/braindecode.samplers.html#r0467437a2408-banville2020" id="id1">[Banville2020]</a>.</p>
+<p>Sample examples as tuples of two window indices, with a label indicating
+whether the windows are close or far, as defined by tau_pos and tau_neg.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>tau_pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the positive context, in samples. A positive pair contains two
+windows x1 and x2 which are separated by at most <cite>tau_pos</cite> samples.</p></li>
+<li><p><strong>tau_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the negative context, in samples. A negative pair contains two
+windows x1 and x2 which are separated by at least <cite>tau_neg</cite> samples and
+at most <cite>tau_max</cite> samples. Ignored if <cite>same_rec_neg</cite> is False.</p></li>
+<li><p><strong>n_examples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of pairs to extract.</p></li>
+<li><p><strong>tau_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – See <cite>tau_neg</cite>.</p></li>
+<li><p><strong>same_rec_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample negative pairs from within the same recording. If
+False, sample negative pairs from two different recordings.</p></li>
+<li><p><strong>random_state</strong> (<em>None</em><em> | </em><em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0467437a2408-banville2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Banville2020</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,
+&amp; Gramfort, A. (2020). Uncovering the structure of clinical EEG
+signals with self-supervised learning.
+arXiv preprint arXiv:2007.16104.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler.presample">
+<span class="sig-name descname"><span class="pre">presample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L94-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler.presample" title="Link to this definition">#</a></dt>
+<dd><p>Presample examples.</p>
+<p>Once presampled, the examples are the same from one epoch to another.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-samplers-relativepositioningsampler">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RelativePositioningSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-relativepositioningsampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div></div><div style='clear:both'></div></section>
+</section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a><ul class="nav section-nav flex-column">
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+  <section id="braindecode-samplers-sequencesampler">
+<h1>braindecode.samplers.SequenceSampler<a class="headerlink" href="#braindecode-samplers-sequencesampler" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">SequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L109-L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.SequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Sample sequences of consecutive windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of windows between two consecutive sequences.</p></li>
+<li><p><strong>random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample sequences randomly. If False, sample sequences in
+order.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>See RecordingSampler.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.file_ids">
+<span class="sig-name descname"><span class="pre">file_ids</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.file_ids" title="Link to this definition">#</a></dt>
+<dd><p>Array of shape (n_sequences,) that indicates from which file each
+sequence comes from. Useful e.g. to do self-ensembling.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-samplers-sequencesampler">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-sequencesampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
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+<h1>braindecode.training.CroppedLoss<a class="headerlink" href="#braindecode-training-croppedloss" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L11-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss" title="Link to this definition">#</a></dt>
+<dd><p>Compute Loss after averaging predictions across time.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L20-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-training-croppedloss">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedloss" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div></div><div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedLoss.forward"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.forward()</span></code></a></li>
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+<h1>braindecode.training.CroppedTimeSeriesEpochScoring<a class="headerlink" href="#braindecode-training-croppedtimeseriesepochscoring" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTimeSeriesEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L229-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Class to compute scores for trials from a model that predicts (super)crops with
+time series target.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L234-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-training-croppedtimeseriesepochscoring">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTimeSeriesEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtimeseriesepochscoring" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></li>
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+  <section id="braindecode-training-croppedtrialepochscoring">
+<h1>braindecode.training.CroppedTrialEpochScoring<a class="headerlink" href="#braindecode-training-croppedtrialepochscoring" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTrialEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L116-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Class to compute scores for trials from a model that predicts (super)crops.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_batch_end">
+<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L150-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each batch.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L160-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-training-croppedtrialepochscoring">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTrialEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtrialepochscoring" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
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+<h1>braindecode.training.PostEpochTrainScoring<a class="headerlink" href="#braindecode-training-postepochtrainscoring" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">PostEpochTrainScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L312-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring" title="Link to this definition">#</a></dt>
+<dd><p>Epoch Scoring class that recomputes predictions after the epoch
+on the training in validation mode.</p>
+<p>Note: For unknown reasons, this affects global random generator and
+therefore all results may change slightly if you add this scoring callback.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>scoring</strong> (<em>None</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, or </em><em>callable</em><em> (</em><em>default=None</em><em>)</em>) – If None, use the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of the model. If str, it
+should be a valid sklearn scorer (e.g. “f1”, “accuracy”). If a
+callable, it should have the signature (model, X, y), and it
+should return a scalar. This works analogously to the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter in sklearn’s <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> et al.</p></li>
+<li><p><strong>lower_is_better</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether lower scores should be considered better or worse.</p></li>
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – If not an explicit string, tries to infer the name from the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> argument.</p></li>
+<li><p><strong>target_extractor</strong> (<em>callable</em><em> (</em><em>default=to_numpy</em><em>)</em>) – This is called on y before it is passed to scoring.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L353-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
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+<h1>braindecode.training.TimeSeriesLoss<a class="headerlink" href="#braindecode-training-timeseriesloss" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L35-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss" title="Link to this definition">#</a></dt>
+<dd><p>Compute Loss between timeseries targets and predictions.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)
+Assumes targets are in shape:
+n_batch size x n_classes x window_len (in time)
+If the targets contain NaNs, the NaNs will be masked out and the loss will be only computed for
+predictions valid corresponding to valid target values.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L48-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-training-timeseriesloss">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.TimeSeriesLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-timeseriesloss" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.TimeSeriesLoss.forward"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.forward()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-training-timeseriesloss">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.TimeSeriesLoss</span></code></a></li>
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+<h1>braindecode.training.mixup_criterion<a class="headerlink" href="#braindecode-training-mixup-criterion" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.mixup_criterion">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">mixup_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L69-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.mixup_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Implements loss for Mixup for EEG data. See <a class="reference internal" href="../source/braindecode.training.html#r54dff61a9a59-1" id="id1">[1]</a>.</p>
+<p>Implementation based on <a class="reference internal" href="../source/braindecode.training.html#r54dff61a9a59-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Predictions from the model.</p></li>
+<li><p><strong>target</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – For predictions without mixup, the targets as a tensor. If mixup has
+been applied, a list containing the targets of the two mixed
+samples and the mixing coefficients as tensors.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r54dff61a9a59-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+mixup: Beyond Empirical Risk Minimization
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r54dff61a9a59-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
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+<h1>braindecode.training.predict_trials<a class="headerlink" href="#braindecode-training-predict-trials" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.predict_trials">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L403-L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset given module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – A pytorch model implementing forward.</p></li>
+<li><p><strong>dataset</strong> (<a class="reference internal" href="../source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The batch size used to iterate the dataset.</p></li>
+<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of workers used in DataLoader to iterate the dataset.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
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+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.trial_preds_from_window_preds">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">trial_preds_from_window_preds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_window_in_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_stop_in_trials</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L21-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.trial_preds_from_window_preds" title="Link to this definition">#</a></dt>
+<dd><p>Assigning window predictions to trials  while removing duplicate
+predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>ndarrays</em><em> (</em><em>at least 2darrays</em><em>)</em>) – <dl class="simple">
+<dt>List of window predictions, in each window prediction</dt><dd><p>time is in axis=1</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>i_window_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Index/number of window in trial</p></li>
+<li><p><strong>i_stop_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – stop position of window in trial</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>preds_per_trial</strong> – Predictions in each trial, duplicates removed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of ndarrays</p>
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+  <section id="braindecode-util-set-random-seeds">
+<h1>braindecode.util.set_random_seeds<a class="headerlink" href="#braindecode-util-set-random-seeds" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.set_random_seeds">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">set_random_seeds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudnn_benchmark</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L17-L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.set_random_seeds" title="Link to this definition">#</a></dt>
+<dd><p>Set seeds for python random module numpy.random and torch.</p>
+<p>For more details about reproducibility in pytorch see
+<a class="reference external" href="https://pytorch.org/docs/stable/notes/randomness.html">https://pytorch.org/docs/stable/notes/randomness.html</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random seed.</p></li>
+<li><p><strong>cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to set cuda seed with torch.</p></li>
+<li><p><strong>cudnn_benchmark</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=None</em><em>)</em>) – Whether pytorch will use cudnn benchmark. When set to <cite>None</cite> it will not modify
+torch.backends.cudnn.benchmark (displays warning in the case of possible lack of
+reproducibility). When set to True, results may not be reproducible (no warning displayed).
+When set to False it may slow down computations.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>In some cases setting environment variable <cite>PYTHONHASHSEED</cite> may be needed before running a
+script to ensure full reproducibility. See
+<a class="reference external" href="https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14">https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14</a></p>
+<p>Using this function may not ensure full reproducibility of the results as we do not set
+<cite>torch.use_deterministic_algorithms(True)</cite>.</p>
+</dd></dl>
+
+<section id="examples-using-braindecode-util-set-random-seeds">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.util.set_random_seeds</span></code><a class="headerlink" href="#examples-using-braindecode-util-set-random-seeds" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
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+<h1>braindecode.visualization.plot_confusion_matrix<a class="headerlink" href="#braindecode-visualization-plot-confusion-matrix" title="Link to this heading">#</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.plot_confusion_matrix">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">plot_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colormap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textcolor='black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontweight='normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_row_labels=90</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_col_labels=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_f1_score=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_axes=(0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_precision=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names_fontsize=12</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/confusion_matrices.py#L10-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.plot_confusion_matrix" title="Link to this definition">#</a></dt>
+<dd><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="../source/braindecode.visualization.html#r8046536b33dd-1" id="id1">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>confusion_mat</strong> (<em>2d numpy array</em>) – A confusion matrix, e.g. sklearn confusion matrix:
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html">https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html</a></p></li>
+<li><p><strong>class_names</strong> (<em>array</em><em>, </em><em>optional</em>) – List of classes/targets.</p></li>
+<li><p><strong>figsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size of the generated confusion matrix figure.</p></li>
+<li><p><strong>colormap</strong> (<em>matplotlib cm colormap</em><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>textcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Color  of the text in the figure.</p></li>
+<li><p><strong>vmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>vmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>fontweight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Weight of the font in the figure:
+[ ‘normal’ | ‘bold’ | ‘heavy’ | ‘light’ | ‘ultrabold’ | ‘ultralight’]</p></li>
+<li><p><strong>rotate_row_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the row labels</p></li>
+<li><p><strong>rotate_col_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the column labels</p></li>
+<li><p><strong>with_f1_score</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>norm_axes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>rotate_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>class_names_fontsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib figure</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8046536b33dd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017. Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+<section id="examples-using-braindecode-visualization-plot-confusion-matrix">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix</span></code><a class="headerlink" href="#examples-using-braindecode-visualization-plot-confusion-matrix" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
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+    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-bs-toggle="dropdown">
+      0.8  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </button>
+    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
+    <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article"></div>
+              
+              
+<div>
+  
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#_"><strong>_</strong></a>
+ | <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#K"><strong>K</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ 
+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier._criteria">_criteria (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier._criteria">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier._criteria">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor._criteria">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor._criteria">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor._criteria">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier._modules">_modules (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier._modules">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier._modules">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor._modules">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor._modules">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor._modules">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier._optimizers">_optimizers (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier._optimizers">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier._optimizers">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor._optimizers">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor._optimizers">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor._optimizers">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.add_log_softmax">add_log_softmax (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.util.aggregate_probas">aggregate_probas() (in module braindecode.models.util)</a>
+</li>
+      <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Preprocessor.apply">apply() (braindecode.preprocessing.mne_preprocess.Preprocessor method)</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.Preprocessor.apply">(braindecode.preprocessing.preprocess.Preprocessor method)</a>
+</li>
+        <li><a href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor.apply">(braindecode.preprocessing.Preprocessor method)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor.apply">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet">ATCNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.atcnet.ATCNet">(class in braindecode.models.atcnet)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader">AugmentedDataLoader (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader">(class in braindecode.augmentation.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv">AvgPool2dWithConv (class in braindecode.models.modules)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler">BalancedSequenceSampler (class in braindecode.samplers)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.BalancedSequenceSampler">(class in braindecode.samplers.base)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.bandstop_filter.html#braindecode.augmentation.functional.bandstop_filter">bandstop_filter() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.bandstop_filter">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter">BandstopFilter (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.BandstopFilter">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset">BaseConcatDataset (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset">(class in braindecode.datasets.base)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset">BaseDataset (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseDataset">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseDataset">(class in braindecode.datasets.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.batch_size">batch_size (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.batch_size">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset">BBCIDataset (class in braindecode.datasets.bbci)</a>
+</li>
+      <li><a href="generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4">BCICompetitionIVDataset4 (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4">(class in braindecode.datasets.bcicomp)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.bias">bias (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.bias">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.BNCI2014001.html#braindecode.datasets.BNCI2014001">BNCI2014001 (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001">(class in braindecode.datasets.moabb)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001.BNCI2014001">BNCI2014001.BNCI2014001 (class in braindecode.datasets)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.BNCI2014001">(class in braindecode.datasets.moabb)</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode
+
+      <ul>
+        <li><a href="api.html#module-braindecode">module</a>, <a href="source/braindecode.html#module-braindecode">[1]</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.augmentation
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.augmentation.base
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.base">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.augmentation.functional
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.functional">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.augmentation.transforms
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.transforms">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.classifier
+
+      <ul>
+        <li><a href="source/braindecode.html#module-braindecode.classifier">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.base
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.base">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.bbci
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.bbci">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.bcicomp
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.bcicomp">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.mne
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.mne">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.moabb
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.moabb">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.sleep_physionet
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.sleep_physionet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.tuh
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.tuh">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datasets.xy
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.xy">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.mne
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.mne">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.preprocess
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.preprocess">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.serialization
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.serialization">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.util
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.util">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.windowers
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.windowers">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.datautil.xy
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.xy">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.eegneuralnet
+
+      <ul>
+        <li><a href="source/braindecode.html#module-braindecode.eegneuralnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.atcnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.atcnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.base
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.base">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.deep4
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.deep4">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    braindecode.models.deepsleepnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.deepsleepnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eegconformer
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegconformer">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eeginception
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eeginception_erp
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception_erp">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eeginception_mi
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception_mi">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eegitnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegitnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eegnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.eegresnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegresnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.functions
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.functions">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.hybrid
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.hybrid">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.modules
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.modules">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.shallow_fbcsp
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.shallow_fbcsp">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.sleep_stager_blanco_2020
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_blanco_2020">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.sleep_stager_chambon_2018
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_chambon_2018">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.sleep_stager_eldele_2021
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_eldele_2021">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.tcn
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.tcn">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.tidnet
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.tidnet">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.usleep
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.usleep">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.models.util
+
+      <ul>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.util">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.preprocessing
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.preprocessing.mne_preprocess
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.mne_preprocess">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.preprocessing.preprocess
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.preprocessing.windowers
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.windowers">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.regressor
+
+      <ul>
+        <li><a href="source/braindecode.html#module-braindecode.regressor">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.samplers
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.samplers.base
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers.base">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.samplers.ssl
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers.ssl">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.training
+
+      <ul>
+        <li><a href="source/braindecode.training.html#module-braindecode.training">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.training.callbacks
+
+      <ul>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.callbacks">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.training.losses
+
+      <ul>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.losses">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.training.scoring
+
+      <ul>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.scoring">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.util
+
+      <ul>
+        <li><a href="source/braindecode.html#module-braindecode.util">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.version
+
+      <ul>
+        <li><a href="source/braindecode.html#module-braindecode.version">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.visualization
+
+      <ul>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.visualization.confusion_matrices
+
+      <ul>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization.confusion_matrices">module</a>
+</li>
+      </ul></li>
+      <li>
+    braindecode.visualization.gradients
+
+      <ul>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization.gradients">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.callbacks_">callbacks_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.callbacks_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.callbacks_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.callbacks_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.callbacks_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.callbacks_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d">CausalConv1d (class in braindecode.models.modules)</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.functional.channels_dropout.html#braindecode.augmentation.functional.channels_dropout">channels_dropout() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_dropout">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.functional.channels_permute.html#braindecode.augmentation.functional.channels_permute">channels_permute() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_permute">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.functional.channels_shuffle.html#braindecode.augmentation.functional.channels_shuffle">channels_shuffle() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.channels_shuffle">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout">ChannelsDropout (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle">ChannelsShuffle (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry">ChannelsSymmetry (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.tcn.Chomp1d">Chomp1d (class in braindecode.models.tcn)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.chs_info">chs_info (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.classes_">classes_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.classes_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.classes_">(braindecode.EEGClassifier attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.CombinedConv">CombinedConv (class in braindecode.models.modules)</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose">Compose (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Compose">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Compose">(class in braindecode.augmentation.base)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.visualization.compute_amplitude_gradients.html#braindecode.visualization.compute_amplitude_gradients">compute_amplitude_gradients() (in module braindecode.visualization)</a>, <a href="source/braindecode.visualization.html#braindecode.visualization.compute_amplitude_gradients">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.visualization.html#braindecode.visualization.gradients.compute_amplitude_gradients">(in module braindecode.visualization.gradients)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.visualization.html#braindecode.visualization.gradients.compute_amplitude_gradients_for_X">compute_amplitude_gradients_for_X() (in module braindecode.visualization.gradients)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint">Conv2dWithConstraint (class in braindecode.models.eegnet)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.corr">corr() (in module braindecode.util)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.cov">cov() (in module braindecode.util)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows">create_fixed_length_windows() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_fixed_length_windows">(in module braindecode.preprocessing.windowers)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs">create_from_mne_epochs() (in module braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.create_from_mne_epochs">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.mne.create_from_mne_epochs">(in module braindecode.datasets.mne)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw">create_from_mne_raw() (in module braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.create_from_mne_raw">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.mne.create_from_mne_raw">(in module braindecode.datasets.mne)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y">create_from_X_y() (in module braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.create_from_X_y">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.xy.create_from_X_y">(in module braindecode.datasets.xy)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.html#braindecode.util.create_mne_dummy_raw">create_mne_dummy_raw() (in module braindecode.util)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events">create_windows_from_events() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_windows_from_events">(in module braindecode.preprocessing.windowers)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels">create_windows_from_target_channels() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_target_channels">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_windows_from_target_channels">(in module braindecode.preprocessing.windowers)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.criterion_">criterion_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.criterion_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.criterion_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.criterion_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.criterion_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.criterion_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop">Crop (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Crop">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Crop">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss">CroppedLoss (class in braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedLoss">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.CroppedLoss">(class in braindecode.training.losses)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring">CroppedTimeSeriesEpochScoring (class in braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.CroppedTimeSeriesEpochScoring">(class in braindecode.training.scoring)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring">CroppedTrialEpochScoring (class in braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedTrialEpochScoring">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring">(class in braindecode.training.scoring)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">cuda_dependent_attributes_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.cuda_dependent_attributes_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.cuda_dependent_attributes_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.cumulative_sizes">cumulative_sizes (braindecode.datasets.base.BaseConcatDataset attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.cumulative_sizes">(braindecode.datasets.BaseConcatDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.cumulative_sizes">(braindecode.datasets.bcicomp.BCICompetitionIVDataset4 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.cumulative_sizes">(braindecode.datasets.BCICompetitionIVDataset4 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001.cumulative_sizes">(braindecode.datasets.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.HGD.cumulative_sizes">(braindecode.datasets.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.cumulative_sizes">(braindecode.datasets.moabb.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD.cumulative_sizes">(braindecode.datasets.moabb.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset.cumulative_sizes">(braindecode.datasets.moabb.MOABBDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.MOABBDataset.cumulative_sizes">(braindecode.datasets.MOABBDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet.cumulative_sizes">(braindecode.datasets.sleep_physionet.SleepPhysionet attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet.cumulative_sizes">(braindecode.datasets.SleepPhysionet attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.TUH.cumulative_sizes">(braindecode.datasets.TUH attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUH.cumulative_sizes">(braindecode.datasets.tuh.TUH attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal.cumulative_sizes">(braindecode.datasets.tuh.TUHAbnormal attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.TUHAbnormal.cumulative_sizes">(braindecode.datasets.TUHAbnormal attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.HGD.Schirrmeister2017.data_path">data_path() (braindecode.datasets.HGD.Schirrmeister2017 method)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD.Schirrmeister2017.data_path">(braindecode.datasets.moabb.HGD.Schirrmeister2017 method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.dataset">dataset (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.dataset">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.datasets">datasets (braindecode.datasets.base.BaseConcatDataset attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.datasets">(braindecode.datasets.BaseConcatDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.datasets">(braindecode.datasets.bcicomp.BCICompetitionIVDataset4 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.datasets">(braindecode.datasets.BCICompetitionIVDataset4 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001.datasets">(braindecode.datasets.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.HGD.datasets">(braindecode.datasets.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.datasets">(braindecode.datasets.moabb.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD.datasets">(braindecode.datasets.moabb.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset.datasets">(braindecode.datasets.moabb.MOABBDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.MOABBDataset.datasets">(braindecode.datasets.MOABBDataset attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet.datasets">(braindecode.datasets.sleep_physionet.SleepPhysionet attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet.datasets">(braindecode.datasets.SleepPhysionet attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.TUH.datasets">(braindecode.datasets.TUH attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUH.datasets">(braindecode.datasets.tuh.TUH attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal.datasets">(braindecode.datasets.tuh.TUHAbnormal attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.TUHAbnormal.datasets">(braindecode.datasets.TUHAbnormal attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net">Deep4Net (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.deep4.Deep4Net">(class in braindecode.models.deep4)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet">DeepSleepNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.deepsleepnet.DeepSleepNet">(class in braindecode.models.deepsleepnet)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.deprecated_args">deprecated_args() (in module braindecode.models.base)</a>
+</li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.description">description (braindecode.datasets.base.BaseConcatDataset property)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseDataset.description">(braindecode.datasets.base.BaseDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.description">(braindecode.datasets.base.EEGWindowsDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.description">(braindecode.datasets.base.WindowsDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.description">(braindecode.datasets.BaseConcatDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseDataset.description">(braindecode.datasets.BaseDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.WindowsDataset.description">(braindecode.datasets.WindowsDataset property)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.dilation">dilation (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.dilation">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.html#braindecode.classifier.EEGClassifier.doc">doc (braindecode.classifier.EEGClassifier attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BNCI2014001.doc">(braindecode.datasets.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.HGD.doc">(braindecode.datasets.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.doc">(braindecode.datasets.moabb.BNCI2014001 attribute)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD.doc">(braindecode.datasets.moabb.HGD attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.doc">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.doc">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.regressor.EEGRegressor.doc">(braindecode.regressor.EEGRegressor attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.download">download() (braindecode.datasets.bcicomp.BCICompetitionIVDataset4 static method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4.download">(braindecode.datasets.BCICompetitionIVDataset4 static method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.download">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.drop_last">drop_last (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.drop_last">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels">DropChannels (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.DropChannels">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.DropChannels">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.html#braindecode.EEGClassifier">EEGClassifier (class in braindecode)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier">(class in braindecode.classifier)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer">EEGConformer (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegconformer.EEGConformer">(class in braindecode.models.eegconformer)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception">EEGInception (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eeginception.EEGInception">(class in braindecode.models.eeginception)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP">EEGInceptionERP (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eeginception_erp.EEGInceptionERP">(class in braindecode.models.eeginception_erp)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI">EEGInceptionMI (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eeginception_mi.EEGInceptionMI">(class in braindecode.models.eeginception_mi)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet">EEGITNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegitnet.EEGITNet">(class in braindecode.models.eegitnet)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin">EEGModuleMixin (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin">(class in braindecode.models.base)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1">EEGNetv1 (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegnet.EEGNetv1">(class in braindecode.models.eegnet)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4">EEGNetv4 (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegnet.EEGNetv4">(class in braindecode.models.eegnet)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.html#braindecode.EEGRegressor">EEGRegressor (class in braindecode)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor">(class in braindecode.regressor)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet">EEGResNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegresnet.EEGResNet">(class in braindecode.models.eegresnet)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset">EEGWindowsDataset (class in braindecode.datasets.base)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.Ensure4d">Ensure4d (class in braindecode.models.modules)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.exponential_moving_demean.html#braindecode.preprocessing.exponential_moving_demean">exponential_moving_demean() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_demean">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.exponential_moving_demean">(in module braindecode.preprocessing.preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize">exponential_moving_standardize() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_standardize">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.exponential_moving_standardize">(in module braindecode.preprocessing.preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.Expression">Expression (class in braindecode.models.modules)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.tcn.Chomp1d.extra_repr">extra_repr() (braindecode.models.tcn.Chomp1d method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.fetch_data_with_moabb">fetch_data_with_moabb() (in module braindecode.datasets.moabb)</a>
+</li>
+      <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.SequenceSampler.file_ids">file_ids (braindecode.samplers.base.SequenceSampler attribute)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler.file_ids">(braindecode.samplers.SequenceSampler attribute)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler.file_ids">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter">Filter (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Filter">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Filter">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank">filterbank() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.filterbank">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.filterbank">(in module braindecode.preprocessing.preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.html#braindecode.EEGRegressor.fit">fit() (braindecode.EEGRegressor method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.fit">(braindecode.regressor.EEGRegressor method)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.fit">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Crop.fn">fn (braindecode.preprocessing.Crop attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.DropChannels.fn">(braindecode.preprocessing.DropChannels attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Filter.fn">(braindecode.preprocessing.Filter attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Crop.fn">(braindecode.preprocessing.mne_preprocess.Crop attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.DropChannels.fn">(braindecode.preprocessing.mne_preprocess.DropChannels attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Filter.fn">(braindecode.preprocessing.mne_preprocess.Filter attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Pick.fn">(braindecode.preprocessing.mne_preprocess.Pick attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Resample.fn">(braindecode.preprocessing.mne_preprocess.Resample attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.SetEEGReference.fn">(braindecode.preprocessing.mne_preprocess.SetEEGReference attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Pick.fn">(braindecode.preprocessing.Pick attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Resample.fn">(braindecode.preprocessing.Resample attribute)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference.fn">(braindecode.preprocessing.SetEEGReference attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Compose.forward">forward() (braindecode.augmentation.base.Compose method)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform.forward">(braindecode.augmentation.base.Transform method)</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose.forward">(braindecode.augmentation.Compose method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Compose.forward">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform.forward">(braindecode.augmentation.Transform method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Transform.forward">[1]</a>
+</li>
+        <li><a href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet.forward">(braindecode.models.ATCNet method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.atcnet.ATCNet.forward">(braindecode.models.atcnet.ATCNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet.forward">(braindecode.models.DeepSleepNet method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.deepsleepnet.DeepSleepNet.forward">(braindecode.models.deepsleepnet.DeepSleepNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer.forward">(braindecode.models.EEGConformer method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegconformer.EEGConformer.forward">(braindecode.models.eegconformer.EEGConformer method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.eeginception_mi.EEGInceptionMI.forward">(braindecode.models.eeginception_mi.EEGInceptionMI method)</a>
+</li>
+        <li><a href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI.forward">(braindecode.models.EEGInceptionMI method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.forward">(braindecode.models.eegnet.Conv2dWithConstraint method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.hybrid.HybridNet.forward">(braindecode.models.hybrid.HybridNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet.forward">(braindecode.models.HybridNet method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv.forward">(braindecode.models.modules.AvgPool2dWithConv method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.forward">(braindecode.models.modules.CausalConv1d method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CombinedConv.forward">(braindecode.models.modules.CombinedConv method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.Ensure4d.forward">(braindecode.models.modules.Ensure4d method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.Expression.forward">(braindecode.models.modules.Expression method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper.forward">(braindecode.models.modules.IntermediateOutputWrapper method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.MaxNormLinear.forward">(braindecode.models.modules.MaxNormLinear method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.TimeDistributed.forward">(braindecode.models.modules.TimeDistributed method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020.forward">(braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020 method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018.forward">(braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018 method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.forward">(braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021 method)</a>
+</li>
+        <li><a href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020.forward">(braindecode.models.SleepStagerBlanco2020 method)</a>
+</li>
+        <li><a href="generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018.forward">(braindecode.models.SleepStagerChambon2018 method)</a>
+</li>
+        <li><a href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021.forward">(braindecode.models.SleepStagerEldele2021 method)</a>
+</li>
+        <li><a href="generated/braindecode.models.TCN.html#braindecode.models.TCN.forward">(braindecode.models.TCN method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.Chomp1d.forward">(braindecode.models.tcn.Chomp1d method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.TCN.forward">(braindecode.models.tcn.TCN method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.TemporalBlock.forward">(braindecode.models.tcn.TemporalBlock method)</a>
+</li>
+        <li><a href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet.forward">(braindecode.models.TIDNet method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tidnet.TIDNet.forward">(braindecode.models.tidnet.TIDNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed.forward">(braindecode.models.TimeDistributed method)</a>
+</li>
+        <li><a href="generated/braindecode.models.USleep.html#braindecode.models.USleep.forward">(braindecode.models.USleep method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.usleep.USleep.forward">(braindecode.models.usleep.USleep method)</a>
+</li>
+        <li><a href="generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss.forward">(braindecode.training.CroppedLoss method)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedLoss.forward">[1]</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.CroppedLoss.forward">(braindecode.training.losses.CroppedLoss method)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.TimeSeriesLoss.forward">(braindecode.training.losses.TimeSeriesLoss method)</a>
+</li>
+        <li><a href="generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss.forward">(braindecode.training.TimeSeriesLoss method)</a>, <a href="source/braindecode.training.html#braindecode.training.TimeSeriesLoss.forward">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.augmentation.functional.frequency_shift.html#braindecode.augmentation.functional.frequency_shift">frequency_shift() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.frequency_shift">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift">FrequencyShift (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FrequencyShift">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.ft_surrogate.html#braindecode.augmentation.functional.ft_surrogate">ft_surrogate() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.ft_surrogate">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate">FTSurrogate (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FTSurrogate">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.augmentation.functional.gaussian_noise.html#braindecode.augmentation.functional.gaussian_noise">gaussian_noise() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.gaussian_noise">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.GaussianNoise.html#braindecode.augmentation.GaussianNoise">GaussianNoise (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.GaussianNoise">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset.get_all_sensors">get_all_sensors() (braindecode.datasets.bbci.BBCIDataset static method)</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter.get_augmentation_params">get_augmentation_params() (braindecode.augmentation.BandstopFilter method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.BandstopFilter.get_augmentation_params">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform.get_augmentation_params">(braindecode.augmentation.base.Transform method)</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout.get_augmentation_params">(braindecode.augmentation.ChannelsDropout method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle.get_augmentation_params">(braindecode.augmentation.ChannelsShuffle method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">(braindecode.augmentation.ChannelsSymmetry method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift.get_augmentation_params">(braindecode.augmentation.FrequencyShift method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FrequencyShift.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate.get_augmentation_params">(braindecode.augmentation.FTSurrogate method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FTSurrogate.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.GaussianNoise.html#braindecode.augmentation.GaussianNoise.get_augmentation_params">(braindecode.augmentation.GaussianNoise method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.GaussianNoise.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup.get_augmentation_params">(braindecode.augmentation.Mixup method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Mixup.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.SensorsRotation.html#braindecode.augmentation.SensorsRotation.get_augmentation_params">(braindecode.augmentation.SensorsRotation method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsRotation.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask.get_augmentation_params">(braindecode.augmentation.SmoothTimeMask method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform.get_augmentation_params">(braindecode.augmentation.Transform method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Transform.get_augmentation_params">[1]</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.get_augmentation_params">(braindecode.augmentation.transforms.BandstopFilter method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.get_augmentation_params">(braindecode.augmentation.transforms.ChannelsDropout method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.get_augmentation_params">(braindecode.augmentation.transforms.ChannelsShuffle method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.get_augmentation_params">(braindecode.augmentation.transforms.ChannelsSymmetry method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.get_augmentation_params">(braindecode.augmentation.transforms.FrequencyShift method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.get_augmentation_params">(braindecode.augmentation.transforms.FTSurrogate method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.get_augmentation_params">(braindecode.augmentation.transforms.GaussianNoise method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.get_augmentation_params">(braindecode.augmentation.transforms.Mixup method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.get_augmentation_params">(braindecode.augmentation.transforms.SensorsRotation method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.get_augmentation_params">(braindecode.augmentation.transforms.SmoothTimeMask method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.html#braindecode.util.get_balanced_batches">get_balanced_batches() (in module braindecode.util)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer.get_fc_size">get_fc_size() (braindecode.models.EEGConformer method)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.eegconformer.EEGConformer.get_fc_size">(braindecode.models.eegconformer.EEGConformer method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_iterator">get_iterator() (braindecode.classifier.EEGClassifier method)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.get_iterator">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.get_iterator">(braindecode.EEGClassifier method)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.get_iterator">(braindecode.EEGRegressor method)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.get_iterator">(braindecode.regressor.EEGRegressor method)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.get_iterator">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_loss">get_loss() (braindecode.classifier.EEGClassifier method)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.get_loss">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.get_loss">(braindecode.EEGClassifier method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.get_metadata">get_metadata() (braindecode.datasets.base.BaseConcatDataset method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata">(braindecode.datasets.BaseConcatDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.get_metadata">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.get_output_shape">get_output_shape() (braindecode.models.base.EEGModuleMixin method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.get_output_shape">(braindecode.models.EEGModuleMixin method)</a>
+</li>
+        <li><a href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape">(in module braindecode.models)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.util.get_output_shape">(in module braindecode.models.util)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.get_torchinfo_statistics">get_torchinfo_statistics() (braindecode.models.base.EEGModuleMixin method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.get_torchinfo_statistics">(braindecode.models.EEGModuleMixin method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.groups">groups (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.groups">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.datasets.HGD.html#braindecode.datasets.HGD">HGD (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.HGD">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD">(class in braindecode.datasets.moabb)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.HGD.Schirrmeister2017">HGD.Schirrmeister2017 (class in braindecode.datasets)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.HGD.Schirrmeister2017">(class in braindecode.datasets.moabb)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet">HybridNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.hybrid.HybridNet">(class in braindecode.models.hybrid)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.augmentation.functional.identity.html#braindecode.augmentation.functional.identity">identity() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.identity">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.functions.identity">(in module braindecode.models.functions)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.IdentityTransform.html#braindecode.augmentation.IdentityTransform">IdentityTransform (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.IdentityTransform">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform">(class in braindecode.augmentation.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.in_channels">in_channels (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.in_channels">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.MaxNormLinear.in_features">in_features (braindecode.models.modules.MaxNormLinear attribute)</a>
+</li>
+      <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.info">info (braindecode.samplers.base.RecordingSampler attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.SequenceSampler.info">(braindecode.samplers.base.SequenceSampler attribute)</a>
+</li>
+        <li><a href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.info">(braindecode.samplers.RecordingSampler attribute)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler.info">[1]</a>
+</li>
+        <li><a href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler.info">(braindecode.samplers.SequenceSampler attribute)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler.info">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.initialized_">initialized_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.initialized_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.initialized_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.initialized_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.initialized_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.initialized_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.input_shape">input_shape (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.input_window_seconds">input_window_seconds (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper">IntermediateOutputWrapper (class in braindecode.models.modules)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="K">K</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.kernel_size">kernel_size (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.kernel_size">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset.load">load() (braindecode.datasets.bbci.BBCIDataset method)</a>
+</li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bbci.load_bbci_sets_from_folder">load_bbci_sets_from_folder() (in module braindecode.datasets.bbci)</a>
+</li>
+      <li><a href="generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset">load_concat_dataset() (in module braindecode.datautil)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#braindecode.datautil.serialization.load_concat_dataset">(in module braindecode.datautil.serialization)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.load_state_dict">load_state_dict() (braindecode.models.base.EEGModuleMixin method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.load_state_dict">(braindecode.models.EEGModuleMixin method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.mapping">mapping (braindecode.models.base.EEGModuleMixin attribute)</a>
+</li>
+      <li><a href="source/braindecode.training.html#braindecode.training.callbacks.MaxNormConstraintCallback">MaxNormConstraintCallback (class in braindecode.training.callbacks)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.MaxNormLinear">MaxNormLinear (class in braindecode.models.modules)</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup">Mixup (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Mixup">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.mixup.html#braindecode.augmentation.functional.mixup">mixup() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.mixup">[1]</a>
+</li>
+      <li><a href="generated/braindecode.training.mixup_criterion.html#braindecode.training.mixup_criterion">mixup_criterion() (in module braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.mixup_criterion">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.mixup_criterion">(in module braindecode.training.losses)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset">MOABBDataset (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.MOABBDataset">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset">(class in braindecode.datasets.moabb)</a>
+</li>
+      </ul></li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="api.html#module-braindecode">braindecode</a>, <a href="source/braindecode.html#module-braindecode">[1]</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation">braindecode.augmentation</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.base">braindecode.augmentation.base</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.functional">braindecode.augmentation.functional</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#module-braindecode.augmentation.transforms">braindecode.augmentation.transforms</a>
+</li>
+        <li><a href="source/braindecode.html#module-braindecode.classifier">braindecode.classifier</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets">braindecode.datasets</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.base">braindecode.datasets.base</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.bbci">braindecode.datasets.bbci</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.bcicomp">braindecode.datasets.bcicomp</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.mne">braindecode.datasets.mne</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.moabb">braindecode.datasets.moabb</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.sleep_physionet">braindecode.datasets.sleep_physionet</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.tuh">braindecode.datasets.tuh</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#module-braindecode.datasets.xy">braindecode.datasets.xy</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil">braindecode.datautil</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.mne">braindecode.datautil.mne</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.preprocess">braindecode.datautil.preprocess</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.serialization">braindecode.datautil.serialization</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.util">braindecode.datautil.util</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.windowers">braindecode.datautil.windowers</a>
+</li>
+        <li><a href="source/braindecode.datautil.html#module-braindecode.datautil.xy">braindecode.datautil.xy</a>
+</li>
+        <li><a href="source/braindecode.html#module-braindecode.eegneuralnet">braindecode.eegneuralnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models">braindecode.models</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.atcnet">braindecode.models.atcnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.base">braindecode.models.base</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.deep4">braindecode.models.deep4</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.deepsleepnet">braindecode.models.deepsleepnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegconformer">braindecode.models.eegconformer</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception">braindecode.models.eeginception</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception_erp">braindecode.models.eeginception_erp</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eeginception_mi">braindecode.models.eeginception_mi</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegitnet">braindecode.models.eegitnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegnet">braindecode.models.eegnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.eegresnet">braindecode.models.eegresnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.functions">braindecode.models.functions</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.hybrid">braindecode.models.hybrid</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.modules">braindecode.models.modules</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.shallow_fbcsp">braindecode.models.shallow_fbcsp</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_blanco_2020">braindecode.models.sleep_stager_blanco_2020</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_chambon_2018">braindecode.models.sleep_stager_chambon_2018</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.sleep_stager_eldele_2021">braindecode.models.sleep_stager_eldele_2021</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.tcn">braindecode.models.tcn</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.tidnet">braindecode.models.tidnet</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.usleep">braindecode.models.usleep</a>
+</li>
+        <li><a href="source/braindecode.models.html#module-braindecode.models.util">braindecode.models.util</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing">braindecode.preprocessing</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.mne_preprocess">braindecode.preprocessing.mne_preprocess</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess">braindecode.preprocessing.preprocess</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.windowers">braindecode.preprocessing.windowers</a>
+</li>
+        <li><a href="source/braindecode.html#module-braindecode.regressor">braindecode.regressor</a>
+</li>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers">braindecode.samplers</a>
+</li>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers.base">braindecode.samplers.base</a>
+</li>
+        <li><a href="source/braindecode.samplers.html#module-braindecode.samplers.ssl">braindecode.samplers.ssl</a>
+</li>
+        <li><a href="source/braindecode.training.html#module-braindecode.training">braindecode.training</a>
+</li>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.callbacks">braindecode.training.callbacks</a>
+</li>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.losses">braindecode.training.losses</a>
+</li>
+        <li><a href="source/braindecode.training.html#module-braindecode.training.scoring">braindecode.training.scoring</a>
+</li>
+        <li><a href="source/braindecode.html#module-braindecode.util">braindecode.util</a>
+</li>
+        <li><a href="source/braindecode.html#module-braindecode.version">braindecode.version</a>
+</li>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization">braindecode.visualization</a>
+</li>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization.confusion_matrices">braindecode.visualization.confusion_matrices</a>
+</li>
+        <li><a href="source/braindecode.visualization.html#module-braindecode.visualization.gradients">braindecode.visualization.gradients</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.module_">module_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.module_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.module_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.module_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.module_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.module_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datautil.html#braindecode.datautil.util.ms_to_samples">ms_to_samples() (in module braindecode.datautil.util)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_chans">n_chans (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_outputs">n_outputs (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.n_recordings">n_recordings (braindecode.samplers.base.RecordingSampler attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#id4">(braindecode.samplers.base.RecordingSampler property)</a>
+</li>
+        <li><a href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.n_recordings">(braindecode.samplers.RecordingSampler attribute)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler.n_recordings">[1]</a>
+</li>
+        <li><a href="source/braindecode.samplers.html#id0">(braindecode.samplers.RecordingSampler property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_times">n_times (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.np_to_th">np_to_th() (in module braindecode.util)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.np_to_var">np_to_var() (in module braindecode.util)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.tidnet.TIDNet.num_features">num_features (braindecode.models.tidnet.TIDNet property)</a>
+</li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.num_workers">num_workers (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.num_workers">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.training.html#braindecode.training.callbacks.MaxNormConstraintCallback.on_batch_end">on_batch_end() (braindecode.training.callbacks.MaxNormConstraintCallback method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end">(braindecode.training.CroppedTrialEpochScoring method)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end">[1]</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring.on_batch_end">(braindecode.training.scoring.CroppedTrialEpochScoring method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">on_epoch_end() (braindecode.training.CroppedTimeSeriesEpochScoring method)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">[1]</a>
+
+      <ul>
+        <li><a href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_epoch_end">(braindecode.training.CroppedTrialEpochScoring method)</a>, <a href="source/braindecode.training.html#braindecode.training.CroppedTrialEpochScoring.on_epoch_end">[1]</a>
+</li>
+        <li><a href="generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring.on_epoch_end">(braindecode.training.PostEpochTrainScoring method)</a>, <a href="source/braindecode.training.html#braindecode.training.PostEpochTrainScoring.on_epoch_end">[1]</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.CroppedTimeSeriesEpochScoring.on_epoch_end">(braindecode.training.scoring.CroppedTimeSeriesEpochScoring method)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring.on_epoch_end">(braindecode.training.scoring.CroppedTrialEpochScoring method)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.PostEpochTrainScoring.on_epoch_end">(braindecode.training.scoring.PostEpochTrainScoring method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Compose.operation">operation (braindecode.augmentation.base.Compose attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform.operation">(braindecode.augmentation.base.Transform attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.Compose.operation">(braindecode.augmentation.Compose attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.Transform.operation">(braindecode.augmentation.Transform attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter.operation">operation() (braindecode.augmentation.BandstopFilter static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.BandstopFilter.operation">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform.operation">(braindecode.augmentation.base.IdentityTransform static method)</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout.operation">(braindecode.augmentation.ChannelsDropout static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle.operation">(braindecode.augmentation.ChannelsShuffle static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry.operation">(braindecode.augmentation.ChannelsSymmetry static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift.operation">(braindecode.augmentation.FrequencyShift static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FrequencyShift.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate.operation">(braindecode.augmentation.FTSurrogate static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.FTSurrogate.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.GaussianNoise.html#braindecode.augmentation.GaussianNoise.operation">(braindecode.augmentation.GaussianNoise static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.GaussianNoise.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.IdentityTransform.html#braindecode.augmentation.IdentityTransform.operation">(braindecode.augmentation.IdentityTransform static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.IdentityTransform.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup.operation">(braindecode.augmentation.Mixup static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Mixup.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.SensorsRotation.html#braindecode.augmentation.SensorsRotation.operation">(braindecode.augmentation.SensorsRotation static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsRotation.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip.operation">(braindecode.augmentation.SignFlip static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SignFlip.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask.operation">(braindecode.augmentation.SmoothTimeMask static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask.operation">[1]</a>
+</li>
+        <li><a href="generated/braindecode.augmentation.TimeReverse.html#braindecode.augmentation.TimeReverse.operation">(braindecode.augmentation.TimeReverse static method)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.TimeReverse.operation">[1]</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.operation">(braindecode.augmentation.transforms.BandstopFilter static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.operation">(braindecode.augmentation.transforms.ChannelsDropout static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.operation">(braindecode.augmentation.transforms.ChannelsShuffle static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.operation">(braindecode.augmentation.transforms.ChannelsSymmetry static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.operation">(braindecode.augmentation.transforms.FrequencyShift static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.operation">(braindecode.augmentation.transforms.FTSurrogate static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.operation">(braindecode.augmentation.transforms.GaussianNoise static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.operation">(braindecode.augmentation.transforms.Mixup static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.operation">(braindecode.augmentation.transforms.SensorsRotation static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip.operation">(braindecode.augmentation.transforms.SignFlip static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.operation">(braindecode.augmentation.transforms.SmoothTimeMask static method)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse.operation">(braindecode.augmentation.transforms.TimeReverse static method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.out_channels">out_channels (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.out_channels">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.modules.MaxNormLinear.out_features">out_features (braindecode.models.modules.MaxNormLinear attribute)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.output_padding">output_padding (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.output_padding">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.padding">padding (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.padding">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.padding_mode">padding_mode (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.padding_mode">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick">Pick (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Pick">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Pick">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.pin_memory">pin_memory (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.pin_memory">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.pin_memory_device">pin_memory_device (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.pin_memory_device">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix">plot_confusion_matrix() (in module braindecode.visualization)</a>, <a href="source/braindecode.visualization.html#braindecode.visualization.plot_confusion_matrix">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.visualization.html#braindecode.visualization.confusion_matrices.plot_confusion_matrix">(in module braindecode.visualization.confusion_matrices)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.possible_subjects">possible_subjects (braindecode.datasets.bcicomp.BCICompetitionIVDataset4 attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.possible_subjects">(braindecode.datasets.BCICompetitionIVDataset4 attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring">PostEpochTrainScoring (class in braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.PostEpochTrainScoring">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.PostEpochTrainScoring">(class in braindecode.training.scoring)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict">predict() (braindecode.classifier.EEGClassifier method)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.predict">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.predict">(braindecode.EEGClassifier method)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_proba">predict_proba() (braindecode.classifier.EEGClassifier method)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.predict_proba">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.predict_proba">(braindecode.EEGClassifier method)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.predict_proba">(braindecode.EEGRegressor method)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_proba">(braindecode.regressor.EEGRegressor method)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.predict_proba">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_trials">predict_trials() (braindecode.classifier.EEGClassifier method)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.predict_trials">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.predict_trials">(braindecode.EEGClassifier method)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.predict_trials">(braindecode.EEGRegressor method)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_trials">(braindecode.regressor.EEGRegressor method)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.predict_trials">[1]</a>
+</li>
+        <li><a href="generated/braindecode.training.predict_trials.html#braindecode.training.predict_trials">(in module braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.predict_trials">[1]</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.predict_trials">(in module braindecode.training.scoring)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.prefetch_factor">prefetch_factor (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.prefetch_factor">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.prefixes_">prefixes_ (braindecode.classifier.EEGClassifier attribute)</a>, <a href="source/braindecode.html#braindecode.classifier.EEGClassifier.prefixes_">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.html#braindecode.EEGClassifier.prefixes_">(braindecode.EEGClassifier attribute)</a>
+</li>
+        <li><a href="source/braindecode.html#braindecode.EEGRegressor.prefixes_">(braindecode.EEGRegressor attribute)</a>
+</li>
+        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.prefixes_">(braindecode.regressor.EEGRegressor attribute)</a>, <a href="source/braindecode.html#braindecode.regressor.EEGRegressor.prefixes_">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess">preprocess() (in module braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.preprocess">(in module braindecode.preprocessing.preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor">Preprocessor (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Preprocessor">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.preprocess.Preprocessor">(class in braindecode.preprocessing.preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler.presample">presample() (braindecode.samplers.RelativePositioningSampler method)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler.presample">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.ssl.RelativePositioningSampler.presample">(braindecode.samplers.ssl.RelativePositioningSampler method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform.probability">probability (braindecode.augmentation.base.Transform property)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.Transform.probability">(braindecode.augmentation.Transform property)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.html#braindecode.util.read_all_file_names">read_all_file_names() (in module braindecode.util)</a>
+</li>
+      <li><a href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler">RecordingSampler (class in braindecode.samplers)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.RecordingSampler">(class in braindecode.samplers.base)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler">RelativePositioningSampler (class in braindecode.samplers)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.ssl.RelativePositioningSampler">(class in braindecode.samplers.ssl)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample">Resample (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.Resample">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Resample">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.return_feats">return_feats (braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021 attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.functions.safe_log">safe_log() (in module braindecode.models.functions)</a>
+</li>
+      <li><a href="generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler.sample_class">sample_class() (braindecode.samplers.BalancedSequenceSampler method)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler.sample_class">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.BalancedSequenceSampler.sample_class">(braindecode.samplers.base.BalancedSequenceSampler method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.sample_recording">sample_recording() (braindecode.samplers.base.RecordingSampler method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_recording">(braindecode.samplers.RecordingSampler method)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler.sample_recording">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.sample_window">sample_window() (braindecode.samplers.base.RecordingSampler method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_window">(braindecode.samplers.RecordingSampler method)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.RecordingSampler.sample_window">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.sampler">sampler (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.sampler">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datautil.html#braindecode.datautil.util.samples_to_ms">samples_to_ms() (in module braindecode.datautil.util)</a>
+</li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.save">save() (braindecode.datasets.base.BaseConcatDataset method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save">(braindecode.datasets.BaseConcatDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.save">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datautil.save_concat_dataset.html#braindecode.datautil.save_concat_dataset">save_concat_dataset() (in module braindecode.datautil)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datautil.html#braindecode.datautil.serialization.save_concat_dataset">(in module braindecode.datautil.serialization)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.sensors_rotation.html#braindecode.augmentation.functional.sensors_rotation">sensors_rotation() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.sensors_rotation">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.SensorsRotation.html#braindecode.augmentation.SensorsRotation">SensorsRotation (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsRotation">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.SensorsXRotation.html#braindecode.augmentation.SensorsXRotation">SensorsXRotation (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsXRotation">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsXRotation">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.SensorsYRotation.html#braindecode.augmentation.SensorsYRotation">SensorsYRotation (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsYRotation">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsYRotation">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.SensorsZRotation.html#braindecode.augmentation.SensorsZRotation">SensorsZRotation (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SensorsZRotation">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsZRotation">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler">SequenceSampler (class in braindecode.samplers)</a>, <a href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.samplers.html#braindecode.samplers.base.SequenceSampler">(class in braindecode.samplers.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.set_description">set_description() (braindecode.datasets.base.BaseConcatDataset method)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseDataset.set_description">(braindecode.datasets.base.BaseDataset method)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.set_description">(braindecode.datasets.base.EEGWindowsDataset method)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.set_description">(braindecode.datasets.base.WindowsDataset method)</a>
+</li>
+        <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.set_description">(braindecode.datasets.BaseConcatDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.set_description">[1]</a>
+</li>
+        <li><a href="generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset.set_description">(braindecode.datasets.BaseDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseDataset.set_description">[1]</a>
+</li>
+        <li><a href="generated/braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset.set_description">(braindecode.datasets.WindowsDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.WindowsDataset.set_description">[1]</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds">set_random_seeds() (in module braindecode.util)</a>, <a href="source/braindecode.html#braindecode.util.set_random_seeds">[1]</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference">SetEEGReference (class in braindecode.preprocessing)</a>, <a href="source/braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.SetEEGReference">(class in braindecode.preprocessing.mne_preprocess)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.sfreq">sfreq (braindecode.models.base.EEGModuleMixin property)</a>
+</li>
+      <li><a href="generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet">ShallowFBCSPNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.shallow_fbcsp.ShallowFBCSPNet">(class in braindecode.models.shallow_fbcsp)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.sign_flip.html#braindecode.augmentation.functional.sign_flip">sign_flip() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.sign_flip">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip">SignFlip (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SignFlip">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet">SleepPhysionet (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet">(class in braindecode.datasets.sleep_physionet)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020">SleepStagerBlanco2020 (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020">(class in braindecode.models.sleep_stager_blanco_2020)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018">SleepStagerChambon2018 (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018">(class in braindecode.models.sleep_stager_chambon_2018)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021">SleepStagerEldele2021 (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021">(class in braindecode.models.sleep_stager_eldele_2021)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.smooth_time_mask.html#braindecode.augmentation.functional.smooth_time_mask">smooth_time_mask() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.smooth_time_mask">[1]</a>
+</li>
+      <li><a href="generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask">SmoothTimeMask (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.split">split() (braindecode.datasets.base.BaseConcatDataset method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split">(braindecode.datasets.BaseConcatDataset method)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.split">[1]</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.functions.square">square() (in module braindecode.models.functions)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.functions.squeeze_final_output">squeeze_final_output() (in module braindecode.models.functions)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.stride">stride (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.stride">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.target_transform">target_transform (braindecode.datasets.base.BaseConcatDataset property)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.target_transform">(braindecode.datasets.BaseConcatDataset property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.models.TCN.html#braindecode.models.TCN">TCN (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.TCN">(class in braindecode.models.tcn)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.tcn.TemporalBlock">TemporalBlock (class in braindecode.models.tcn)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.th_to_np">th_to_np() (in module braindecode.util)</a>
+</li>
+      <li><a href="source/braindecode.html#braindecode.util.ThrowAwayIndexLoader">ThrowAwayIndexLoader (class in braindecode.util)</a>
+</li>
+      <li><a href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet">TIDNet (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.tidnet.TIDNet">(class in braindecode.models.tidnet)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.functional.time_reverse.html#braindecode.augmentation.functional.time_reverse">time_reverse() (in module braindecode.augmentation.functional)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.functional.time_reverse">[1]</a>
+</li>
+      <li><a href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed">TimeDistributed (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.TimeDistributed">(class in braindecode.models.modules)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.timeout">timeout (braindecode.augmentation.AugmentedDataLoader attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.timeout">(braindecode.augmentation.base.AugmentedDataLoader attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.TimeReverse.html#braindecode.augmentation.TimeReverse">TimeReverse (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.TimeReverse">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse">(class in braindecode.augmentation.transforms)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss">TimeSeriesLoss (class in braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.TimeSeriesLoss">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.TimeSeriesLoss">(class in braindecode.training.losses)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin.to_dense_prediction_model">to_dense_prediction_model() (braindecode.models.base.EEGModuleMixin method)</a>
+
+      <ul>
+        <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.to_dense_prediction_model">(braindecode.models.EEGModuleMixin method)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.util.to_dense_prediction_model">(in module braindecode.models.util)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Compose.training">training (braindecode.augmentation.base.Compose attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform.training">(braindecode.augmentation.base.IdentityTransform attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform.training">(braindecode.augmentation.base.Transform attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.training">(braindecode.augmentation.transforms.BandstopFilter attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.training">(braindecode.augmentation.transforms.ChannelsDropout attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.training">(braindecode.augmentation.transforms.ChannelsShuffle attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.training">(braindecode.augmentation.transforms.ChannelsSymmetry attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.training">(braindecode.augmentation.transforms.FrequencyShift attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.training">(braindecode.augmentation.transforms.FTSurrogate attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.training">(braindecode.augmentation.transforms.GaussianNoise attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.training">(braindecode.augmentation.transforms.Mixup attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.training">(braindecode.augmentation.transforms.SensorsRotation attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsXRotation.training">(braindecode.augmentation.transforms.SensorsXRotation attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsYRotation.training">(braindecode.augmentation.transforms.SensorsYRotation attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsZRotation.training">(braindecode.augmentation.transforms.SensorsZRotation attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip.training">(braindecode.augmentation.transforms.SignFlip attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.training">(braindecode.augmentation.transforms.SmoothTimeMask attribute)</a>
+</li>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse.training">(braindecode.augmentation.transforms.TimeReverse attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.atcnet.ATCNet.training">(braindecode.models.atcnet.ATCNet attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv.training">(braindecode.models.modules.AvgPool2dWithConv attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CombinedConv.training">(braindecode.models.modules.CombinedConv attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.Ensure4d.training">(braindecode.models.modules.Ensure4d attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.Expression.training">(braindecode.models.modules.Expression attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper.training">(braindecode.models.modules.IntermediateOutputWrapper attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.TimeDistributed.training">(braindecode.models.modules.TimeDistributed attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.Chomp1d.training">(braindecode.models.tcn.Chomp1d attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.tcn.TemporalBlock.training">(braindecode.models.tcn.TemporalBlock attribute)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.CroppedLoss.training">(braindecode.training.CroppedLoss attribute)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.CroppedLoss.training">(braindecode.training.losses.CroppedLoss attribute)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.losses.TimeSeriesLoss.training">(braindecode.training.losses.TimeSeriesLoss attribute)</a>
+</li>
+        <li><a href="source/braindecode.training.html#braindecode.training.TimeSeriesLoss.training">(braindecode.training.TimeSeriesLoss attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.transform">transform (braindecode.datasets.base.BaseConcatDataset property)</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.BaseDataset.transform">(braindecode.datasets.base.BaseDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.transform">(braindecode.datasets.base.EEGWindowsDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.transform">(braindecode.datasets.base.WindowsDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.transform">(braindecode.datasets.BaseConcatDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.BaseDataset.transform">(braindecode.datasets.BaseDataset property)</a>
+</li>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.WindowsDataset.transform">(braindecode.datasets.WindowsDataset property)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform">Transform (class in braindecode.augmentation)</a>, <a href="source/braindecode.augmentation.html#braindecode.augmentation.Transform">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.augmentation.html#braindecode.augmentation.base.Transform">(class in braindecode.augmentation.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.models.html#braindecode.models.functions.transpose_time_to_spat">transpose_time_to_spat() (in module braindecode.models.functions)</a>
+</li>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.transposed">transposed (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.transposed">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.training.trial_preds_from_window_preds.html#braindecode.training.trial_preds_from_window_preds">trial_preds_from_window_preds() (in module braindecode.training)</a>, <a href="source/braindecode.training.html#braindecode.training.trial_preds_from_window_preds">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.training.html#braindecode.training.scoring.trial_preds_from_window_preds">(in module braindecode.training.scoring)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH">TUH (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.TUH">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUH">(class in braindecode.datasets.tuh)</a>
+</li>
+      </ul></li>
+      <li><a href="generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal">TUHAbnormal (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.TUHAbnormal">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal">(class in braindecode.datasets.tuh)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.html#braindecode.util.update_estimator_docstring">update_estimator_docstring() (in module braindecode.util)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.USleep.html#braindecode.models.USleep">USleep (class in braindecode.models)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.usleep.USleep">(class in braindecode.models.usleep)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.html#braindecode.util.var_to_np">var_to_np() (in module braindecode.util)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.weight">weight (braindecode.models.eegnet.Conv2dWithConstraint attribute)</a>
+
+      <ul>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.CausalConv1d.weight">(braindecode.models.modules.CausalConv1d attribute)</a>
+</li>
+        <li><a href="source/braindecode.models.html#braindecode.models.modules.MaxNormLinear.weight">(braindecode.models.modules.MaxNormLinear attribute)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset">WindowsDataset (class in braindecode.datasets)</a>, <a href="source/braindecode.datasets.html#braindecode.datasets.WindowsDataset">[1]</a>
+
+      <ul>
+        <li><a href="source/braindecode.datasets.html#braindecode.datasets.base.WindowsDataset">(class in braindecode.datasets.base)</a>
+</li>
+      </ul></li>
+      <li><a href="source/braindecode.html#braindecode.util.wrap_reshape_apply_fn">wrap_reshape_apply_fn() (in module braindecode.util)</a>
+</li>
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diff --git a/0.8/help.html b/0.8/help.html
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+<span id="faq"></span><h1>Frequently Asked Questions (FAQ)<a class="headerlink" href="#frequently-asked-questions-faq" title="Link to this heading">#</a></h1>
+<section id="how-do-i-cite-braindecode">
+<h2>How do I cite Braindecode?<a class="headerlink" href="#how-do-i-cite-braindecode" title="Link to this heading">#</a></h2>
+<p>See  <a class="reference internal" href="cite.html#cite"><span class="std std-ref">How to cite Braindecode</span></a>.</p>
+</section>
+<section id="help-i-can-t-get-python-and-braindecode-working">
+<h2>Help! I can’t get Python and Braindecode working!<a class="headerlink" href="#help-i-can-t-get-python-and-braindecode-working" title="Link to this heading">#</a></h2>
+<p>Check out if your are running the most recent version of braindecode.</p>
+</section>
+<section id="i-still-can-t-get-it-to-work">
+<h2>I still can’t get it to work!<a class="headerlink" href="#i-still-can-t-get-it-to-work" title="Link to this heading">#</a></h2>
+<p>When you encounter an error message or unexpected results, it can be hard to
+tell whether it happened because of a bug in Braindecode, a mistake in user
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+<p>Your first step when asking for help should be the <a class="reference external" href="https://gitter.im/braindecodechat/community">Braindecode Chat</a>, not GitHub. This bears
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+open an issue that contains only a usage question, we will close the issue and
+direct you to the chat.</p>
+</section>
+<section id="i-think-i-found-a-bug-what-do-i-do">
+<h2>I think I found a bug, what do I do?<a class="headerlink" href="#i-think-i-found-a-bug-what-do-i-do" title="Link to this heading">#</a></h2>
+<p>If you’re pretty sure the problem you’ve encountered
+is a software bug (not bad data or user error):</p>
+<ul>
+<li><p>Make sure you’re using <a class="reference external" href="https://github.com/mne-tools/mne-python/releases/latest">the most current version</a>. You can check it locally
+at a shell prompt with:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>python<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import braindecode; print(braindecode.__version__)&quot;</span>
+</pre></div>
+</div>
+<p>which will also give you version info about braindecode.</p>
+</li>
+<li><p>If you’re already on the most current version, if possible try using
+<a class="reference external" href="https://mne.tools/stable/install/updating.html#installing-main" title="(in MNE v1.5)"><span class="xref std std-ref">the latest development version</span></a>, as the bug may
+have been fixed already since the latest release. If you can’t try the latest
+development version, search the GitHub issues page to see if the problem has
+already been reported and/or fixed.</p></li>
+<li><p>Try to replicate the problem with one of the <a class="reference external" href="https://skorch.readthedocs.io/en/stable/user/neuralnet.html#datasets" title="(in skorch v0.15)"><span class="xref std std-ref">MNE sample datasets</span></a>. If you can’t replicate it with a built-in dataset, provide a
+link to a small, anonymized portion of your data that does yield the error.</p></li>
+</ul>
+<p>If the problem persists, <a class="reference external" href="https://github.com/braindecode/braindecode/issues/new">open a new issue</a>
+and include the <em>smallest possible</em> code sample that replicates the error
+you’re seeing. Paste the code sample into the issue, with a line containing
+three backticks (```) above and below the lines of code. This
+<a class="reference external" href="https://en.wikipedia.org/wiki/Minimal_Working_Example">minimal working example</a> should be self-contained, which means that
+Braindecode contributors should be able to copy and paste the provided snippet
+and replicate the bug on their own computers.</p>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
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+</html>
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diff --git a/0.8/install/install.html b/0.8/install/install.html
new file mode 100644
index 00000000..926e45bd
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+              
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+<div>
+  
+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h1>
+<p>Braindecode is written in Python 3, specifically for version 3.8 or above.</p>
+<p>The package is distributed via Python package index (<a class="reference external" href="https://pypi.org/project/braincode">PyPI</a>), and you can access the
+source code via <a class="reference external" href="https://github.com/braincode/braindecode">Github</a> repository.</p>
+<p>There are different ways to install Braindecode, depending on your needs and:</p>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
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+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-text-center docutils">
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+<p class="font-weight-bold mb-0 sd-card-text">Building from source code</p>
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+</div>
+<div class="sd-card-body docutils">
+<img alt="Terminal Window" src="https://mne.tools/stable/_images/mne_installer_console.png" />
+<p class="sd-card-text"><strong>Already familiar with Python?</strong>
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diff --git a/0.8/install/install_pip.html b/0.8/install/install_pip.html
new file mode 100644
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--- /dev/null
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+              
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+  
+  <section id="installing-from-pypi">
+<span id="install-pip"></span><h1>Installing from PyPI<a class="headerlink" href="#installing-from-pypi" title="Link to this heading">#</a></h1>
+<p>Braincode can be installed via pip from <a class="reference external" href="https://pypi.org/project/braindecode">PyPI</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We recommend the most updated version of pip to install from PyPI.</p>
+</div>
+<p>Below are the installation commands for the most common use cases.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">pip install braindecode</span>
+</pre></div>
+</div>
+<p>Braindecode can also be installed along MOABB to download open datasets:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>moabb
+</pre></div>
+</div>
+<p>To use the potential of the deep learning modules PyTorch with GPU, we recommend the following sequence before installing the braindecode:</p>
+<ol class="arabic simple">
+<li><p>Install the latest NVIDIA driver.</p></li>
+<li><p>Check <a class="reference external" href="https://pytorch.org/get-started/locally/">PyTorch Official Guide</a>, for the recommended CUDA versions. For the Pip package, the user must download the CUDA manually, install it on the system, and ensure CUDA_PATH is appropriately set and working!</p></li>
+<li><p>Continue to follow the guide and install PyTorch.</p></li>
+</ol>
+<p>See <a class="reference external" href="braindecode.faq">Troubleshooting</a> section if you have a problem.</p>
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diff --git a/0.8/install/install_source.html b/0.8/install/install_source.html
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+      
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+  
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+</div>
+              
+              
+<div>
+  
+  <section id="installing-from-sources">
+<span id="install-source"></span><h1>Installing from sources<a class="headerlink" href="#installing-from-sources" title="Link to this heading">#</a></h1>
+<p>If you want to test features under development or contribute to the library, or if you want to test the new tools that have been tested in braindecode and not released yet, this is the right tutorial for you!</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are only trying to install Braindecode, we recommend using the pip installation <a class="reference external" href="https://braindecode.org/braindecode/install/install_pip.html#install-pip">Installation</a> for details on that.</p>
+</div>
+<section id="clone-the-repository-from-github">
+<span id="system-level"></span><h2>Clone the repository from GitHub<a class="headerlink" href="#clone-the-repository-from-github" title="Link to this heading">#</a></h2>
+<p>The first thing you should do is clone the Braindecode repository to your computer and enter inside the repository.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/braindecode/braindecode<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>braindecode
+</pre></div>
+</div>
+<p>You should now be in the root directory of the Braindecode repository.</p>
+</section>
+<section id="installing-braindecode-from-the-source">
+<h2>Installing Braindecode from the source<a class="headerlink" href="#installing-braindecode-from-the-source" title="Link to this heading">#</a></h2>
+<p>If you want to only install Braindecode from source once and not do any development
+work, then the recommended way to build and install is to use <code class="docutils literal notranslate"><span class="pre">pip</span></code>:</p>
+<p>For the latest development version, directly from GitHub:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-U<span class="w"> </span>https://api.github.com/repos/braindecode/braindecode/zipball/master#egg<span class="o">=</span>braindecode
+</pre></div>
+</div>
+<p>If you have a local clone of the Braindecode git repository:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
+</pre></div>
+</div>
+<p>This will install Braindecode in editable mode, i.e., changes to the source code could be used
+directly in python.</p>
+<p>You could also install optional dependency, like to import datasets from MOABB.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="o">[</span>moabb<span class="o">]</span>
+</pre></div>
+</div>
+<p>There is also optional dependencies for unit testing and building documentation, you could install
+them if you want to contribute to Braindecode.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="o">[</span>moabb,tests,docs<span class="o">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="testing-if-your-installation-is-working">
+<h1>Testing if your installation is working<a class="headerlink" href="#testing-if-your-installation-is-working" title="Link to this heading">#</a></h1>
+<p>To verify that Braindecode is installed and running correctly, run the following command:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python -m &quot;import braindecode; braindecode.__version__&quot;</span>
+</pre></div>
+</div>
+<p>For more information, please see the <a class="reference external" href="https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md">contributors’ guidelines</a>.</p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#installing-braindecode-from-the-source">Installing Braindecode from the source</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#testing-if-your-installation-is-working">Testing if your installation is working</a></li>
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+<script src="../_static/scripts/pydata-sphinx-theme.js?digest=e353d410970836974a52"></script>
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\ No newline at end of file
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+  
+
+   <h1>Python Module Index</h1>
+
+   <div class="modindex-jumpbox">
+   <a href="#cap-b"><strong>b</strong></a>
+   </div>
+
+   <table class="indextable modindextable">
+     <tr class="pcap"><td></td><td>&#160;</td><td></td></tr>
+     <tr class="cap" id="cap-b"><td></td><td>
+       <strong>b</strong></td><td></td></tr>
+     <tr>
+       <td><img src="_static/minus.png" class="toggler"
+              id="toggle-1" style="display: none" alt="-" /></td>
+       <td>
+       <a href="source/braindecode.html#module-braindecode"><code class="xref">braindecode</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.augmentation.html#module-braindecode.augmentation"><code class="xref">braindecode.augmentation</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.augmentation.html#module-braindecode.augmentation.base"><code class="xref">braindecode.augmentation.base</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.augmentation.html#module-braindecode.augmentation.functional"><code class="xref">braindecode.augmentation.functional</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.augmentation.html#module-braindecode.augmentation.transforms"><code class="xref">braindecode.augmentation.transforms</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.html#module-braindecode.classifier"><code class="xref">braindecode.classifier</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.datasets.html#module-braindecode.datasets"><code class="xref">braindecode.datasets</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.datasets.html#module-braindecode.datasets.base"><code class="xref">braindecode.datasets.base</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.datasets.html#module-braindecode.datasets.bbci"><code class="xref">braindecode.datasets.bbci</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.datasets.html#module-braindecode.datasets.bcicomp"><code class="xref">braindecode.datasets.bcicomp</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="source/braindecode.datasets.html#module-braindecode.datasets.mne"><code class="xref">braindecode.datasets.mne</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
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140, 141, 143, 146], "fly": [2, 8, 28, 30, 71, 77, 143, 151], "transform": [2, 5, 8, 9, 10, 11, 20, 27, 28, 29, 30, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 54, 55, 56, 57, 58, 59, 60, 61, 64, 65, 66, 67, 71, 77, 78, 79, 80, 86, 91, 120, 141, 143, 145, 150, 151], "pytorch": [2, 8, 14, 22, 24, 25, 26, 27, 28, 30, 33, 67, 70, 73, 83, 84, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 100, 101, 103, 109, 113, 116, 118, 120, 131, 133, 139, 141, 145, 148, 151], "torchvis": [2, 8, 28, 30], "exponential_moving_standard": [2, 3, 4, 8, 27, 28, 29, 30, 32, 141, 146, 150], "preprocessor": [2, 3, 4, 5, 8, 9, 10, 11, 12, 16, 18, 20, 27, 28, 29, 30, 32, 118, 141, 146, 150, 151], "low_cut_hz": [2, 3, 4, 8, 27, 28, 29, 30, 32], "low": [2, 3, 4, 5, 8, 9, 10, 11, 16, 27, 28, 29, 30, 32, 36, 54, 88, 89, 107, 142, 145, 146], "high_cut_hz": [2, 3, 4, 5, 8, 9, 10, 27, 28, 29, 30, 32], "200": [2, 8, 10, 11, 14, 84, 145], "high": [2, 3, 4, 8, 11, 27, 28, 29, 30, 32, 36, 54, 72, 88, 89, 103, 107, 121, 142, 143, 145, 146, 147], "higher": [2, 5, 8, 32, 99, 145], "paramet": [2, 3, 4, 5, 8, 10, 14, 16, 17, 18, 19, 26, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 128, 129, 130, 131, 132, 133, 135, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151], "exponenti": [2, 3, 4, 8, 27, 28, 29, 30, 32, 103, 115, 116, 145, 146], "move": [2, 3, 4, 8, 14, 16, 27, 28, 29, 30, 32, 68, 91, 115, 116, 143, 145, 146, 151], "factor_new": [2, 3, 4, 8, 27, 28, 29, 30, 32, 115, 116, 146], "1e": [2, 3, 4, 8, 9, 10, 11, 27, 28, 29, 30, 32, 145], "init_block_s": [2, 3, 4, 8, 27, 28, 29, 30, 32, 115, 116, 146], "1000": [2, 3, 4, 8, 12, 22, 26, 27, 28, 29, 30, 32, 72, 88, 89, 122, 143, 145, 147], "30": [2, 5, 8, 9, 10, 11, 16, 22, 27, 31, 32, 74, 96, 97, 98, 99, 103, 143, 145], "second": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 26, 27, 28, 30, 31, 32, 39, 69, 72, 75, 76, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 103, 105, 142, 143, 145, 146], "limit": [2, 5, 8, 11, 12, 16, 26, 105, 146], "memori": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 26, 27, 28, 29, 30, 31, 32, 113, 146], "run": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 26, 27, 28, 29, 30, 31, 32, 67, 69, 72, 80, 83, 86, 90, 97, 107, 109, 110, 113, 120, 122, 133, 136, 140, 141, 143, 145, 146, 147, 151], "6": [2, 3, 4, 5, 9, 10, 11, 12, 16, 17, 18, 19, 20, 21, 26, 27, 28, 29, 30, 31, 32, 33, 69, 72, 86, 107, 143, 145, 146], "To": [2, 3, 4, 5, 8, 9, 10, 11, 14, 16, 17, 22, 26, 27, 28, 29, 30, 31, 32, 107, 139, 140, 145, 146], "obtain": [2, 3, 4, 5, 8, 9, 11, 27, 28, 46, 63, 67, 71, 72, 77, 120, 122, 141, 142, 143, 145, 147], "whole": [2, 8, 9, 10], "should": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 20, 26, 27, 28, 30, 36, 39, 46, 51, 54, 63, 65, 67, 68, 70, 71, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 103, 105, 107, 108, 110, 111, 120, 128, 136, 140, 141, 142, 143, 145, 146, 148], "valid_set": [2, 3, 8, 9, 10, 11, 27, 28, 31], "deepcopi": 2, "tmin": [2, 8, 12, 16, 19, 105, 146], "24": [2, 5, 10, 11, 12, 27, 32, 33, 97, 101, 108, 145, 146], "tmax": [2, 8, 12, 105, 146], "n_job": [2, 3, 4, 5, 12, 14, 16, 27, 28, 29, 30, 32, 75, 76, 81, 107, 110, 112, 113, 114, 118, 143, 144, 146, 151], "0": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 41, 43, 44, 45, 53, 55, 57, 59, 62, 67, 69, 70, 74, 75, 76, 80, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 101, 103, 105, 106, 107, 108, 110, 111, 112, 113, 115, 116, 120, 122, 131, 135, 141, 142, 143, 145, 146, 147, 148, 149], 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3, 4, 5, 8, 11, 14, 16, 21, 26, 27, 28, 29, 113, 146], "mani": [2, 4, 5, 8, 16, 26, 27, 29, 32, 70, 143], "ignor": [2, 4, 8, 51, 67, 83, 86, 90, 106, 109, 112, 113, 120, 123, 141, 142, 145, 146, 147], "perform": [2, 4, 8, 9, 10, 11, 26, 27, 28, 29, 30, 32, 69, 70, 72, 83, 86, 90, 99, 115, 116, 143, 145, 146, 151], "oper": [2, 3, 4, 8, 16, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 61, 105, 110, 141, 142, 145, 146], "see": [2, 3, 4, 5, 8, 9, 10, 11, 16, 20, 21, 24, 26, 27, 28, 29, 30, 32, 45, 62, 67, 68, 69, 72, 74, 76, 77, 87, 91, 92, 93, 95, 100, 101, 105, 106, 107, 108, 110, 111, 112, 113, 118, 120, 121, 123, 124, 130, 133, 136, 139, 140, 141, 142, 143, 145, 146, 147, 148], "tool": [2, 4, 8, 74, 137, 140, 143], "stabl": [2, 4, 5, 8, 16, 26, 67, 74, 83, 84, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 100, 101, 103, 120, 133, 135, 141, 143, 145, 149], "glossari": [2, 4, 8], "html": [2, 4, 5, 8, 12, 26, 67, 74, 75, 76, 83, 84, 86, 87, 88, 89, 90, 91, 92, 94, 95, 96, 97, 100, 101, 103, 120, 133, 135, 141, 143, 145, 149], "term": [2, 4, 8, 85, 91, 141, 145], "todo": 2, "ensur": [2, 103, 133, 139, 141, 145], "remov": [2, 3, 4, 5, 8, 9, 11, 21, 26, 27, 28, 29, 30, 31, 32, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 106, 111, 132, 145, 146, 148, 151], "pick_typ": [2, 3, 4, 8, 20, 27, 28, 29, 30, 32, 106, 146], "true": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 26, 27, 28, 29, 30, 31, 32, 45, 46, 47, 48, 49, 67, 70, 71, 74, 75, 76, 77, 80, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 105, 109, 111, 112, 113, 114, 117, 120, 123, 124, 126, 127, 128, 131, 133, 141, 142, 143, 145, 146, 147, 148, 151], "lambda": [2, 3, 4, 5, 8, 9, 10, 12, 27, 28, 29, 30, 32, 67, 94, 120, 141, 145], "x": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 20, 22, 26, 27, 28, 29, 30, 31, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 78, 79, 80, 83, 85, 86, 90, 95, 97, 98, 99, 100, 101, 102, 103, 110, 115, 116, 120, 125, 128, 129, 131, 141, 142, 143, 145, 146, 148, 149], "1e6": [2, 3, 4, 5, 8, 9, 10, 12, 27, 28, 29, 30, 32], "pick": [2, 8, 10, 12, 20, 107, 112, 113, 114, 141, 146, 150, 151], "convert": [2, 3, 4, 5, 8, 9, 10, 12, 17, 19, 27, 28, 29, 30, 31, 32, 107, 141, 146], "v": [2, 3, 4, 5, 8, 9, 10, 12, 16, 27, 28, 29, 30, 32, 88, 89, 92, 93, 100, 145], "uv": [2, 3, 4, 5, 8, 9, 10, 12, 27, 28, 29, 30, 32], "l_freq": [2, 3, 4, 5, 8, 9, 10, 16, 27, 28, 29, 30, 32, 107, 146], "h_freq": [2, 3, 4, 5, 8, 9, 10, 16, 27, 28, 29, 30, 32, 107, 146], "extract": [2, 8, 12, 14, 16, 17, 19, 22, 27, 32, 77, 88, 89, 90, 97, 99, 112, 113, 121, 123, 143, 145, 146, 147], "check": [2, 3, 4, 8, 9, 10, 11, 26, 27, 28, 29, 30, 32, 34, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 100, 101, 103, 136, 139, 143, 145, 151], "thei": [2, 3, 4, 8, 14, 16, 26, 27, 28, 29, 30, 32, 111, 146, 151], "same": [2, 3, 4, 5, 8, 9, 10, 11, 14, 18, 21, 27, 28, 29, 30, 32, 42, 51, 58, 65, 67, 72, 74, 99, 120, 123, 141, 142, 143, 145, 147], "all": [2, 3, 4, 8, 12, 14, 16, 24, 26, 27, 28, 29, 30, 32, 42, 43, 51, 58, 60, 65, 67, 68, 69, 72, 73, 74, 83, 86, 88, 89, 90, 91, 105, 107, 108, 110, 111, 112, 113, 120, 128, 141, 142, 143, 145, 146, 148, 151], "sfreq": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 26, 27, 28, 29, 30, 31, 32, 36, 42, 54, 58, 78, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 103, 107, 120, 141, 142, 143, 145, 146, 151], "assert": [2, 3, 4, 8, 12, 14, 27, 28, 29, 30, 32], "d": [2, 3, 4, 5, 8, 12, 14, 22, 26, 27, 28, 29, 30, 31, 32, 37, 38, 41, 55, 57, 59, 83, 84, 93, 95, 96, 97, 101, 123, 135, 142, 145, 147, 149, 151], "target_sfreq": [2, 8], "temp": [2, 8], "contrast": [2, 4, 5, 27, 36, 43, 51, 54, 60, 65, 142], "trialwis": [2, 4, 8, 20, 27, 28, 30, 67, 120, 131, 141, 148, 151], "befor": [2, 11, 16, 27, 28, 29, 30, 32, 67, 70, 71, 74, 77, 83, 84, 85, 86, 97, 98, 99, 115, 116, 120, 128, 133, 139, 141, 143, 145, 146, 148, 151], "becaus": [2, 16, 26, 27, 29, 30, 136], "know": [2, 3, 4, 26, 27], "recept": [2, 27, 67, 120, 131, 141, 148], "field": [2, 27, 67, 111, 120, 131, 141, 146, 148], "network": [2, 4, 5, 7, 8, 10, 13, 14, 25, 26, 27, 28, 29, 32, 33, 34, 43, 60, 67, 70, 71, 74, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 109, 113, 118, 120, 122, 124, 131, 133, 135, 141, 142, 143, 145, 148, 149, 151], "larg": [2, 5, 14, 16, 26, 27, 70, 113, 143, 146], "stride": [2, 9, 10, 11, 26, 27, 78, 79, 80, 84, 86, 91, 96, 101, 112, 113, 141, 143, 145, 146], "choos": [2, 3, 4, 8, 18, 27, 28, 29, 30, 32], "input": [2, 3, 4, 8, 10, 11, 14, 21, 26, 27, 28, 29, 32, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 66, 67, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 120, 141, 142, 145, 151], "size": [2, 3, 4, 5, 11, 14, 16, 26, 27, 28, 29, 30, 31, 32, 36, 51, 54, 65, 67, 78, 79, 80, 83, 86, 88, 89, 90, 91, 97, 98, 100, 101, 103, 104, 107, 110, 112, 113, 120, 123, 125, 129, 131, 135, 141, 142, 143, 145, 146, 147, 148, 149], "fed": [2, 27, 47, 48, 49, 142], "dure": [2, 4, 5, 8, 9, 10, 11, 12, 14, 27, 28, 29, 30, 32, 67, 68, 107, 111, 120, 141, 143, 145, 146, 151], "ha": [2, 3, 4, 5, 8, 10, 12, 20, 21, 22, 27, 28, 29, 30, 31, 32, 47, 48, 49, 67, 70, 71, 74, 77, 80, 87, 88, 89, 90, 92, 93, 103, 107, 113, 120, 130, 136, 141, 142, 143, 145, 146, 148, 151], "larger": [2, 5, 27, 88, 89, 145], "otherwis": [2, 14, 27, 29, 31, 80, 113, 143, 146], "chosen": [2, 16, 18, 27, 32, 51, 67, 107, 120, 131, 141, 142, 146, 148], "effici": [2, 3, 16, 27], "explan": [2, 27, 117, 118, 146], "begin": [2, 27, 29, 30, 69, 97, 107, 112, 143, 145, 146], "which": [2, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 20, 21, 24, 26, 27, 29, 30, 32, 34, 35, 38, 41, 46, 57, 59, 63, 67, 70, 71, 72, 77, 91, 99, 102, 107, 110, 111, 120, 122, 123, 124, 136, 141, 142, 143, 145, 146, 147], "rate": [2, 5, 14, 27, 28, 29, 30, 31, 32, 67, 72, 86, 88, 89, 97, 98, 99, 103, 120, 141, 143, 144, 145, 151], "input_window_sampl": [2, 3, 4, 8, 14, 26, 27, 28, 29, 30, 31, 32, 84, 86, 87, 88, 89, 92, 93, 94, 95, 96, 101, 104, 145], "come": [2, 8, 14, 28, 30, 32, 124, 133, 141, 147], "some": [2, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 22, 26, 27, 28, 30, 34, 67, 70, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 111, 120, 133, 141, 143, 145, 146], "predefin": [2, 8, 28, 30, 145], "convolut": [2, 5, 8, 9, 11, 24, 25, 26, 27, 28, 29, 30, 33, 34, 41, 43, 59, 60, 71, 83, 84, 85, 86, 87, 88, 89, 90, 92, 93, 95, 96, 97, 98, 99, 100, 103, 135, 142, 143, 145, 149, 151], "neural": [2, 4, 5, 8, 9, 10, 11, 14, 25, 26, 27, 28, 29, 33, 34, 43, 60, 71, 84, 85, 86, 88, 89, 90, 91, 95, 96, 97, 98, 99, 101, 104, 135, 142, 143, 145, 149, 151], "architectur": [2, 5, 8, 9, 10, 11, 26, 27, 28, 30, 67, 85, 86, 97, 98, 99, 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[[2, "loading"], [3, "loading"], [4, "loading"], [8, "loading"], [9, "loading"], [10, "loading"], [11, "loading"], [30, "loading"]], "Split dataset into train and test": [[2, "split-dataset-into-train-and-test"], [29, "split-dataset-into-train-and-test"], [32, "split-dataset-into-train-and-test"]], "Create model": [[2, "create-model"], [3, "create-model"], [4, "create-model"], [8, "create-model"], [9, "create-model"], [10, "create-model"], [11, "create-model"], [29, "create-model"], [30, "create-model"]], "Cut Compute Windows": [[2, "cut-compute-windows"], [8, "cut-compute-windows"], [12, "cut-compute-windows"], [32, "cut-compute-windows"]], "Plot Results": [[2, "plot-results"], [8, "plot-results"], [27, "plot-results"]], "Data Augmentation on BCIC IV 2a Dataset": [[3, "data-augmentation-on-bcic-iv-2a-dataset"]], "This example covers:": [[3, "this-example-covers"], [4, "this-example-covers"], [5, "this-example-covers"], [12, "this-example-covers"], [21, "this-example-covers"], [26, "this-example-covers"], [27, "this-example-covers"], [28, "this-example-covers"], [29, "this-example-covers"], [30, "this-example-covers"], [32, "this-example-covers"]], "Loading and preprocessing the dataset": [[3, "loading-and-preprocessing-the-dataset"], [4, "loading-and-preprocessing-the-dataset"], [5, "loading-and-preprocessing-the-dataset"], [9, "loading-and-preprocessing-the-dataset"], [10, "loading-and-preprocessing-the-dataset"], [11, "loading-and-preprocessing-the-dataset"], [27, "loading-and-preprocessing-the-dataset"], [30, "loading-and-preprocessing-the-dataset"]], "Extracting windows": [[3, "extracting-windows"], [4, "extracting-windows"], [5, "extracting-windows"]], "Split dataset into train and valid": [[3, "split-dataset-into-train-and-valid"], [4, "split-dataset-into-train-and-valid"], [9, "split-dataset-into-train-and-valid"], [10, "split-dataset-into-train-and-valid"], [11, "split-dataset-into-train-and-valid"], [30, "split-dataset-into-train-and-valid"]], "Defining a Transform": [[3, "defining-a-transform"]], "Manipulating one session and visualizing the transformed data": [[3, "manipulating-one-session-and-visualizing-the-transformed-data"]], "Training a model with data augmentation": [[3, "training-a-model-with-data-augmentation"], [4, "training-a-model-with-data-augmentation"]], "Create an EEGClassifier with the desired augmentation": [[3, "create-an-eegclassifier-with-the-desired-augmentation"], [4, "create-an-eegclassifier-with-the-desired-augmentation"]], "Manually composing Transforms": [[3, "manually-composing-transforms"]], "Setting the data augmentation at the Dataset level": [[3, "setting-the-data-augmentation-at-the-dataset-level"]], "Searching the best data augmentation on BCIC IV 2a Dataset": [[4, "searching-the-best-data-augmentation-on-bcic-iv-2a-dataset"]], "Data Augmentation": [[4, null]], "Defining a list of transforms": [[4, "defining-a-list-of-transforms"]], "Analysing the best fit": [[4, "analysing-the-best-fit"]], "Plot results": [[4, "plot-results"], [9, "plot-results"], [10, "plot-results"], [11, "plot-results"]], "References": [[4, "references"], [5, "references"], [9, "references"], [10, "references"], [11, "references"], [26, "references"], [27, "references"], [28, "references"], [29, "references"], [30, "references"], [31, "references"], [136, "references"]], "Self-supervised learning on EEG with relative positioning": [[5, "self-supervised-learning-on-eeg-with-relative-positioning"]], "Self-supervised learning": [[5, null]], "Loading the raw recordings": [[5, "loading-the-raw-recordings"]], "Preprocessing windows": [[5, "preprocessing-windows"]], "Splitting dataset into train, valid and test sets": [[5, "splitting-dataset-into-train-valid-and-test-sets"]], "Creating samplers": [[5, "creating-samplers"]], "Creating the model": [[5, "creating-the-model"]], "Visualizing the results": [[5, "visualizing-the-results"]], "Inspecting pretext task performance": [[5, "inspecting-pretext-task-performance"]], "Using the learned representation for sleep staging": [[5, "using-the-learned-representation-for-sleep-staging"]], "Conclusion": [[5, "conclusion"]], "Computation times": [[6, "computation-times"], [13, "computation-times"], [23, "computation-times"], [33, "computation-times"]], "Applied examples on real-world datasets": [[7, "applied-examples-on-real-world-datasets"], [24, "applied-examples-on-real-world-datasets"]], "Fingers flexion decoding on BCIC IV 4 ECoG Dataset": [[8, "fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset"]], "Split dataset into train, valid, and test": [[8, "split-dataset-into-train-valid-and-test"]], "Sleep staging on the Sleep Physionet dataset using Chambon2018 network": [[9, "sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network"]], "Extract windows": [[9, "extract-windows"], [10, "extract-windows"], [11, "extract-windows"]], "Window preprocessing": [[9, "window-preprocessing"], [10, 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parallelization and serialization": [[16, "benchmarking-preprocessing-with-parallelization-and-serialization"]], "Custom Dataset Example": [[17, "custom-dataset-example"]], "Load and save dataset example": [[18, "load-and-save-dataset-example"]], "MNE Dataset Example": [[19, "mne-dataset-example"]], "MOABB Dataset Example": [[20, "moabb-dataset-example"]], "Split Dataset Example": [[21, "split-dataset-example"]], "Loading the dataset": [[21, "loading-the-dataset"], [28, "loading-the-dataset"]], "Splitting": [[21, "splitting"]], "By description information": [[21, "by-description-information"]], "By row index": [[21, "by-row-index"]], "Observation": [[21, "observation"]], "Multiple discrete targets with the TUH EEG Corpus": [[22, "multiple-discrete-targets-with-the-tuh-eeg-corpus"]], "Creating Temple University Hospital (TUH) EEG Corpus Dataset": [[22, "creating-temple-university-hospital-tuh-eeg-corpus-dataset"]], "Exploring Data": [[22, "exploring-data"]], "Creating Windows": [[22, "creating-windows"]], "Exploring Windows": [[22, "exploring-windows"]], "DataLoader for Model Training": [[22, "dataloader-for-model-training"]], "Exploring DataLoader": [[22, "exploring-dataloader"]], "Examples": [[24, "examples"]], "Basic model building and training": [[24, "basic-model-building-and-training"], [25, "basic-model-building-and-training"]], "Simple training on MNE epochs": [[26, "simple-training-on-mne-epochs"]], "Finding the model you want": [[26, "finding-the-model-you-want"]], "Exploring the braindecode online documentation": [[26, "exploring-the-braindecode-online-documentation"]], "Examining the model": [[26, "examining-the-model"]], "Loading your own data with MNE": [[26, "loading-your-own-data-with-mne"]], "Training your model (scikit-learn compatible)": [[26, "training-your-model-scikit-learn-compatible"]], "Cropped Decoding on BCIC IV 2a Dataset": [[27, "cropped-decoding-on-bcic-iv-2a-dataset"]], "Create model and compute windowing parameters": [[27, "create-model-and-compute-windowing-parameters"]], "Cut the data into windows": [[27, "cut-the-data-into-windows"]], "Split the dataset": [[27, "split-the-dataset"]], "Plot Confusion Matrix": [[27, "plot-confusion-matrix"]], "Basic Brain Decoding on EEG Data": [[28, "basic-brain-decoding-on-eeg-data"]], "Loading and preparing the data": [[28, "loading-and-preparing-the-data"]], "Extracting Compute Windows": [[28, "extracting-compute-windows"]], "Splitting the dataset into training and validation sets": [[28, "splitting-the-dataset-into-training-and-validation-sets"]], "Creating a model": [[28, "creating-a-model"]], "Model Training": [[28, "model-training"]], "Plotting Results": [[28, "plotting-results"]], "Plotting a  Confusion Matrix": [[28, "plotting-a-confusion-matrix"]], "How to train, test and tune your model?": [[29, "how-to-train-test-and-tune-your-model"]], "Why should I care about model evaluation?": [[29, "why-should-i-care-about-model-evaluation"], [32, "why-should-i-care-about-model-evaluation"]], "Loading and preprocessing of data, defining a model, etc.": [[29, "loading-and-preprocessing-of-data-defining-a-model-etc"]], "Loading data": [[29, "loading-data"]], "Preprocessing data": [[29, "preprocessing-data"]], "Extraction of the Windows": [[29, "extraction-of-the-windows"], [30, "extraction-of-the-windows"]], "How to train and evaluate your model": [[29, "how-to-train-and-evaluate-your-model"], [32, "how-to-train-and-evaluate-your-model"]], "Option 1: Simple Train-Test Split": [[29, "option-1-simple-train-test-split"]], "Let\u2019s visualize the first option with a util function.": [[29, "let-s-visualize-the-first-option-with-a-util-function"]], "Option 2: Train-Val-Test Split": [[29, "option-2-train-val-test-split"]], "Let\u2019s visualize the second option with a util function.": [[29, "let-s-visualize-the-second-option-with-a-util-function"]], "Option 3: k-Fold Cross Validation": [[29, "option-3-k-fold-cross-validation"]], "Let\u2019s visualize the third option with a util function.": [[29, "let-s-visualize-the-third-option-with-a-util-function"]], "How to tune your hyperparameters": [[29, "how-to-tune-your-hyperparameters"]], "Option 1: Train-Val-Test Split": [[29, "option-1-train-val-test-split"]], "Option 2: k-Fold Cross Validation": [[29, "option-2-k-fold-cross-validation"]], "Hyperparameter tuning with scikit-learn": [[30, "hyperparameter-tuning-with-scikit-learn"]], "Why do you need to tune the neural networks model?": [[30, null]], "Extraction of the Compute Windows": [[30, "extraction-of-the-compute-windows"]], "Plotting the results": [[30, "plotting-the-results"]], "Get the best hyperparameters": [[30, "get-the-best-hyperparameters"]], "Convolutional neural network regression model on fake data.": [[31, "convolutional-neural-network-regression-model-on-fake-data"]], "Fake regression data": [[31, "fake-regression-data"]], "Generating fake regression dataset": [[31, 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"braindecode.augmentation.functional.mixup": [[62, "braindecode-augmentation-functional-mixup"]], "braindecode.augmentation.functional.sensors_rotation": [[63, "braindecode-augmentation-functional-sensors-rotation"]], "braindecode.augmentation.functional.sign_flip": [[64, "braindecode-augmentation-functional-sign-flip"]], "braindecode.augmentation.functional.smooth_time_mask": [[65, "braindecode-augmentation-functional-smooth-time-mask"]], "braindecode.augmentation.functional.time_reverse": [[66, "braindecode-augmentation-functional-time-reverse"]], "braindecode.classifier.EEGClassifier": [[67, "braindecode-classifier-eegclassifier"]], "braindecode.datasets.BCICompetitionIVDataset4": [[68, "braindecode-datasets-bcicompetitionivdataset4"]], "Examples using braindecode.datasets.BCICompetitionIVDataset4": [[68, "examples-using-braindecode-datasets-bcicompetitionivdataset4"]], "braindecode.datasets.BNCI2014001": [[69, "braindecode-datasets-bnci2014001"]], "Dataset summary": [[69, null], 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"braindecode.datasets.TUH": [[75, "braindecode-datasets-tuh"]], "Examples using braindecode.datasets.TUH": [[75, "examples-using-braindecode-datasets-tuh"]], "braindecode.datasets.TUHAbnormal": [[76, "braindecode-datasets-tuhabnormal"]], "Examples using braindecode.datasets.TUHAbnormal": [[76, "examples-using-braindecode-datasets-tuhabnormal"]], "braindecode.datasets.WindowsDataset": [[77, "braindecode-datasets-windowsdataset"]], "braindecode.datasets.create_from_X_y": [[78, "braindecode-datasets-create-from-x-y"]], "Examples using braindecode.datasets.create_from_X_y": [[78, "examples-using-braindecode-datasets-create-from-x-y"]], "braindecode.datasets.create_from_mne_epochs": [[79, "braindecode-datasets-create-from-mne-epochs"]], "Examples using braindecode.datasets.create_from_mne_epochs": [[79, "examples-using-braindecode-datasets-create-from-mne-epochs"]], "braindecode.datasets.create_from_mne_raw": [[80, "braindecode-datasets-create-from-mne-raw"]], "Examples using 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+  <section id="module-braindecode.augmentation">
+<span id="braindecode-augmentation-package"></span><h1>braindecode.augmentation package<a class="headerlink" href="#module-braindecode.augmentation" title="Link to this heading">#</a></h1>
+<p>Utilities for data augmentation.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L186-L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a></p>
+<p>A base dataloader class customized to applying augmentation Transforms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><em>BaseDataset</em></a>) – The dataset containing the signals.</p></li>
+<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference internal" href="#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><em>Transform</em></a><em>, </em><em>optional</em>) – Transform or sequence of Transform to be applied to each batch.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Device on which to transform the data. Defaults to None.</p></li>
+<li><p><strong>**kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – keyword arguments to pass to standard DataLoader class.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.batch_size">
+<span class="sig-name descname"><span class="pre">batch_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.batch_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.dataset">
+<span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.dataset" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.drop_last">
+<span class="sig-name descname"><span class="pre">drop_last</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.drop_last" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.num_workers">
+<span class="sig-name descname"><span class="pre">num_workers</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.num_workers" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.pin_memory">
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.pin_memory" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.pin_memory_device">
+<span class="sig-name descname"><span class="pre">pin_memory_device</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.pin_memory_device" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.prefetch_factor">
+<span class="sig-name descname"><span class="pre">prefetch_factor</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.prefetch_factor" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.sampler">
+<span class="sig-name descname"><span class="pre">sampler</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler" title="(in PyTorch v2.1)"><span class="pre">Sampler</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.sampler" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader.timeout">
+<span class="sig-name descname"><span class="pre">timeout</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader.timeout" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">BandstopFilter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L516-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Apply a band-stop filter with desired bandwidth at a randomly selected
+frequency position between 0 and <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r4ef33a8aa142-1" id="id1">[1]</a> and <a class="reference internal" href="#r4ef33a8aa142-2" id="id2">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</p></li>
+<li><p><strong>max_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Maximal admissible frequency. The low cut frequency will be sampled so
+that the corresponding high cut frequency + transition (=1Hz) are below
+<code class="docutils literal notranslate"><span class="pre">max_freq</span></code>. If omitted or <cite>None</cite>, will default to the Nyquist
+frequency (<code class="docutils literal notranslate"><span class="pre">sfreq</span> <span class="pre">/</span> <span class="pre">2</span></code>).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4ef33a8aa142-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r4ef33a8aa142-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L586-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be filtered.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>bandwidth<span class="classifier">float</span></dt><dd><p>Bandwidth of the filter, i.e. distance between the low and high
+cut frequencies.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>freqs_to_notch<span class="classifier">array-like | None</span></dt><dd><p>Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center
+of the frequency band to filter out for each sample in the
+batch. Frequencies should be greater than
+<code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where
+<code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.operation" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
+position.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r4e2c3378db18-1" id="id3">[1]</a> and <a class="reference internal" href="#r4e2c3378db18-2" id="id4">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
+frequency band to filter out for each sample in the batch. Frequencies
+should be greater than <code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4e2c3378db18-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r4e2c3378db18-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L154-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#ra3d0be2d719b-1" id="id5">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>proba_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of dropping each channel.
+Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument and to sample channels to erase. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra3d0be2d719b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L191-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_drop<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of dropping each
+channel.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r40820df5a429-1" id="id6">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r40820df5a429-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L218-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r021aa678995d-1" id="id7">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permuted channels. Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument, to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r021aa678995d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id7">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L256-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_shuffle<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of including the
+channel in the set of permuted channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r97a313e9d2d3-1" id="id8">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permutted channels.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r97a313e9d2d3-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id8">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsSymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L351-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Permute EEG channels inverting left and right-side sensors.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r5999d4af6ac6-1" id="id9">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Ordered list of strings containing the names (in 10-20
+nomenclature) of the EEG channels that will be transformed. The
+first name should correspond the data in the first row of X, the
+second name in the second row and so on.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r5999d4af6ac6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id9">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L410-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>permutation<span class="classifier">float</span></dt><dd><p>List of integers defining the new channels order.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.operation" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels according to fixed permutation matrix.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rae865eae5a1e-1" id="id10">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rae865eae5a1e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id10">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Compose">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Compose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L147-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Transform composition.</p>
+<p>Callable class allowing to cast a sequence of Transform objects into a
+single one.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Sequence of Transforms to be composed.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Compose.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L163-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.Compose.operation">
+<span class="sig-name descname"><span class="pre">operation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.Compose.operation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FTSurrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L78-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#ra7c6c14d9bd9-1" id="id11">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+<code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">phase_noise_magnitude</span> <span class="pre">*</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">pi]</span></code>. Defaults to 1.</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI. Default False.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra7c6c14d9bd9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id11">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L127-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>phase_noise_magnitude<span class="classifier">float</span></dt><dd><p>The magnitude of the transformation.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.operation" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#r0a58a8f194ca-1" id="id12">[1]</a>.</p>
+<p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
+and modified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+[0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0a58a8f194ca-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id12">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FrequencyShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_delta_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L631-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Add a random shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>max_delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Maximum shift in Hz that can be sampled (in absolute value).
+Defaults to 2 (shift sampled between -2 and 2 Hz).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L668-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>delta_freq<span class="classifier">float</span></dt><dd><p>The amplitude of the frequency shift (in Hz).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be transformed.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.operation" title="Link to this definition">#</a></dt>
+<dd><p>Adds a shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">GaussianNoise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L283-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly add white noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r8004aa1aead9-1" id="id13">[1]</a>, <a class="reference internal" href="#r8004aa1aead9-2" id="id14">[2]</a> and <a class="reference internal" href="#r8004aa1aead9-3" id="id15">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation to use for the additive noise. Defaults to 0.1.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8004aa1aead9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id13">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r8004aa1aead9-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id14">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r8004aa1aead9-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id15">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L325-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>std<span class="classifier">float</span></dt><dd><p>Standard deviation to use for the additive noise.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white Gaussian noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r0afcdefddcf0-1" id="id16">[1]</a>, <a class="reference internal" href="#r0afcdefddcf0-2" id="id17">[2]</a> and <a class="reference internal" href="#r0afcdefddcf0-3" id="id18">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0afcdefddcf0-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id16">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r0afcdefddcf0-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id17">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r0afcdefddcf0-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id18">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">IdentityTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L139-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Identity transform.</p>
+<p>Transform that does not change the input.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform.operation" title="Link to this definition">#</a></dt>
+<dd><p>Identity operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_per_sample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1030-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Implements Iterator for Mixup for EEG data. See <a class="reference internal" href="#r8b127a6b4d44-1" id="id19">[1]</a>.
+Implementation based on <a class="reference internal" href="#r8b127a6b4d44-2" id="id20">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Mixup hyperparameter.</p></li>
+<li><p><strong>beta_per_sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – By default, one mixing coefficient per batch is drawn from a beta
+distribution. If True, one mixing coefficient per sample is drawn.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8b127a6b4d44-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id19">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r8b127a6b4d44-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id20">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1068-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>params</strong> – Contains the values sampled uniformly between 0 and 1 setting the
+linear interpolation between examples (lam) and the shuffled
+indices of examples that are mixed into original examples
+(idx_perm).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Mixup.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.operation" title="Link to this definition">#</a></dt>
+<dd><p>Mixes two channels of EEG data.</p>
+<p>See <a class="reference internal" href="#r6a9a88664e19-1" id="id21">[1]</a> for details.
+Implementation based on <a class="reference internal" href="#r6a9a88664e19-2" id="id22">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
+3 containing the labels of the two mixed channels and the mixing
+coefficient.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6a9a88664e19-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id21">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r6a9a88664e19-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id22">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'z'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L733-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the desired axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#r6bd6859a1fa8-1" id="id23">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<cite>mne</cite> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em><em>, </em><em>optional</em>) – Axis around which to rotate. Defaults to ‘z’.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6bd6859a1fa8-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id23">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L809-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains four elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sensors_positions_matrix<span class="classifier">numpy.ndarray</span></dt><dd><p>Matrix giving the positions of each sensor in a 3D cartesian
+coordinate system. Should have shape (3, n_channels), where
+n_channels is the number of channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>axis<span class="classifier">’x’ | ‘y’ | ‘z’</span></dt><dd><p>Axis around which to rotate.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>angles<span class="classifier">array-like</span></dt><dd><p>Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the
+rotation angles (in degrees) for each element of the input
+batch, sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degrees``and</span>
+<span class="pre">``max_degrees</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>spherical_splines<span class="classifier">bool</span></dt><dd><p>Whether to use spherical splines for the interpolation or not.
+When <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic
+kernel) will be used (as in the original paper).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.operation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with the desired angle.</p>
+<p>Suggested in <a class="reference internal" href="#rd4626e24d252-1" id="id24">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
+<li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
+angles (in degrees) for each element of the input batch.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
+used (as in the original paper).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd4626e24d252-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id24">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsXRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsXRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L974-L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsXRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the X axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#r44460ea0e68a-1" id="id25">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r44460ea0e68a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id25">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsYRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsYRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L918-L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsYRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the Y axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#r05cec9135bb3-1" id="id26">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r05cec9135bb3-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id26">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SensorsZRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsZRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L862-L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsZRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the Z axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#r8d1fae5fc86d-1" id="id27">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8d1fae5fc86d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id27">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SignFlip">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SignFlip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L53-L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Flip the sign axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SignFlip.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SmoothTimeMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L431-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Smoothly replace a randomly chosen contiguous part of all channels by
+zeros.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rdc48fbfa356b-1" id="id28">[1]</a> and <a class="reference internal" href="#rdc48fbfa356b-2" id="id29">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Number of consecutive samples to zero out. Will be ignored if
+magnitude is not set to None. Defaults to 100.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rdc48fbfa356b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id28">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rdc48fbfa356b-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id29">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L476-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains two elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>mask_start_per_sample<span class="classifier">torch.tensor</span></dt><dd><p>Tensor of integers containing the position (in last dimension)
+where to start masking the signal. Should have the same size as
+the first dimension of X (i.e. one start position per example
+in the batch).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>mask_len_samples<span class="classifier">int</span></dt><dd><p>Number of consecutive samples to zero out.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.operation" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
+<p>Originally proposed in <a class="reference internal" href="#rbbea5cf64590-1" id="id30">[1]</a> and <a class="reference internal" href="#rbbea5cf64590-2" id="id31">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
+start masking the signal. Should have the same size as the first
+dimension of X (i.e. one start position per example in the batch).</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rbbea5cf64590-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id30">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rbbea5cf64590-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id31">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">TimeReverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L28-L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Flip the time axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L36-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Basic transform class used for implementing data augmentation
+operations.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operation</strong> (<em>callable</em>) – A function taking arrays X, y (inputs and targets resp.) and
+other required arguments, and returning the transformed X and y.</p></li>
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 defining the uniform probability of applying the
+operation. Set to 1.0 by default (e.g always apply the operation).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L71-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L68-L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.operation">
+<span class="sig-name descname"><span class="pre">operation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.Transform.operation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.augmentation.Transform.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#braindecode.augmentation.Transform.probability" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.augmentation.base">
+<span id="braindecode-augmentation-base-module"></span><h2>braindecode.augmentation.base module<a class="headerlink" href="#module-braindecode.augmentation.base" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.base.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L186-L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a></p>
+<p>A base dataloader class customized to applying augmentation Transforms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><em>BaseDataset</em></a>) – The dataset containing the signals.</p></li>
+<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><em>Transform</em></a><em>, </em><em>optional</em>) – Transform or sequence of Transform to be applied to each batch.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Device on which to transform the data. Defaults to None.</p></li>
+<li><p><strong>**kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – keyword arguments to pass to standard DataLoader class.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.batch_size">
+<span class="sig-name descname"><span class="pre">batch_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.batch_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.dataset">
+<span class="sig-name descname"><span class="pre">dataset</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.dataset" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.drop_last">
+<span class="sig-name descname"><span class="pre">drop_last</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.drop_last" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.num_workers">
+<span class="sig-name descname"><span class="pre">num_workers</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.num_workers" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.pin_memory">
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.pin_memory" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.pin_memory_device">
+<span class="sig-name descname"><span class="pre">pin_memory_device</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.pin_memory_device" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.prefetch_factor">
+<span class="sig-name descname"><span class="pre">prefetch_factor</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.prefetch_factor" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.sampler">
+<span class="sig-name descname"><span class="pre">sampler</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler" title="(in PyTorch v2.1)"><span class="pre">Sampler</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.sampler" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.AugmentedDataLoader.timeout">
+<span class="sig-name descname"><span class="pre">timeout</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.AugmentedDataLoader.timeout" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Compose">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.base.</span></span><span class="sig-name descname"><span class="pre">Compose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L147-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.Compose" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Transform composition.</p>
+<p>Callable class allowing to cast a sequence of Transform objects into a
+single one.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Sequence of Transforms to be composed.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Compose.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L163-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.Compose.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Compose.operation">
+<span class="sig-name descname"><span class="pre">operation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.base.Compose.operation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Compose.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.Compose.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.IdentityTransform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.base.</span></span><span class="sig-name descname"><span class="pre">IdentityTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L139-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.IdentityTransform" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Identity transform.</p>
+<p>Transform that does not change the input.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.IdentityTransform.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.IdentityTransform.operation" title="Link to this definition">#</a></dt>
+<dd><p>Identity operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.IdentityTransform.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.IdentityTransform.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.base.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L36-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.Transform" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Basic transform class used for implementing data augmentation
+operations.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>operation</strong> (<em>callable</em>) – A function taking arrays X, y (inputs and targets resp.) and
+other required arguments, and returning the transformed X and y.</p></li>
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 defining the uniform probability of applying the
+operation. Set to 1.0 by default (e.g always apply the operation).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L71-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.Transform.forward" title="Link to this definition">#</a></dt>
+<dd><p>General forward pass for an augmentation transform.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L68-L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.base.Transform.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform.operation">
+<span class="sig-name descname"><span class="pre">operation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.augmentation.base.Transform.operation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform.probability">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">probability</span></span><a class="headerlink" href="#braindecode.augmentation.base.Transform.probability" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.base.Transform.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.base.Transform.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.augmentation.functional">
+<span id="braindecode-augmentation-functional-module"></span><h2>braindecode.augmentation.functional module<a class="headerlink" href="#module-braindecode.augmentation.functional" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.bandstop_filter">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">bandstop_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.bandstop_filter" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
+position.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r8d0f6c52c5ba-1" id="id32">[1]</a> and <a class="reference internal" href="#r8d0f6c52c5ba-2" id="id33">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
+frequency band to filter out for each sample in the batch. Frequencies
+should be greater than <code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8d0f6c52c5ba-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id32">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r8d0f6c52c5ba-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id33">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_dropout">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_dropout" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r993501bc6f56-1" id="id34">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r993501bc6f56-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id34">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_permute">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_permute" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels according to fixed permutation matrix.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rcdffecb1125b-1" id="id35">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcdffecb1125b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id35">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_shuffle">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_shuffle" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#rc9211ddb2257-1" id="id36">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permutted channels.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc9211ddb2257-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id36">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.frequency_shift">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">frequency_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.frequency_shift" title="Link to this definition">#</a></dt>
+<dd><p>Adds a shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.ft_surrogate">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">ft_surrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.ft_surrogate" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#r52a4658fffa7-1" id="id37">[1]</a>.</p>
+<p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
+and modified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+[0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r52a4658fffa7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id37">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.gaussian_noise">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.gaussian_noise" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white Gaussian noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r6e3153c55efd-1" id="id38">[1]</a>, <a class="reference internal" href="#r6e3153c55efd-2" id="id39">[2]</a> and <a class="reference internal" href="#r6e3153c55efd-3" id="id40">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6e3153c55efd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id38">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="r6e3153c55efd-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id39">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r6e3153c55efd-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id40">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.identity">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.identity" title="Link to this definition">#</a></dt>
+<dd><p>Identity operation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.mixup">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.mixup" title="Link to this definition">#</a></dt>
+<dd><p>Mixes two channels of EEG data.</p>
+<p>See <a class="reference internal" href="#r3a833d0ab9c7-1" id="id41">[1]</a> for details.
+Implementation based on <a class="reference internal" href="#r3a833d0ab9c7-2" id="id42">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
+3 containing the labels of the two mixed channels and the mixing
+coefficient.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r3a833d0ab9c7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id41">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r3a833d0ab9c7-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id42">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.sensors_rotation">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sensors_rotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sensors_rotation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with the desired angle.</p>
+<p>Suggested in <a class="reference internal" href="#r228e8514004c-1" id="id43">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
+<li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
+angles (in degrees) for each element of the input batch.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
+used (as in the original paper).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r228e8514004c-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id43">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.sign_flip">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sign_flip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sign_flip" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.smooth_time_mask">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_time_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.smooth_time_mask" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
+<p>Originally proposed in <a class="reference internal" href="#rbf2d67638580-1" id="id44">[1]</a> and <a class="reference internal" href="#rbf2d67638580-2" id="id45">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
+start masking the signal. Should have the same size as the first
+dimension of X (i.e. one start position per example in the batch).</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rbf2d67638580-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id44">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rbf2d67638580-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id45">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.augmentation.functional.time_reverse">
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">time_reverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.time_reverse" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.augmentation.transforms">
+<span id="braindecode-augmentation-transforms-module"></span><h2>braindecode.augmentation.transforms module<a class="headerlink" href="#module-braindecode.augmentation.transforms" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.BandstopFilter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">BandstopFilter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L516-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.BandstopFilter" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Apply a band-stop filter with desired bandwidth at a randomly selected
+frequency position between 0 and <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rcf0ce2dbdd32-1" id="id46">[1]</a> and <a class="reference internal" href="#rcf0ce2dbdd32-2" id="id47">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</p></li>
+<li><p><strong>max_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Maximal admissible frequency. The low cut frequency will be sampled so
+that the corresponding high cut frequency + transition (=1Hz) are below
+<code class="docutils literal notranslate"><span class="pre">max_freq</span></code>. If omitted or <cite>None</cite>, will default to the Nyquist
+frequency (<code class="docutils literal notranslate"><span class="pre">sfreq</span> <span class="pre">/</span> <span class="pre">2</span></code>).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcf0ce2dbdd32-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id46">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rcf0ce2dbdd32-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id47">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.BandstopFilter.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L586-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.BandstopFilter.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be filtered.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>bandwidth<span class="classifier">float</span></dt><dd><p>Bandwidth of the filter, i.e. distance between the low and high
+cut frequencies.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>freqs_to_notch<span class="classifier">array-like | None</span></dt><dd><p>Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center
+of the frequency band to filter out for each sample in the
+batch. Frequencies should be greater than
+<code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where
+<code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.BandstopFilter.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.BandstopFilter.operation" title="Link to this definition">#</a></dt>
+<dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
+position.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r378a7bbf7f38-1" id="id48">[1]</a> and <a class="reference internal" href="#r378a7bbf7f38-2" id="id49">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+frequencies.</p></li>
+<li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
+frequency band to filter out for each sample in the batch. Frequencies
+should be greater than <code class="docutils literal notranslate"><span class="pre">bandwidth/2</span> <span class="pre">+</span> <span class="pre">transition</span></code> and lower than
+<code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r378a7bbf7f38-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id48">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r378a7bbf7f38-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id49">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.BandstopFilter.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.BandstopFilter.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsDropout">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">ChannelsDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L154-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsDropout" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r7a6a7c4287f2-1" id="id50">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>proba_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of dropping each channel.
+Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument and to sample channels to erase. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r7a6a7c4287f2-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id50">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsDropout.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L191-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsDropout.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_drop<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of dropping each
+channel.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsDropout.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsDropout.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly set channels to flat signal.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r7fda772ecac4-1" id="id51">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r7fda772ecac4-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id51">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsDropout.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsDropout.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsShuffle">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">ChannelsShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L218-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsShuffle" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#ra18ad71dd141-1" id="id52">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permuted channels. Defaults to 0.2.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument, to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra18ad71dd141-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id52">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsShuffle.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L256-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsShuffle.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>p_shuffle<span class="classifier">float</span></dt><dd><p>Float between 0 and 1 setting the probability of including the
+channel in the set of permuted channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsShuffle.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsShuffle.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly shuffle channels in EEG data matrix.</p>
+<p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r0580ba0e6727-1" id="id53">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+in the set of permutted channels.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to sample which channels to shuffle and to carry the shuffle.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0580ba0e6727-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id53">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+Learning from Heterogeneous EEG Signals with Differentiable Channel
+Reordering. arXiv preprint arXiv:2010.13694.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsShuffle.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsShuffle.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsSymmetry">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">ChannelsSymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L351-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsSymmetry" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Permute EEG channels inverting left and right-side sensors.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rc320b5ee2781-1" id="id54">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Ordered list of strings containing the names (in 10-20
+nomenclature) of the EEG channels that will be transformed. The
+first name should correspond the data in the first row of X, the
+second name in the second row and so on.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc320b5ee2781-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id54">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsSymmetry.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L410-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsSymmetry.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>permutation<span class="classifier">float</span></dt><dd><p>List of integers defining the new channels order.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsSymmetry.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsSymmetry.operation" title="Link to this definition">#</a></dt>
+<dd><p>Permute EEG channels according to fixed permutation matrix.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r79ba8de62d3a-1" id="id55">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r79ba8de62d3a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id55">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+(2018). HAMLET: interpretable human and machine co-learning technique.
+arXiv preprint arXiv:1803.09702.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.ChannelsSymmetry.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.ChannelsSymmetry.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FTSurrogate">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">FTSurrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L78-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FTSurrogate" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#rda3fd54d62b0-1" id="id56">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+<code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">phase_noise_magnitude</span> <span class="pre">*</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">pi]</span></code>. Defaults to 1.</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI. Default False.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rda3fd54d62b0-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id56">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FTSurrogate.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L127-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FTSurrogate.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>phase_noise_magnitude<span class="classifier">float</span></dt><dd><p>The magnitude of the transformation.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FTSurrogate.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FTSurrogate.operation" title="Link to this definition">#</a></dt>
+<dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#r05986c5dcf8a-1" id="id57">[1]</a>.</p>
+<p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
+and modified.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
+[0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+not. It is advised to set it to False when spatial information is
+important for the task, like in BCI.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r05986c5dcf8a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id57">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+Clifford, G. D. (2018). Addressing Class Imbalance in Classification
+Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
+preprint arXiv:1806.08675.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FTSurrogate.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.FTSurrogate.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FrequencyShift">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">FrequencyShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_delta_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L631-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FrequencyShift" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Add a random shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>max_delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Maximum shift in Hz that can be sampled (in absolute value).
+Defaults to 2 (shift sampled between -2 and 2 Hz).</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FrequencyShift.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L668-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FrequencyShift.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>delta_freq<span class="classifier">float</span></dt><dd><p>The amplitude of the frequency shift (in Hz).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>sfreq<span class="classifier">float</span></dt><dd><p>Sampling frequency of the signals to be transformed.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FrequencyShift.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.FrequencyShift.operation" title="Link to this definition">#</a></dt>
+<dd><p>Adds a shift in the frequency domain to all channels.</p>
+<p>Note that here, the shift is the same for all channels of a single example.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.FrequencyShift.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.FrequencyShift.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.GaussianNoise">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">GaussianNoise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L283-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.GaussianNoise" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Randomly add white noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rb8cae2251e3e-1" id="id58">[1]</a>, <a class="reference internal" href="#rb8cae2251e3e-2" id="id59">[2]</a> and <a class="reference internal" href="#rb8cae2251e3e-3" id="id60">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation to use for the additive noise. Defaults to 0.1.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb8cae2251e3e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id58">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="rb8cae2251e3e-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id59">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rb8cae2251e3e-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id60">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.GaussianNoise.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L325-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.GaussianNoise.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>std<span class="classifier">float</span></dt><dd><p>Standard deviation to use for the additive noise.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>random_state<span class="classifier">numpy.random.Generator</span></dt><dd><p>The generator to use.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.GaussianNoise.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.GaussianNoise.operation" title="Link to this definition">#</a></dt>
+<dd><p>Randomly add white Gaussian noise to all channels.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#rd8480f393525-1" id="id61">[1]</a>, <a class="reference internal" href="#rd8480f393525-2" id="id62">[2]</a> and <a class="reference internal" href="#rd8480f393525-3" id="id63">[3]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd8480f393525-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id61">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+augmentation for eeg-based emotion recognition with deep convolutional
+neural networks. In International Conference on Multimedia Modeling
+(pp. 82-93).</p>
+</div>
+<div class="citation" id="rd8480f393525-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id62">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="rd8480f393525-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id63">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.GaussianNoise.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.GaussianNoise.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.Mixup">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">Mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_per_sample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1030-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.Mixup" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Implements Iterator for Mixup for EEG data. See <a class="reference internal" href="#r311135285d84-1" id="id64">[1]</a>.
+Implementation based on <a class="reference internal" href="#r311135285d84-2" id="id65">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Mixup hyperparameter.</p></li>
+<li><p><strong>beta_per_sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – By default, one mixing coefficient per batch is drawn from a beta
+distribution. If True, one mixing coefficient per sample is drawn.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r311135285d84-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id64">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r311135285d84-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id65">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.Mixup.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1068-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.Mixup.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>params</strong> – Contains the values sampled uniformly between 0 and 1 setting the
+linear interpolation between examples (lam) and the shuffled
+indices of examples that are mixed into original examples
+(idx_perm).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.Mixup.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.Mixup.operation" title="Link to this definition">#</a></dt>
+<dd><p>Mixes two channels of EEG data.</p>
+<p>See <a class="reference internal" href="#radf375f01940-1" id="id66">[1]</a> for details.
+Implementation based on <a class="reference internal" href="#radf375f01940-2" id="id67">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
+3 containing the labels of the two mixed channels and the mixing
+coefficient.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="radf375f01940-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id66">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+(2018). mixup: Beyond Empirical Risk Minimization. In 2018
+International Conference on Learning Representations (ICLR)
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="radf375f01940-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id67">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.Mixup.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.Mixup.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SensorsRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'z'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L733-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the desired axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#rcdb4d729dd88-1" id="id68">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<cite>mne</cite> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em><em>, </em><em>optional</em>) – Axis around which to rotate. Defaults to ‘z’.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rcdb4d729dd88-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id68">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsRotation.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L809-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsRotation.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains four elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>sensors_positions_matrix<span class="classifier">numpy.ndarray</span></dt><dd><p>Matrix giving the positions of each sensor in a 3D cartesian
+coordinate system. Should have shape (3, n_channels), where
+n_channels is the number of channels.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>axis<span class="classifier">’x’ | ‘y’ | ‘z’</span></dt><dd><p>Axis around which to rotate.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>angles<span class="classifier">array-like</span></dt><dd><p>Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the
+rotation angles (in degrees) for each element of the input
+batch, sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degrees``and</span>
+<span class="pre">``max_degrees</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>spherical_splines<span class="classifier">bool</span></dt><dd><p>Whether to use spherical splines for the interpolation or not.
+When <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic
+kernel) will be used (as in the original paper).</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsRotation.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsRotation.operation" title="Link to this definition">#</a></dt>
+<dd><p>Interpolates EEG signals over sensors rotated around the desired axis
+with the desired angle.</p>
+<p>Suggested in <a class="reference internal" href="#raa963ec91121-1" id="id69">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+system. Should have shape (3, n_channels), where n_channels is the
+number of channels. Standard 10-20 positions can be obtained from
+<code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">channels</span><span class="o">.</span><span class="n">make_standard_montage</span><span class="p">(</span>
+<span class="gp">... </span>   <span class="s1">&#39;standard_1020&#39;</span>
+<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
+<li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
+angles (in degrees) for each element of the input batch.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
+used (as in the original paper).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="raa963ec91121-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id69">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsRotation.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsRotation.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsXRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SensorsXRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L974-L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsXRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the X axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#ra67d2cffde57-1" id="id70">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra67d2cffde57-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id70">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsXRotation.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsXRotation.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsYRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SensorsYRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L918-L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsYRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the Y axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#rfb50d4fd78d9-1" id="id71">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rfb50d4fd78d9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id71">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsYRotation.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsYRotation.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsZRotation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SensorsZRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L862-L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsZRotation" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.transforms.SensorsRotation" title="braindecode.augmentation.transforms.SensorsRotation"><code class="xref py py-class docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></p>
+<p>Interpolates EEG signals over sensors rotated around the Z axis
+with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
+<p>Suggested in <a class="reference internal" href="#rd85db0e62bb7-1" id="id72">[1]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+Has to be in standard 10-20 style. The order has to be consistent with
+the order of channels in the input matrices that will be fed to the
+transform. This channel will be used to compute approximate sensors
+positions from a standard 10-20 montage.</p></li>
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
+be used (as in the original paper). Defaults to True.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd85db0e62bb7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id72">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+electroencephalographic data. In 2017 39th Annual International
+Conference of the IEEE Engineering in Medicine and Biology Society
+(EMBC) (pp. 471-474).</p>
+</div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SensorsZRotation.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SensorsZRotation.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SignFlip">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SignFlip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L53-L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SignFlip" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Flip the sign axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SignFlip.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SignFlip.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the sign axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SignFlip.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SignFlip.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SmoothTimeMask">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">SmoothTimeMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L431-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SmoothTimeMask" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Smoothly replace a randomly chosen contiguous part of all channels by
+zeros.</p>
+<p>Suggested e.g. in <a class="reference internal" href="#r5bfc23302c5b-1" id="id73">[1]</a> and <a class="reference internal" href="#r5bfc23302c5b-2" id="id74">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Number of consecutive samples to zero out. Will be ignored if
+magnitude is not set to None. Defaults to 100.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r5bfc23302c5b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id73">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r5bfc23302c5b-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id74">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SmoothTimeMask.get_augmentation_params">
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L476-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SmoothTimeMask.get_augmentation_params" title="Link to this definition">#</a></dt>
+<dd><p>Return transform parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
+<li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p><strong>params</strong> – Contains two elements:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>mask_start_per_sample<span class="classifier">torch.tensor</span></dt><dd><p>Tensor of integers containing the position (in last dimension)
+where to start masking the signal. Should have the same size as
+the first dimension of X (i.e. one start position per example
+in the batch).</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>mask_len_samples<span class="classifier">int</span></dt><dd><p>Number of consecutive samples to zero out.</p>
+</dd>
+</dl>
+</li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SmoothTimeMask.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.SmoothTimeMask.operation" title="Link to this definition">#</a></dt>
+<dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
+<p>Originally proposed in <a class="reference internal" href="#r329532d81621-1" id="id75">[1]</a> and <a class="reference internal" href="#r329532d81621-2" id="id76">[2]</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
+start masking the signal. Should have the same size as the first
+dimension of X (i.e. one start position per example in the batch).</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r329532d81621-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id75">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+Subject-aware contrastive learning for biosignals. arXiv preprint
+arXiv:2007.04871.</p>
+</div>
+<div class="citation" id="r329532d81621-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id76">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+Representation Learning for Electroencephalogram Classification. In
+Machine Learning for Health (pp. 238-253). PMLR.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.SmoothTimeMask.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.SmoothTimeMask.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.TimeReverse">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.transforms.</span></span><span class="sig-name descname"><span class="pre">TimeReverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L28-L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.TimeReverse" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.augmentation.base.Transform" title="braindecode.augmentation.base.Transform"><code class="xref py py-class docutils literal notranslate"><span class="pre">Transform</span></code></a></p>
+<p>Flip the time axis of each input with a given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+Used to decide whether or not to transform given the probability
+argument. Defaults to None.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.TimeReverse.operation">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.transforms.TimeReverse.operation" title="Link to this definition">#</a></dt>
+<dd><p>Flip the time axis of each input.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
+<li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.augmentation.transforms.TimeReverse.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.augmentation.transforms.TimeReverse.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+
+
+  
+</div>
+
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.batch_size"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.batch_size</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.dataset"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.dataset</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.drop_last"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.drop_last</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.num_workers"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.num_workers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.pin_memory"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.pin_memory_device"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory_device</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.prefetch_factor"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.prefetch_factor</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.sampler"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.sampler</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.AugmentedDataLoader.timeout"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.timeout</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Compose.forward"><code class="docutils literal notranslate"><span class="pre">Compose.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Compose.operation"><code class="docutils literal notranslate"><span class="pre">Compose.operation</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.operation"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FrequencyShift"><code class="docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FrequencyShift.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FrequencyShift.operation"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.operation"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsXRotation"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsYRotation"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsZRotation"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SignFlip"><code class="docutils literal notranslate"><span class="pre">SignFlip</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SignFlip.operation"><code class="docutils literal notranslate"><span class="pre">SignFlip.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.operation"><code class="docutils literal notranslate"><span class="pre">Transform.operation</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.probability"><code class="docutils literal notranslate"><span class="pre">Transform.probability</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.augmentation.base">braindecode.augmentation.base module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.batch_size"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.batch_size</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.dataset"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.dataset</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.drop_last"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.drop_last</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.num_workers"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.num_workers</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.pin_memory"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.pin_memory_device"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory_device</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.prefetch_factor"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.prefetch_factor</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.sampler"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.sampler</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.AugmentedDataLoader.timeout"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.timeout</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Compose.forward"><code class="docutils literal notranslate"><span class="pre">Compose.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Compose.operation"><code class="docutils literal notranslate"><span class="pre">Compose.operation</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Compose.training"><code class="docutils literal notranslate"><span class="pre">Compose.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.IdentityTransform.training"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform.operation"><code class="docutils literal notranslate"><span class="pre">Transform.operation</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform.probability"><code class="docutils literal notranslate"><span class="pre">Transform.probability</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.base.Transform.training"><code class="docutils literal notranslate"><span class="pre">Transform.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.augmentation.functional">braindecode.augmentation.functional module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.bandstop_filter"><code class="docutils literal notranslate"><span class="pre">bandstop_filter()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_dropout"><code class="docutils literal notranslate"><span class="pre">channels_dropout()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_permute"><code class="docutils literal notranslate"><span class="pre">channels_permute()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_shuffle"><code class="docutils literal notranslate"><span class="pre">channels_shuffle()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.frequency_shift"><code class="docutils literal notranslate"><span class="pre">frequency_shift()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.ft_surrogate"><code class="docutils literal notranslate"><span class="pre">ft_surrogate()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.gaussian_noise"><code class="docutils literal notranslate"><span class="pre">gaussian_noise()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.mixup"><code class="docutils literal notranslate"><span class="pre">mixup()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.sensors_rotation"><code class="docutils literal notranslate"><span class="pre">sensors_rotation()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.sign_flip"><code class="docutils literal notranslate"><span class="pre">sign_flip()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.smooth_time_mask"><code class="docutils literal notranslate"><span class="pre">smooth_time_mask()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.time_reverse"><code class="docutils literal notranslate"><span class="pre">time_reverse()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.augmentation.transforms">braindecode.augmentation.transforms module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.BandstopFilter.training"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsDropout.training"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsShuffle.training"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.ChannelsSymmetry.training"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FTSurrogate.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FTSurrogate.operation"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FTSurrogate.training"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FrequencyShift"><code class="docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FrequencyShift.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FrequencyShift.operation"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.FrequencyShift.training"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.GaussianNoise.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.GaussianNoise.operation"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.GaussianNoise.training"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.Mixup.training"><code class="docutils literal notranslate"><span class="pre">Mixup.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsXRotation"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsXRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsYRotation"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsYRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsZRotation"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SensorsZRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SignFlip"><code class="docutils literal notranslate"><span class="pre">SignFlip</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SignFlip.operation"><code class="docutils literal notranslate"><span class="pre">SignFlip.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SignFlip.training"><code class="docutils literal notranslate"><span class="pre">SignFlip.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.SmoothTimeMask.training"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.transforms.TimeReverse.training"><code class="docutils literal notranslate"><span class="pre">TimeReverse.training</span></code></a></li>
+</ul>
+</li>
+</ul>
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+  <section id="module-braindecode.datasets">
+<span id="braindecode-datasets-package"></span><h1>braindecode.datasets package<a class="headerlink" href="#module-braindecode.datasets" title="Link to this heading">#</a></h1>
+<p>Loader code for some datasets.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BCICompetitionIVDataset4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L23-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>BCI competition IV dataset 4.</p>
+<p>Contains ECoG recordings for three patients moving fingers during the experiment.
+Targets correspond to the time courses of the flexion of each of five fingers.
+See <a class="reference external" href="http://www.bbci.de/competition/iv/desc_4.pdf">http://www.bbci.de/competition/iv/desc_4.pdf</a> and
+<a class="reference external" href="http://www.bbci.de/competition/iv/">http://www.bbci.de/competition/iv/</a> for the dataset and competition description.
+ECoG library containing the dataset: <a class="reference external" href="https://searchworks.stanford.edu/view/zk881ps0522">https://searchworks.stanford.edu/view/zk881ps0522</a></p>
+<p class="rubric">Notes</p>
+<p>When using this dataset please cite <a class="reference internal" href="#rb33c1a7bbf7d-1" id="id1">[1]</a> .</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects. Should be in range 1-3.</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb33c1a7bbf7d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Miller, Kai J. “A library of human electrocorticographic data and analyses.”</p>
+</div>
+</div>
+<p>Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.download">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L74-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.download" title="Link to this definition">#</a></dt>
+<dd><p>Download the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>location.</strong> (<em>path</em><em>  (</em><em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>) </em><em>– Location</em><em> of </em><em>where to look for the data storing</em>) – </p></li>
+<li><p><strong>None</strong> (<em>or None</em><em>) </em><em>– If not</em>) – </p></li>
+<li><p><strong>parameter</strong> (<em>the environment variable</em><em> or </em><em>config</em>) – </p></li>
+<li><p><strong>exist</strong> (<em>MNE_DATASETS_</em><em>(</em><em>dataset</em><em>)</em><em>_PATH is used. If it doesn’t</em>) – </p></li>
+<li><p><strong>“~/mne_data”</strong> (<em>the</em>) – </p></li>
+<li><p><strong>path</strong> (<em>directory is used. If the dataset is not found under the given</em>) – </p></li>
+<li><p><strong>data</strong> (<em>the</em>) – </p></li>
+<li><p><strong>folder.</strong> (<em>will be automatically downloaded to the specified</em>) – </p></li>
+<li><p><strong>exists.</strong> (<em>force_update</em><em> (</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>) </em><em>– Force update</em><em> of </em><em>the dataset even if a local copy</em>) – </p></li>
+<li><p><strong>(</strong><strong>bool</strong> (<em>verbose</em>) – </p></li>
+<li><p><strong>str</strong> – </p></li>
+<li><p><strong>int</strong> – </p></li>
+<li><p><strong>None</strong> – </p></li>
+<li><p><strong>level</strong> (<em>override default verbose</em>) – </p></li>
+<li><p><strong>mne.verbose</strong><strong>(</strong><strong>)</strong><strong>)</strong> (<em>(</em><em>see</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.possible_subjects">
+<span class="sig-name descname"><span class="pre">possible_subjects</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[1,</span> <span class="pre">2,</span> <span class="pre">3]</span></em><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.possible_subjects" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L114-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BNCI2014001" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.moabb.MOABBDataset" title="braindecode.datasets.moabb.MOABBDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a></p>
+<p>BNCI 2014-001 Motor Imagery dataset.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <span id="id2">[R382d436f3223-1]</span>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.bnci.BNCI2014001</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001.BNCI2014001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/bnci.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BNCI2014001.BNCI2014001" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></p>
+<p>BNCI 2014-001 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <a class="reference internal" href="#rc0a22ca68967-1" id="id3">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc0a22ca68967-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BNCI2014001.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BNCI2014001.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'See</span> <span class="pre">moabb.datasets.bnci.BNCI2014001\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">subject_ids:</span> <span class="pre">list(int)</span> <span class="pre">|</span> <span class="pre">int</span> <span class="pre">|</span> <span class="pre">None\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">(list</span> <span class="pre">of)</span> <span class="pre">int</span> <span class="pre">of</span> <span class="pre">subject(s)</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">fetched.</span> <span class="pre">If</span> <span class="pre">None,</span> <span class="pre">data</span> <span class="pre">of</span> <span class="pre">all\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">subjects</span> <span class="pre">is</span> <span class="pre">fetched.\n</span>&#160;&#160;&#160; <span class="pre">'</span></em><a class="headerlink" href="#braindecode.datasets.BNCI2014001.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L386-L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.ConcatDataset" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConcatDataset</span></code></a></p>
+<p>A base class for concatenated datasets. Holds either mne.Raw or
+mne.Epoch in self.datasets and has a pandas DataFrame with additional
+description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_ds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – list of BaseDataset, BaseConcatDataset or WindowsDataset</p></li>
+<li><p><strong>target_transform</strong> (<em>callable</em><em> | </em><em>None</em>) – Optional function to call on targets before returning them.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.get_metadata">
+<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L490-L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.get_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate the metadata and description of the wrapped Epochs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>metadata</strong> – DataFrame containing as many rows as there are windows in the
+BaseConcatDataset, with the metadata and description information
+for each window.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L628-L709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.save" title="Link to this definition">#</a></dt>
+<dd><p>Save datasets to files by creating one subdirectory for each dataset:
+path/</p>
+<blockquote>
+<div><dl class="simple">
+<dt>0/</dt><dd><p>0-raw.fif | 0-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+<dt>1/</dt><dd><p>1-raw.fif | 1-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – <dl class="simple">
+<dt>Directory in which subdirectories are created to store</dt><dd><p>-raw.fif | -epo.fif and .json files to.</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to delete old subdirectories that will be saved to in this
+call.</p></li>
+<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – If provided, the integer is added to the id of the dataset in the
+concat. This is useful in the setting of very large datasets, where
+one dataset has to be processed and saved at a time to account for
+its original position.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L611-L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.DataFrame</em>) – Description in the form key: value where the length of the value
+has to match the number of datasets.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.split">
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">property</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L436-L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.split" title="Link to this definition">#</a></dt>
+<dd><p>Split the dataset based on information listed in its description
+DataFrame or based on indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a string, splitting is performed based on the
+description DataFrame column with this name.
+If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a (list of) list of integers, the position in the first
+list corresponds to the split id and the integers to the
+datapoints of that split.
+If a dict then each key will be used in the returned
+splits dict and each value should be a list of int.</p></li>
+<li><p><strong>property</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Some property which is listed in info DataFrame.</p></li>
+<li><p><strong>split_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – List of indices to be combined in a subset.
+It can be a list of int or a list of list of int.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>splits</strong> – A dictionary with the name of the split (a string) as key and the
+dataset as value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.target_transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">target_transform</span></span><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.target_transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L36-L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a></p>
+<p>Returns samples from an mne.io.Raw object along with a target.</p>
+<p>Dataset which serves samples from an mne.io.Raw object along with a target.
+The target is unique for the dataset, and is obtained through the
+<cite>description</cite> attribute.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – Continuous data.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional description about the continuous signal / subject.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> | </em><em>None</em>) – Name(s) of the index in <cite>description</cite> that should be used to provide the
+target (e.g., to be used in a prediction task later on).</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to the example before it is returned.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.BaseDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L92-L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.BaseDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.BaseDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">HGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L133-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.moabb.MOABBDataset" title="braindecode.datasets.moabb.MOABBDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a></p>
+<p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <span id="id4">[R5e478952091a-1]</span></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.schirrmeister2017.Schirrmeister2017</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD.Schirrmeister2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Schirrmeister2017</span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/schirrmeister2017.py#L17-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD.Schirrmeister2017" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseDataset</span></code></p>
+<p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#rb794529bcd0e-1" id="id5">[1]</a></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb794529bcd0e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, Robin Tibor, et al. “Deep learning with convolutional
+neural networks for EEG decoding and visualization.” Human brain mapping 38.11
+(2017): 5391-5420.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD.Schirrmeister2017.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/schirrmeister2017.py#L77-L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD.Schirrmeister2017.data_path" title="Link to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.HGD.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.HGD.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.HGD.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'See</span> <span class="pre">moabb.datasets.schirrmeister2017.Schirrmeister2017\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">subject_ids:</span> <span class="pre">list(int)</span> <span class="pre">|</span> <span class="pre">int</span> <span class="pre">|</span> <span class="pre">None\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">(list</span> <span class="pre">of)</span> <span class="pre">int</span> <span class="pre">of</span> <span class="pre">subject(s)</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">fetched.</span> <span class="pre">If</span> <span class="pre">None,</span> <span class="pre">data</span> <span class="pre">of</span> <span class="pre">all\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">subjects</span> <span class="pre">is</span> <span class="pre">fetched.\n</span>&#160;&#160;&#160; <span class="pre">'</span></em><a class="headerlink" href="#braindecode.datasets.HGD.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.MOABBDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">MOABBDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L93-L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.MOABBDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>A class for moabb datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – name of dataset included in moabb to be fetched</p></li>
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p></li>
+<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
+to pass to the moabb dataset when instantiating it.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.MOABBDataset.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.MOABBDataset.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.MOABBDataset.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.MOABBDataset.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.SleepPhysionet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">SleepPhysionet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_eeg_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_wake_mins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/sleep_physionet.py#L16-L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.SleepPhysionet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>Sleep Physionet dataset.</p>
+<p>Sleep dataset from <a class="reference external" href="https://physionet.org/content/sleep-edfx/1.0.0/">https://physionet.org/content/sleep-edfx/1.0.0/</a>.
+Contains overnight recordings from 78 healthy subjects.</p>
+<p>See [MNE example](<a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html</a>).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><em>None</em>) – Recordings to load per subject (each subject except 13 has two
+recordings). Can be [1], [2] or [1, 2] (same as None).</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>load_eeg_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, only load the EEG channels and discard the others (EOG, EMG,
+temperature, respiration) to avoid resampling the other signals.</p></li>
+<li><p><strong>crop_wake_mins</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Number of minutes of wake time to keep before the first sleep event
+and after the last sleep event. Used to reduce the imbalance in this
+dataset. Default of 30 mins.</p></li>
+<li><p><strong>crop</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – If not None crop the raw files (e.g. to use only the first 3h).
+Example: <code class="docutils literal notranslate"><span class="pre">crop=(0,</span> <span class="pre">3600*3)</span></code> to keep only the first 3h.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.SleepPhysionet.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.SleepPhysionet.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.SleepPhysionet.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.SleepPhysionet.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.TUH">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L26-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUH" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>Temple University Hospital (TUH) EEG Corpus
+(www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.TUH.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.TUH.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.TUH.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.TUH.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.TUHAbnormal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUHAbnormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pathological'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L274-L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUHAbnormal" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.tuh.TUH" title="braindecode.datasets.tuh.TUH"><code class="xref py py-class docutils literal notranslate"><span class="pre">TUH</span></code></a></p>
+<p>Temple University Hospital (TUH) Abnormal EEG Corpus.
+see www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tuab</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘pathological’, ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.TUHAbnormal.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.TUHAbnormal.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.TUHAbnormal.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.TUHAbnormal.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">WindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L271-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseDataset" title="braindecode.datasets.base.BaseDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseDataset</span></code></a></p>
+<p>Returns windows from an mne.Epochs object along with a target.</p>
+<p>Dataset which serves windows from an mne.Epochs object along with their
+target and additional information. The <cite>metadata</cite> attribute of the Epochs
+object must contain a column called <cite>target</cite>, which will be used to return
+the target that corresponds to a window. Additional columns
+<cite>i_window_in_trial</cite>, <cite>i_start_in_trial</cite>, <cite>i_stop_in_trial</cite> are also
+required to serve information about the windowing (e.g., useful for cropped
+training).
+See <cite>braindecode.datautil.windowers</cite> to directly create a <cite>WindowsDataset</cite>
+from a <cite>BaseDataset</cite> object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>windows</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><em>mne.Epochs</em></a>) – Windows obtained through the application of a windower to a BaseDataset
+(see <cite>braindecode.datautil.windowers</cite>).</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to a window before it is returned.</p></li>
+<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs <cite>misc</cite>
+channels (time series targets). It can be <cite>metadata</cite> (default) or <cite>channels</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.WindowsDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L365-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.WindowsDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.create_from_X_y">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/xy.py#L16-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_X_y" title="Link to this definition">#</a></dt>
+<dd><p>Create a BaseConcatDataset of WindowsDatasets from X and y to be used for
+decoding with skorch and braindecode, where X is a list of pre-cut trials
+and y are corresponding targets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em>) – list of pre-cut trials as n_trials x n_channels x n_times</p></li>
+<li><p><strong>y</strong> (<em>array-like</em>) – targets corresponding to the trials</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows/windows do not equally divide the continuous signal</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of signals.</p></li>
+<li><p><strong>ch_names</strong> (<em>array-like</em>) – Names of the channels.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_epochs">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_epochs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_epochs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L87-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_epochs" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.Epochs</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_epochs</strong> (<em>array-like</em>) – list of mne.Epochs</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_raw">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raws</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">descriptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L13-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_raw" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.RawArrays</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raws</strong> (<em>array-like</em>) – list of mne.RawArrays</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – start offset from original trial onsets in samples</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stop offset from original trial stop in samples</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+<li><p><strong>descriptions</strong> (<em>array-like</em>) – list of dicts or pandas.Series with additional information about the raws</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – mapping from event description to target value</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials withinconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.datasets.base">
+<span id="braindecode-datasets-base-module"></span><h2>braindecode.datasets.base module<a class="headerlink" href="#module-braindecode.datasets.base" title="Link to this heading">#</a></h2>
+<p>Dataset classes.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.base.</span></span><span class="sig-name descname"><span class="pre">BaseConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L386-L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.ConcatDataset" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConcatDataset</span></code></a></p>
+<p>A base class for concatenated datasets. Holds either mne.Raw or
+mne.Epoch in self.datasets and has a pandas DataFrame with additional
+description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_ds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – list of BaseDataset, BaseConcatDataset or WindowsDataset</p></li>
+<li><p><strong>target_transform</strong> (<em>callable</em><em> | </em><em>None</em>) – Optional function to call on targets before returning them.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.get_metadata">
+<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L490-L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.get_metadata" title="Link to this definition">#</a></dt>
+<dd><p>Concatenate the metadata and description of the wrapped Epochs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>metadata</strong> – DataFrame containing as many rows as there are windows in the
+BaseConcatDataset, with the metadata and description information
+for each window.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.save">
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L628-L709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.save" title="Link to this definition">#</a></dt>
+<dd><p>Save datasets to files by creating one subdirectory for each dataset:
+path/</p>
+<blockquote>
+<div><dl class="simple">
+<dt>0/</dt><dd><p>0-raw.fif | 0-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+<dt>1/</dt><dd><p>1-raw.fif | 1-epo.fif
+description.json
+raw_preproc_kwargs.json (if raws were preprocessed)
+window_kwargs.json (if this is a windowed dataset)
+window_preproc_kwargs.json  (if windows were preprocessed)
+target_name.json (if target_name is not None and dataset is raw)</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – <dl class="simple">
+<dt>Directory in which subdirectories are created to store</dt><dd><p>-raw.fif | -epo.fif and .json files to.</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to delete old subdirectories that will be saved to in this
+call.</p></li>
+<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – If provided, the integer is added to the id of the dataset in the
+concat. This is useful in the setting of very large datasets, where
+one dataset has to be processed and saved at a time to account for
+its original position.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L611-L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.DataFrame</em>) – Description in the form key: value where the length of the value
+has to match the number of datasets.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.split">
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">property</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L436-L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.split" title="Link to this definition">#</a></dt>
+<dd><p>Split the dataset based on information listed in its description
+DataFrame or based on indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a string, splitting is performed based on the
+description DataFrame column with this name.
+If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a (list of) list of integers, the position in the first
+list corresponds to the split id and the integers to the
+datapoints of that split.
+If a dict then each key will be used in the returned
+splits dict and each value should be a list of int.</p></li>
+<li><p><strong>property</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Some property which is listed in info DataFrame.</p></li>
+<li><p><strong>split_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – List of indices to be combined in a subset.
+It can be a list of int or a list of list of int.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>splits</strong> – A dictionary with the name of the split (a string) as key and the
+dataset as value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.target_transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">target_transform</span></span><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.target_transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseConcatDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.base.BaseConcatDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.base.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L36-L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a></p>
+<p>Returns samples from an mne.io.Raw object along with a target.</p>
+<p>Dataset which serves samples from an mne.io.Raw object along with a target.
+The target is unique for the dataset, and is obtained through the
+<cite>description</cite> attribute.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – Continuous data.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional description about the continuous signal / subject.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> | </em><em>None</em>) – Name(s) of the index in <cite>description</cite> that should be used to provide the
+target (e.g., to be used in a prediction task later on).</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to the example before it is returned.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.base.BaseDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L92-L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.BaseDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.BaseDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.base.BaseDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.base.EEGWindowsDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.base.</span></span><span class="sig-name descname"><span class="pre">EEGWindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L138-L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.EEGWindowsDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseDataset" title="braindecode.datasets.base.BaseDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseDataset</span></code></a></p>
+<p>Returns windows from an mne.Raw object, its window indices, along with a target.</p>
+<p>Dataset which serves windows from an mne.Epochs object along with their
+target and additional information. The <cite>metadata</cite> attribute of the Epochs
+object must contain a column called <cite>target</cite>, which will be used to return
+the target that corresponds to a window. Additional columns
+<cite>i_window_in_trial</cite>, <cite>i_start_in_trial</cite>, <cite>i_stop_in_trial</cite> are also
+required to serve information about the windowing (e.g., useful for cropped
+training).
+See <cite>braindecode.datautil.windowers</cite> to directly create a <cite>WindowsDataset</cite>
+from a <cite>BaseDataset</cite> object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>windows</strong> (<em>mne.Raw</em><em> or </em><a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><em>mne.Epochs</em></a><em> (</em><em>Epochs is outdated</em><em>)</em>) – Windows obtained through the application of a windower to a BaseDataset
+(see <cite>braindecode.datautil.windowers</cite>).</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to a window before it is returned.</p></li>
+<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Defines whether targets will be extracted from  metadata or from <cite>misc</cite>
+channels (time series targets). It can be <cite>metadata</cite> (default) or <cite>channels</cite>.</p></li>
+<li><p><strong>last_target_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If targets are obtained from misc channels whether all targets if the entire
+(compute) window will be returned or only the last target in the window.</p></li>
+<li><p><strong>metadata</strong> (<em>pandas.DataFrame</em>) – Dataframe with crop indices, so <cite>i_window_in_trial</cite>, <cite>i_start_in_trial</cite>, <cite>i_stop_in_trial</cite>
+as well as <cite>targets</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.EEGWindowsDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.base.EEGWindowsDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.EEGWindowsDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L250-L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.EEGWindowsDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.EEGWindowsDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.base.EEGWindowsDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.base.WindowsDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.base.</span></span><span class="sig-name descname"><span class="pre">WindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L271-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.WindowsDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseDataset" title="braindecode.datasets.base.BaseDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseDataset</span></code></a></p>
+<p>Returns windows from an mne.Epochs object along with a target.</p>
+<p>Dataset which serves windows from an mne.Epochs object along with their
+target and additional information. The <cite>metadata</cite> attribute of the Epochs
+object must contain a column called <cite>target</cite>, which will be used to return
+the target that corresponds to a window. Additional columns
+<cite>i_window_in_trial</cite>, <cite>i_start_in_trial</cite>, <cite>i_stop_in_trial</cite> are also
+required to serve information about the windowing (e.g., useful for cropped
+training).
+See <cite>braindecode.datautil.windowers</cite> to directly create a <cite>WindowsDataset</cite>
+from a <cite>BaseDataset</cite> object.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>windows</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><em>mne.Epochs</em></a>) – Windows obtained through the application of a windower to a BaseDataset
+(see <cite>braindecode.datautil.windowers</cite>).</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
+<li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to a window before it is returned.</p></li>
+<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs <cite>misc</cite>
+channels (time series targets). It can be <cite>metadata</cite> (default) or <cite>channels</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.WindowsDataset.description">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">description</span></span><a class="headerlink" href="#braindecode.datasets.base.WindowsDataset.description" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.base.WindowsDataset.set_description">
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L365-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.base.WindowsDataset.set_description" title="Link to this definition">#</a></dt>
+<dd><p>Update (add or overwrite) the dataset description.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+dataset description.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.datasets.base.WindowsDataset.transform">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">transform</span></span><a class="headerlink" href="#braindecode.datasets.base.WindowsDataset.transform" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.bbci">
+<span id="braindecode-datasets-bbci-module"></span><h2>braindecode.datasets.bbci module<a class="headerlink" href="#module-braindecode.datasets.bbci" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.bbci.BBCIDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.bbci.</span></span><span class="sig-name descname"><span class="pre">BBCIDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_sensor_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bbci.py#L18-L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bbci.BBCIDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Loader class for files created by saving BBCI files in matlab (make
+sure to save with ‘-v7.3’ in matlab, see
+<a class="reference external" href="https://de.mathworks.com/help/matlab/import_export/mat-file-versions.html#buk6i87">https://de.mathworks.com/help/matlab/import_export/mat-file-versions.html#buk6i87</a>
+)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – </p></li>
+<li><p><strong>load_sensor_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Also speeds up loading if you only load some sensors.
+None means load all sensors.</p></li>
+<li><p><strong>check_class_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – check if the class names are part of some known class names at
+Translational NeuroTechnology Lab, AG Ball, Freiburg, Germany.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.bbci.BBCIDataset.get_all_sensors">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_all_sensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bbci.py#L138-L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bbci.BBCIDataset.get_all_sensors" title="Link to this definition">#</a></dt>
+<dd><p>Get all sensors that exist in the given file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – </p></li>
+<li><p><strong>pattern</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Only return those sensor names that match the given pattern.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>sensor_names</strong> – Sensor names that match the pattern or all sensor names in the file.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.bbci.BBCIDataset.load">
+<span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bbci.py#L42-L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bbci.BBCIDataset.load" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.bbci.load_bbci_sets_from_folder">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.bbci.</span></span><span class="sig-name descname"><span class="pre">load_bbci_sets_from_folder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">runs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bbci.py#L671-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bbci.load_bbci_sets_from_folder" title="Link to this definition">#</a></dt>
+<dd><p>Load bbci datasets from files in given folder.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Folder with .BBCI.mat files inside</p></li>
+<li><p><strong>runs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – If you only want to load specific runs.
+Assumes filenames with such kind of part: S001R02 for Run 2.
+Tries to match this regex: <code class="docutils literal notranslate"><span class="pre">'S[0-9]{3,3}R[0-9]{2,2}_'</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.bcicomp">
+<span id="braindecode-datasets-bcicomp-module"></span><h2>braindecode.datasets.bcicomp module<a class="headerlink" href="#module-braindecode.datasets.bcicomp" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.bcicomp.BCICompetitionIVDataset4">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.bcicomp.</span></span><span class="sig-name descname"><span class="pre">BCICompetitionIVDataset4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L23-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>BCI competition IV dataset 4.</p>
+<p>Contains ECoG recordings for three patients moving fingers during the experiment.
+Targets correspond to the time courses of the flexion of each of five fingers.
+See <a class="reference external" href="http://www.bbci.de/competition/iv/desc_4.pdf">http://www.bbci.de/competition/iv/desc_4.pdf</a> and
+<a class="reference external" href="http://www.bbci.de/competition/iv/">http://www.bbci.de/competition/iv/</a> for the dataset and competition description.
+ECoG library containing the dataset: <a class="reference external" href="https://searchworks.stanford.edu/view/zk881ps0522">https://searchworks.stanford.edu/view/zk881ps0522</a></p>
+<p class="rubric">Notes</p>
+<p>When using this dataset please cite <a class="reference internal" href="#r9558a42df370-1" id="id6">[1]</a> .</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects. Should be in range 1-3.</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r9558a42df370-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">1</a><span class="fn-bracket">]</span></span>
+<p>Miller, Kai J. “A library of human electrocorticographic data and analyses.”</p>
+</div>
+</div>
+<p>Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.bcicomp.BCICompetitionIVDataset4.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.bcicomp.BCICompetitionIVDataset4.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.bcicomp.BCICompetitionIVDataset4.download">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L74-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.download" title="Link to this definition">#</a></dt>
+<dd><p>Download the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>location.</strong> (<em>path</em><em>  (</em><em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>) </em><em>– Location</em><em> of </em><em>where to look for the data storing</em>) – </p></li>
+<li><p><strong>None</strong> (<em>or None</em><em>) </em><em>– If not</em>) – </p></li>
+<li><p><strong>parameter</strong> (<em>the environment variable</em><em> or </em><em>config</em>) – </p></li>
+<li><p><strong>exist</strong> (<em>MNE_DATASETS_</em><em>(</em><em>dataset</em><em>)</em><em>_PATH is used. If it doesn’t</em>) – </p></li>
+<li><p><strong>“~/mne_data”</strong> (<em>the</em>) – </p></li>
+<li><p><strong>path</strong> (<em>directory is used. If the dataset is not found under the given</em>) – </p></li>
+<li><p><strong>data</strong> (<em>the</em>) – </p></li>
+<li><p><strong>folder.</strong> (<em>will be automatically downloaded to the specified</em>) – </p></li>
+<li><p><strong>exists.</strong> (<em>force_update</em><em> (</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>) </em><em>– Force update</em><em> of </em><em>the dataset even if a local copy</em>) – </p></li>
+<li><p><strong>(</strong><strong>bool</strong> (<em>verbose</em>) – </p></li>
+<li><p><strong>str</strong> – </p></li>
+<li><p><strong>int</strong> – </p></li>
+<li><p><strong>None</strong> – </p></li>
+<li><p><strong>level</strong> (<em>override default verbose</em>) – </p></li>
+<li><p><strong>mne.verbose</strong><strong>(</strong><strong>)</strong><strong>)</strong> (<em>(</em><em>see</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.bcicomp.BCICompetitionIVDataset4.possible_subjects">
+<span class="sig-name descname"><span class="pre">possible_subjects</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">[1,</span> <span class="pre">2,</span> <span class="pre">3]</span></em><a class="headerlink" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.possible_subjects" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.mne">
+<span id="braindecode-datasets-mne-module"></span><h2>braindecode.datasets.mne module<a class="headerlink" href="#module-braindecode.datasets.mne" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.mne.create_from_mne_epochs">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.mne.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_epochs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_epochs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L87-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.mne.create_from_mne_epochs" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.Epochs</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_of_epochs</strong> (<em>array-like</em>) – list of mne.Epochs</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.mne.create_from_mne_raw">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.mne.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raws</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">descriptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L13-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.mne.create_from_mne_raw" title="Link to this definition">#</a></dt>
+<dd><p>Create WindowsDatasets from mne.RawArrays</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raws</strong> (<em>array-like</em>) – list of mne.RawArrays</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – start offset from original trial onsets in samples</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stop offset from original trial stop in samples</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows do not equally divide the continuous signal</p></li>
+<li><p><strong>descriptions</strong> (<em>array-like</em>) – list of dicts or pandas.Series with additional information about the raws</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – mapping from event description to target value</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials withinconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.moabb">
+<span id="braindecode-datasets-moabb-module"></span><h2>braindecode.datasets.moabb module<a class="headerlink" href="#module-braindecode.datasets.moabb" title="Link to this heading">#</a></h2>
+<p>Dataset objects for some public datasets.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.BNCI2014001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.moabb.</span></span><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L114-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.BNCI2014001" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.moabb.MOABBDataset" title="braindecode.datasets.moabb.MOABBDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a></p>
+<p>BNCI 2014-001 Motor Imagery dataset.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <span id="id7">[Ra334fadbbade-1]</span>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.bnci.BNCI2014001</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.BNCI2014001.BNCI2014001">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/bnci.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.BNCI2014001.BNCI2014001" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BNCI2014_001</span></code></p>
+<p>BNCI 2014-001 Motor Imagery dataset.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <a class="reference internal" href="#r4d406fbeb418-1" id="id8">[1]</a>.</p>
+<p><strong>Dataset Description</strong></p>
+<p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
+paradigm consisted of four different motor imagery tasks, namely the imag-
+ination of movement of the left hand (class 1), right hand (class 2), both
+feet (class 3), and tongue (class 4).  Two sessions on different days were
+recorded for each subject.  Each session is comprised of 6 runs separated
+by short breaks.  One run consists of 48 trials (12 for each of the four
+possible classes), yielding a total of 288 trials per session.</p>
+<p>The subjects were sitting in a comfortable armchair in front of a computer
+screen.  At the beginning of a trial ( t = 0 s), a fixation cross appeared
+on the black screen.  In addition, a short acoustic warning tone was
+presented.  After two seconds ( t = 2 s), a cue in the form of an arrow
+pointing either to the left, right, down or up (corresponding to one of the
+four classes left hand, right hand, foot or tongue) appeared and stayed on
+the screen for 1.25 s.  This prompted the subjects to perform the desired
+motor imagery task.  No feedback was provided.  The subjects were ask to
+carry out the motor imagery task until the fixation cross disappeared from
+the screen at t = 6 s.</p>
+<p>Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm)
+were used to record the EEG; the montage is shown in Figure 3 left.  All
+signals were recorded monopolarly with the left mastoid serving as
+reference and the right mastoid as ground. The signals were sampled with.
+250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of
+the amplifier was set to 100 μV . An additional 50 Hz notch filter was
+enabled to suppress line noise</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4d406fbeb418-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id8">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.BNCI2014001.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.BNCI2014001.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.BNCI2014001.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.BNCI2014001.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.BNCI2014001.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'See</span> <span class="pre">moabb.datasets.bnci.BNCI2014001\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">subject_ids:</span> <span class="pre">list(int)</span> <span class="pre">|</span> <span class="pre">int</span> <span class="pre">|</span> <span class="pre">None\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">(list</span> <span class="pre">of)</span> <span class="pre">int</span> <span class="pre">of</span> <span class="pre">subject(s)</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">fetched.</span> <span class="pre">If</span> <span class="pre">None,</span> <span class="pre">data</span> <span class="pre">of</span> <span class="pre">all\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">subjects</span> <span class="pre">is</span> <span class="pre">fetched.\n</span>&#160;&#160;&#160; <span class="pre">'</span></em><a class="headerlink" href="#braindecode.datasets.moabb.BNCI2014001.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.moabb.</span></span><span class="sig-name descname"><span class="pre">HGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L133-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.HGD" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.moabb.MOABBDataset" title="braindecode.datasets.moabb.MOABBDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a></p>
+<p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<blockquote>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <span id="id9">[Ra4397400a5be-1]</span></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See moabb.datasets.schirrmeister2017.Schirrmeister2017</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD.Schirrmeister2017">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Schirrmeister2017</span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/schirrmeister2017.py#L17-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.HGD.Schirrmeister2017" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseDataset</span></code></p>
+<p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
+<div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <a class="reference internal" href="#r7b7715bc74f4-1" id="id10">[1]</a></p>
+<p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
+44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
+healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
+roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
+movements divided into 13 runs per subject.  The four classes of movements were
+movements of either the left hand, the right hand, both feet, and rest (no
+movement, but same type of visual cue as for the other classes).  The training
+set consists of the approx.  880 trials of all runs except the last two runs,
+the test set of the approx.  160 trials of the last 2 runs.  This dataset was
+acquired in an EEG lab optimized for non-invasive detection of high- frequency
+movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).</p>
+<p>Depending on the direction of a gray arrow that was shown on black back-
+ground, the subjects had to repetitively clench their toes (downward arrow),
+perform sequential finger-tapping of their left (leftward arrow) or right
+(rightward arrow) hand, or relax (upward arrow).  The movements were selected
+to require little proximal muscular activity while still being complex enough
+to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
+repetitive movements at their own pace, which had to be maintained as long as
+the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
+to 4 s of continuous random inter-trial interval.  The order of presentation
+was pseudo-randomized, with all four arrows being shown every four trials.
+Ideally 13 runs were performed to collect 260 trials of each movement and rest.
+The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
+2004).  The experiment was approved by the ethical committee of the University
+of Freiburg.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r7b7715bc74f4-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id10">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, Robin Tibor, et al. “Deep learning with convolutional
+neural networks for EEG decoding and visualization.” Human brain mapping 38.11
+(2017): 5391-5420.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD.Schirrmeister2017.data_path">
+<span class="sig-name descname"><span class="pre">data_path</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">update_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/moabb/datasets/schirrmeister2017.py#L77-L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.HGD.Schirrmeister2017.data_path" title="Link to this definition">#</a></dt>
+<dd><p>Get path to local copy of a subject data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of subject to use</p></li>
+<li><p><strong>path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Location of where to look for the data storing location.
+If None, the environment variable or config parameter
+<code class="docutils literal notranslate"><span class="pre">MNE_DATASETS_(dataset)_PATH</span></code> is used. If it doesn’t exist, the
+“~/mne_data” directory is used. If the dataset
+is not found under the given path, the data
+will be automatically downloaded to the specified folder.</p></li>
+<li><p><strong>force_update</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Force update of the dataset even if a local copy exists.</p></li>
+<li><p><strong>update_path</strong> (bool | None <strong>Deprecated</strong>) – If True, set the MNE_DATASETS_(dataset)_PATH in mne-python
+config to the given path. If None, the user is prompted.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, or </em><em>None</em>) – If not None, override default verbose level
+(see <a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a>).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>path</strong> – Local path to the given data file. This path is contained inside a
+list of length one, for compatibility.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.HGD.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.HGD.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.HGD.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'See</span> <span class="pre">moabb.datasets.schirrmeister2017.Schirrmeister2017\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">subject_ids:</span> <span class="pre">list(int)</span> <span class="pre">|</span> <span class="pre">int</span> <span class="pre">|</span> <span class="pre">None\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">(list</span> <span class="pre">of)</span> <span class="pre">int</span> <span class="pre">of</span> <span class="pre">subject(s)</span> <span class="pre">to</span> <span class="pre">be</span> <span class="pre">fetched.</span> <span class="pre">If</span> <span class="pre">None,</span> <span class="pre">data</span> <span class="pre">of</span> <span class="pre">all\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">subjects</span> <span class="pre">is</span> <span class="pre">fetched.\n</span>&#160;&#160;&#160; <span class="pre">'</span></em><a class="headerlink" href="#braindecode.datasets.moabb.HGD.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.MOABBDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.moabb.</span></span><span class="sig-name descname"><span class="pre">MOABBDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L93-L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.MOABBDataset" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>A class for moabb datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – name of dataset included in moabb to be fetched</p></li>
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be fetched. If None, data of all
+subjects is fetched.</p></li>
+<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
+to pass to the moabb dataset when instantiating it.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.MOABBDataset.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.MOABBDataset.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.MOABBDataset.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.moabb.MOABBDataset.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.moabb.fetch_data_with_moabb">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.moabb.</span></span><span class="sig-name descname"><span class="pre">fetch_data_with_moabb</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L69-L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.moabb.fetch_data_with_moabb" title="Link to this definition">#</a></dt>
+<dd><p>Fetch data using moabb.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – the name of a dataset included in moabb</p></li>
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – (list of) int of subject(s) to be fetched</p></li>
+<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
+to pass to the moabb dataset when instantiating it.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>raws</strong> (<em>mne.Raw</em>)</p></li>
+<li><p><strong>info</strong> (<em>pandas.DataFrame</em>)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.sleep_physionet">
+<span id="braindecode-datasets-sleep-physionet-module"></span><h2>braindecode.datasets.sleep_physionet module<a class="headerlink" href="#module-braindecode.datasets.sleep_physionet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.sleep_physionet.SleepPhysionet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.sleep_physionet.</span></span><span class="sig-name descname"><span class="pre">SleepPhysionet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_eeg_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_wake_mins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/sleep_physionet.py#L16-L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.sleep_physionet.SleepPhysionet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>Sleep Physionet dataset.</p>
+<p>Sleep dataset from <a class="reference external" href="https://physionet.org/content/sleep-edfx/1.0.0/">https://physionet.org/content/sleep-edfx/1.0.0/</a>.
+Contains overnight recordings from 78 healthy subjects.</p>
+<p>See [MNE example](<a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html</a>).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+subjects.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><em>None</em>) – Recordings to load per subject (each subject except 13 has two
+recordings). Can be [1], [2] or [1, 2] (same as None).</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>load_eeg_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, only load the EEG channels and discard the others (EOG, EMG,
+temperature, respiration) to avoid resampling the other signals.</p></li>
+<li><p><strong>crop_wake_mins</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Number of minutes of wake time to keep before the first sleep event
+and after the last sleep event. Used to reduce the imbalance in this
+dataset. Default of 30 mins.</p></li>
+<li><p><strong>crop</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – If not None crop the raw files (e.g. to use only the first 3h).
+Example: <code class="docutils literal notranslate"><span class="pre">crop=(0,</span> <span class="pre">3600*3)</span></code> to keep only the first 3h.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.sleep_physionet.SleepPhysionet.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.sleep_physionet.SleepPhysionet.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.sleep_physionet.SleepPhysionet.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.sleep_physionet.SleepPhysionet.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.tuh">
+<span id="braindecode-datasets-tuh-module"></span><h2>braindecode.datasets.tuh module<a class="headerlink" href="#module-braindecode.datasets.tuh" title="Link to this heading">#</a></h2>
+<p>Dataset classes for the Temple University Hospital (TUH) EEG Corpus and the
+TUH Abnormal EEG Corpus.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUH">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.tuh.</span></span><span class="sig-name descname"><span class="pre">TUH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L26-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.tuh.TUH" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.base.BaseConcatDataset" title="braindecode.datasets.base.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></p>
+<p>Temple University Hospital (TUH) EEG Corpus
+(www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUH.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.tuh.TUH.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUH.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.tuh.TUH.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUHAbnormal">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.tuh.</span></span><span class="sig-name descname"><span class="pre">TUHAbnormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pathological'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L274-L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.tuh.TUHAbnormal" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.datasets.tuh.TUH" title="braindecode.datasets.tuh.TUH"><code class="xref py py-class docutils literal notranslate"><span class="pre">TUH</span></code></a></p>
+<p>Temple University Hospital (TUH) Abnormal EEG Corpus.
+see www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tuab</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+overwrite default chronological order, e.g. if recording_ids=[1,0],
+then the first recording returned by this class will be chronologically
+later then the second recording. Provide recording_ids in ascending
+order to preserve chronological order.).</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘pathological’, ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+description.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUHAbnormal.cumulative_sizes">
+<span class="sig-name descname"><span class="pre">cumulative_sizes</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.tuh.TUHAbnormal.cumulative_sizes" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.datasets.tuh.TUHAbnormal.datasets">
+<span class="sig-name descname"><span class="pre">datasets</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset" title="(in PyTorch v2.1)"><span class="pre">Dataset</span></a><span class="p"><span class="pre">[</span></span><span class="pre">T_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.datasets.tuh.TUHAbnormal.datasets" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datasets.xy">
+<span id="braindecode-datasets-xy-module"></span><h2>braindecode.datasets.xy module<a class="headerlink" href="#module-braindecode.datasets.xy" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datasets.xy.create_from_X_y">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.xy.</span></span><span class="sig-name descname"><span class="pre">create_from_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/xy.py#L16-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.xy.create_from_X_y" title="Link to this definition">#</a></dt>
+<dd><p>Create a BaseConcatDataset of WindowsDatasets from X and y to be used for
+decoding with skorch and braindecode, where X is a list of pre-cut trials
+and y are corresponding targets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>array-like</em>) – list of pre-cut trials as n_trials x n_channels x n_times</p></li>
+<li><p><strong>y</strong> (<em>array-like</em>) – targets corresponding to the trials</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+windows/windows do not equally divide the continuous signal</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of signals.</p></li>
+<li><p><strong>ch_names</strong> (<em>array-like</em>) – Names of the channels.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
+and braindecode</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
+  
+</div>
+
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+    <i class="fa-solid fa-list"></i> On this page
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.datasets"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.possible_subjects"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.possible_subjects</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BNCI2014001.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.BNCI2014001</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BNCI2014001.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BNCI2014001.datasets"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BNCI2014001.doc"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.datasets"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.description</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.target_transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.target_transform</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.description</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD"><code class="docutils literal notranslate"><span class="pre">HGD</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD.Schirrmeister2017"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD.Schirrmeister2017.data_path"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017.data_path()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">HGD.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD.datasets"><code class="docutils literal notranslate"><span class="pre">HGD.datasets</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.HGD.doc"><code class="docutils literal notranslate"><span class="pre">HGD.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.MOABBDataset"><code class="docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.MOABBDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.MOABBDataset.datasets"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.SleepPhysionet.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.SleepPhysionet.datasets"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUH"><code class="docutils literal notranslate"><span class="pre">TUH</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUH.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUH.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUH.datasets"><code class="docutils literal notranslate"><span class="pre">TUH.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUHAbnormal.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.cumulative_sizes</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUHAbnormal.datasets"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.description</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.set_description()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.create_from_X_y"><code class="docutils literal notranslate"><span class="pre">create_from_X_y()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.create_from_mne_raw"><code class="docutils literal notranslate"><span class="pre">create_from_mne_raw()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.base">braindecode.datasets.base module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.datasets"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.datasets</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.description</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.target_transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.target_transform</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseConcatDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.description</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.BaseDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.EEGWindowsDataset"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.EEGWindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.description</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.EEGWindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.set_description()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.EEGWindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.WindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.description</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.WindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.set_description()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.base.WindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.bbci">braindecode.datasets.bbci module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bbci.BBCIDataset"><code class="docutils literal notranslate"><span class="pre">BBCIDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bbci.BBCIDataset.get_all_sensors"><code class="docutils literal notranslate"><span class="pre">BBCIDataset.get_all_sensors()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bbci.BBCIDataset.load"><code class="docutils literal notranslate"><span class="pre">BBCIDataset.load()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bbci.load_bbci_sets_from_folder"><code class="docutils literal notranslate"><span class="pre">load_bbci_sets_from_folder()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.bcicomp">braindecode.datasets.bcicomp module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.datasets"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.datasets</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.possible_subjects"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.possible_subjects</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.mne">braindecode.datasets.mne module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.mne.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.mne.create_from_mne_raw"><code class="docutils literal notranslate"><span class="pre">create_from_mne_raw()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.moabb">braindecode.datasets.moabb module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.BNCI2014001.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.BNCI2014001</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.BNCI2014001.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.BNCI2014001.datasets"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.datasets</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.BNCI2014001.doc"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD"><code class="docutils literal notranslate"><span class="pre">HGD</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD.Schirrmeister2017"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD.Schirrmeister2017.data_path"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017.data_path()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">HGD.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD.datasets"><code class="docutils literal notranslate"><span class="pre">HGD.datasets</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.HGD.doc"><code class="docutils literal notranslate"><span class="pre">HGD.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.MOABBDataset"><code class="docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.MOABBDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.MOABBDataset.datasets"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.moabb.fetch_data_with_moabb"><code class="docutils literal notranslate"><span class="pre">fetch_data_with_moabb()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.sleep_physionet">braindecode.datasets.sleep_physionet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.sleep_physionet.SleepPhysionet.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.sleep_physionet.SleepPhysionet.datasets"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.datasets</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.tuh">braindecode.datasets.tuh module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUH"><code class="docutils literal notranslate"><span class="pre">TUH</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUH.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUH.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUH.datasets"><code class="docutils literal notranslate"><span class="pre">TUH.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUHAbnormal.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.cumulative_sizes</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.tuh.TUHAbnormal.datasets"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.datasets</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.datasets.xy">braindecode.datasets.xy module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.xy.create_from_X_y"><code class="docutils literal notranslate"><span class="pre">create_from_X_y()</span></code></a></li>
+</ul>
+</li>
+</ul>
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+  
+  <section id="module-braindecode.datautil">
+<span id="braindecode-datautil-package"></span><h1>braindecode.datautil package<a class="headerlink" href="#module-braindecode.datautil" title="Link to this heading">#</a></h1>
+<p>Utilities for data manipulation.</p>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.datautil.mne">
+<span id="braindecode-datautil-mne-module"></span><h2>braindecode.datautil.mne module<a class="headerlink" href="#module-braindecode.datautil.mne" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.datautil.preprocess">
+<span id="braindecode-datautil-preprocess-module"></span><h2>braindecode.datautil.preprocess module<a class="headerlink" href="#module-braindecode.datautil.preprocess" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.datautil.serialization">
+<span id="braindecode-datautil-serialization-module"></span><h2>braindecode.datautil.serialization module<a class="headerlink" href="#module-braindecode.datautil.serialization" title="Link to this heading">#</a></h2>
+<p>Convenience functions for storing and loading of windows datasets.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datautil.serialization.load_concat_dataset">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.serialization.</span></span><span class="sig-name descname"><span class="pre">load_concat_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids_to_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/serialization.py#L157-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.serialization.load_concat_dataset" title="Link to this definition">#</a></dt>
+<dd><p>Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from
+files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><em>pathlib.Path</em></a>) – Path to the directory of the .fif / -epo.fif and .json files.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to preload the data.</p></li>
+<li><p><strong>ids_to_load</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Ids of specific files to load.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><em>None</em>) – Load specific description column as target. If not given, take saved
+target name.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>concat_dataset</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a> of BaseDatasets or WindowsDatasets</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datautil.serialization.save_concat_dataset">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.serialization.</span></span><span class="sig-name descname"><span class="pre">save_concat_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concat_dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/serialization.py#L23-L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.serialization.save_concat_dataset" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="module-braindecode.datautil.util">
+<span id="braindecode-datautil-util-module"></span><h2>braindecode.datautil.util module<a class="headerlink" href="#module-braindecode.datautil.util" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datautil.util.ms_to_samples">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.util.</span></span><span class="sig-name descname"><span class="pre">ms_to_samples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/util.py#L6-L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.util.ms_to_samples" title="Link to this definition">#</a></dt>
+<dd><p>Compute milliseconds to number of samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ms</strong> (<em>number</em>) – Milliseconds</p></li>
+<li><p><strong>fs</strong> (<em>number</em>) – Sampling rate</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>n_samples</strong> – Number of samples</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.datautil.util.samples_to_ms">
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.util.</span></span><span class="sig-name descname"><span class="pre">samples_to_ms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/util.py#L26-L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.util.samples_to_ms" title="Link to this definition">#</a></dt>
+<dd><p>Compute milliseconds to number of samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_samples</strong> (<em>number</em>) – Number of samples</p></li>
+<li><p><strong>fs</strong> (<em>number</em>) – Sampling rate</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>milliseconds</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.datautil.windowers">
+<span id="braindecode-datautil-windowers-module"></span><h2>braindecode.datautil.windowers module<a class="headerlink" href="#module-braindecode.datautil.windowers" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.datautil.xy">
+<span id="braindecode-datautil-xy-module"></span><h2>braindecode.datautil.xy module<a class="headerlink" href="#module-braindecode.datautil.xy" title="Link to this heading">#</a></h2>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datautil.serialization.load_concat_dataset"><code class="docutils literal notranslate"><span class="pre">load_concat_dataset()</span></code></a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datautil.util.ms_to_samples"><code class="docutils literal notranslate"><span class="pre">ms_to_samples()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datautil.util.samples_to_ms"><code class="docutils literal notranslate"><span class="pre">samples_to_ms()</span></code></a></li>
+</ul>
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+<div>
+  
+  <section id="module-braindecode">
+<span id="braindecode-package"></span><h1>braindecode package<a class="headerlink" href="#module-braindecode" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.</span></span><span class="sig-name descname"><span class="pre">EEGClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion=&lt;class</span> <span class="pre">'torch.nn.modules.loss.CrossEntropyLoss'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L22-L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_EEGNeuralNet</span></code>, <a class="reference external" href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNetClassifier</span></code></a></p>
+<p>Classifier that does not assume softmax activation.
+Calls loss function directly without applying log or anything.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.NLLLoss</em><em>)</em>) – Negative log likelihood loss. Note that the module should return
+probabilities, the log is applied during <code class="docutils literal notranslate"><span class="pre">get_loss</span></code>.</p></li>
+<li><p><strong>classes</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default=None</em><em>)</em>) – If None, the <code class="docutils literal notranslate"><span class="pre">classes_</span></code> attribute will be inferred from the
+<code class="docutils literal notranslate"><span class="pre">y</span></code> data passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If a non-empty list is passed,
+that list will be returned as <code class="docutils literal notranslate"><span class="pre">classes_</span></code>. If the initial
+skorch behavior should be restored, i.e. raising an
+<code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>, pass an empty list.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.EEGClassifier._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.EEGClassifier._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.EEGClassifier._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.classes_">
+<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#braindecode.EEGClassifier.classes_" title="Link to this definition">#</a></dt>
+<dd><p>A list of class labels known to the classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_classes, )</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&quot;Classifier</span> <span class="pre">that</span> <span class="pre">does</span> <span class="pre">not</span> <span class="pre">assume</span> <span class="pre">softmax</span> <span class="pre">activation.\n</span>&#160;&#160;&#160; <span class="pre">Calls</span> <span class="pre">loss</span> <span class="pre">function</span> <span class="pre">directly</span> <span class="pre">without</span> <span class="pre">applying</span> <span class="pre">log</span> <span class="pre">or</span> <span class="pre">anything.\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">module:</span> <span class="pre">str</span> <span class="pre">or</span> <span class="pre">torch</span> <span class="pre">Module</span> <span class="pre">(class</span> <span class="pre">or</span> <span class="pre">instance)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Either</span> <span class="pre">the</span> <span class="pre">name</span> <span class="pre">of</span> <span class="pre">one</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">braindecode</span> <span class="pre">models</span> <span class="pre">(see\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:obj:`braindecode.models.util.models_dict`)</span> <span class="pre">or</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">PyTorch</span> <span class="pre">module.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">When</span> <span class="pre">passing</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">torch</span> <span class="pre">module,</span> <span class="pre">uninstantiated</span> <span class="pre">class</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">preferred,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">although</span> <span class="pre">instantiated</span> <span class="pre">modules</span> <span class="pre">will</span> <span class="pre">also</span> <span class="pre">work.\n\n</span>&#160;&#160;&#160; <span class="pre">cropped:</span> <span class="pre">bool</span> <span class="pre">(default=False)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">torch</span> <span class="pre">model</span> <span class="pre">passed</span> <span class="pre">to</span> <span class="pre">this</span> <span class="pre">class</span> <span class="pre">is</span> <span class="pre">cropped</span> <span class="pre">or</span> <span class="pre">not.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Currently</span> <span class="pre">used</span> <span class="pre">for</span> <span class="pre">callbacks</span> <span class="pre">definition.\n\n</span>&#160;&#160;&#160; <span class="pre">callbacks:</span> <span class="pre">None</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">Callback</span> <span class="pre">instances</span> <span class="pre">(default=None)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">More</span> <span class="pre">callbacks,</span> <span class="pre">in</span> <span class="pre">addition</span> <span class="pre">to</span> <span class="pre">those</span> <span class="pre">returned</span> <span class="pre">by\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``get_default_callbacks``.</span> <span class="pre">Each</span> <span class="pre">callback</span> <span class="pre">should</span> <span class="pre">inherit</span> <span class="pre">from\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:class:`skorch.callbacks.Callback`.</span> <span class="pre">If</span> <span class="pre">not</span> <span class="pre">``None``,</span> <span class="pre">callbacks</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">a\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">specifying</span> <span class="pre">`sklearn`</span> <span class="pre">scoring</span> <span class="pre">functions</span> <span class="pre">(for</span> <span class="pre">scoring\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">functions</span> <span class="pre">names</span> <span class="pre">see:</span> <span class="pre">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">or</span> <span class="pre">a</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">callbacks</span> <span class="pre">where</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">names</span> <span class="pre">are</span> <span class="pre">inferred</span> <span class="pre">from</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">class</span> <span class="pre">name.</span> <span class="pre">Name</span> <span class="pre">conflicts</span> <span class="pre">are</span> <span class="pre">resolved</span> <span class="pre">by</span> <span class="pre">appending</span> <span class="pre">a</span> <span class="pre">count</span> <span class="pre">suffix\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">starting</span> <span class="pre">with</span> <span class="pre">1,</span> <span class="pre">e.g.</span> <span class="pre">``EpochScoring_1``.</span> <span class="pre">Alternatively,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">a</span> <span class="pre">tuple</span> <span class="pre">``(name,</span> <span class="pre">callback)``</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">passed,</span> <span class="pre">where</span> <span class="pre">``name``\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">should</span> <span class="pre">be</span> <span class="pre">unique.</span> <span class="pre">Callbacks</span> <span class="pre">may</span> <span class="pre">or</span> <span class="pre">may</span> <span class="pre">not</span> <span class="pre">be</span> <span class="pre">instantiated.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">The</span> <span class="pre">callback</span> <span class="pre">name</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">used</span> <span class="pre">to</span> <span class="pre">set</span> <span class="pre">parameters</span> <span class="pre">on</span> <span class="pre">specific\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">callbacks</span> <span class="pre">(e.g.,</span> <span class="pre">for</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">with</span> <span class="pre">name</span> <span class="pre">``'print_log'``,</span> <span class="pre">use\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``net.set_params(callbacks__print_log__keys_ignored=['epoch',\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">'train_loss'])``).\n\n</span>&#160;&#160;&#160; <span class="pre">iterator_train__shuffle:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">will</span> <span class="pre">be</span> <span class="pre">shuffled.</span> <span class="pre">As</span> <span class="pre">skorch</span> <span class="pre">does</span> <span class="pre">not\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">shuffle</span> <span class="pre">the</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">by</span> <span class="pre">default</span> <span class="pre">this</span> <span class="pre">one</span> <span class="pre">overwrites</span> <span class="pre">this</span> <span class="pre">option.\n\n</span>&#160;&#160;&#160; <span class="pre">aggregate_predictions:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Whether</span> <span class="pre">to</span> <span class="pre">average</span> <span class="pre">cropped</span> <span class="pre">predictions</span> <span class="pre">to</span> <span class="pre">obtain</span> <span class="pre">window</span> <span class="pre">predictions.</span> <span class="pre">Used</span> <span class="pre">only</span> <span class="pre">in</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">cropped</span> <span class="pre">mode.\n\n</span>&#160;&#160;&#160; <span class="pre">&quot;</span></em><a class="headerlink" href="#braindecode.EEGClassifier.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L90-L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.get_loss">
+<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L142-L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier.get_loss" title="Link to this definition">#</a></dt>
+<dd><p>Return the loss for this batch by calling NeuralNet get_loss.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_pred</strong> (<em>torch tensor</em>) – Predicted target values</p></li>
+<li><p><strong>y_true</strong> (<em>torch tensor</em>) – True target values.</p></li>
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L174-L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier.predict" title="Link to this definition">#</a></dt>
+<dd><p>Return class labels for samples in X.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_pred</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L97-L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGClassifier.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L200-L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGClassifier.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.</span></span><span class="sig-name descname"><span class="pre">EEGRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L19-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGRegressor" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_EEGNeuralNet</span></code>, <a class="reference external" href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNetRegressor</span></code></a></p>
+<p>Regressor that calls loss function directly.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.MSELoss</em><em>)</em>) – Mean squared error loss.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.EEGRegressor.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.EEGRegressor._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.EEGRegressor._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.EEGRegressor._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&quot;Regressor</span> <span class="pre">that</span> <span class="pre">calls</span> <span class="pre">loss</span> <span class="pre">function</span> <span class="pre">directly.\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">module:</span> <span class="pre">str</span> <span class="pre">or</span> <span class="pre">torch</span> <span class="pre">Module</span> <span class="pre">(class</span> <span class="pre">or</span> <span class="pre">instance)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Either</span> <span class="pre">the</span> <span class="pre">name</span> <span class="pre">of</span> <span class="pre">one</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">braindecode</span> <span class="pre">models</span> <span class="pre">(see\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:obj:`braindecode.models.util.models_dict`)</span> <span class="pre">or</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">PyTorch</span> <span class="pre">module.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">When</span> <span class="pre">passing</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">torch</span> <span class="pre">module,</span> <span class="pre">uninstantiated</span> <span class="pre">class</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">preferred,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">although</span> <span class="pre">instantiated</span> <span class="pre">modules</span> <span class="pre">will</span> <span class="pre">also</span> <span class="pre">work.\n\n</span>&#160;&#160;&#160; <span class="pre">cropped:</span> <span class="pre">bool</span> <span class="pre">(default=False)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">torch</span> <span class="pre">model</span> <span class="pre">passed</span> <span class="pre">to</span> <span class="pre">this</span> <span class="pre">class</span> <span class="pre">is</span> <span class="pre">cropped</span> <span class="pre">or</span> <span class="pre">not.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Currently</span> <span class="pre">used</span> <span class="pre">for</span> <span class="pre">callbacks</span> <span class="pre">definition.\n\n</span>&#160;&#160;&#160; <span class="pre">callbacks:</span> <span class="pre">None</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">Callback</span> <span class="pre">instances</span> <span class="pre">(default=None)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">More</span> <span class="pre">callbacks,</span> <span class="pre">in</span> <span class="pre">addition</span> <span class="pre">to</span> <span class="pre">those</span> <span class="pre">returned</span> <span class="pre">by\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``get_default_callbacks``.</span> <span class="pre">Each</span> <span class="pre">callback</span> <span class="pre">should</span> <span class="pre">inherit</span> <span class="pre">from\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:class:`skorch.callbacks.Callback`.</span> <span class="pre">If</span> <span class="pre">not</span> <span class="pre">``None``,</span> <span class="pre">callbacks</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">a\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">specifying</span> <span class="pre">`sklearn`</span> <span class="pre">scoring</span> <span class="pre">functions</span> <span class="pre">(for</span> <span class="pre">scoring\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">functions</span> <span class="pre">names</span> <span class="pre">see:</span> <span class="pre">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">or</span> <span class="pre">a</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">callbacks</span> <span class="pre">where</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">names</span> <span class="pre">are</span> <span class="pre">inferred</span> <span class="pre">from</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">class</span> <span class="pre">name.</span> <span class="pre">Name</span> <span class="pre">conflicts</span> <span class="pre">are</span> <span class="pre">resolved</span> <span class="pre">by</span> <span class="pre">appending</span> <span class="pre">a</span> <span class="pre">count</span> <span class="pre">suffix\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">starting</span> <span class="pre">with</span> <span class="pre">1,</span> <span class="pre">e.g.</span> <span class="pre">``EpochScoring_1``.</span> <span class="pre">Alternatively,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">a</span> <span class="pre">tuple</span> <span class="pre">``(name,</span> <span class="pre">callback)``</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">passed,</span> <span class="pre">where</span> <span class="pre">``name``\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">should</span> <span class="pre">be</span> <span class="pre">unique.</span> <span class="pre">Callbacks</span> <span class="pre">may</span> <span class="pre">or</span> <span class="pre">may</span> <span class="pre">not</span> <span class="pre">be</span> <span class="pre">instantiated.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">The</span> <span class="pre">callback</span> <span class="pre">name</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">used</span> <span class="pre">to</span> <span class="pre">set</span> <span class="pre">parameters</span> <span class="pre">on</span> <span class="pre">specific\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">callbacks</span> <span class="pre">(e.g.,</span> <span class="pre">for</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">with</span> <span class="pre">name</span> <span class="pre">``'print_log'``,</span> <span class="pre">use\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``net.set_params(callbacks__print_log__keys_ignored=['epoch',\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">'train_loss'])``).\n\n</span>&#160;&#160;&#160; <span class="pre">iterator_train__shuffle:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">will</span> <span class="pre">be</span> <span class="pre">shuffled.</span> <span class="pre">As</span> <span class="pre">skorch</span> <span class="pre">does</span> <span class="pre">not\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">shuffle</span> <span class="pre">the</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">by</span> <span class="pre">default</span> <span class="pre">this</span> <span class="pre">one</span> <span class="pre">overwrites</span> <span class="pre">this</span> <span class="pre">option.\n\n</span>&#160;&#160;&#160; <span class="pre">aggregate_predictions:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Whether</span> <span class="pre">to</span> <span class="pre">average</span> <span class="pre">cropped</span> <span class="pre">predictions</span> <span class="pre">to</span> <span class="pre">obtain</span> <span class="pre">window</span> <span class="pre">predictions.</span> <span class="pre">Used</span> <span class="pre">only</span> <span class="pre">in</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">cropped</span> <span class="pre">mode.\n\n</span>&#160;&#160;&#160; <span class="pre">&quot;</span></em><a class="headerlink" href="#braindecode.EEGRegressor.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L171-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGRegressor.fit" title="Link to this definition">#</a></dt>
+<dd><p>Initialize and fit the module.</p>
+<p>If the module was already initialized, by calling fit, the
+module will be re-initialized (unless <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> is True).
+If possible, signal-related parameters are inferred from the
+data and passed to the module at initialisation.
+Depending on the type of input passed, the following parameters
+are inferred:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>mne.Epochs: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code>, <code class="docutils literal notranslate"><span class="pre">chs_info</span></code>,
+<code class="docutils literal notranslate"><span class="pre">sfreq</span></code>, <code class="docutils literal notranslate"><span class="pre">input_window_seconds</span></code></p></li>
+<li><p>numpy array: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>WindowsDataset with <code class="docutils literal notranslate"><span class="pre">targets_from='metadata'</span></code>
+(or BaseConcatDataset of such datasets): <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>other Dataset: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code></p></li>
+<li><p>other types: no parameters are inferred.</p></li>
+</ul>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>mne.Epochs</p></li>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>y</strong> (<em>target data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – The same data types as for <code class="docutils literal notranslate"><span class="pre">X</span></code> are supported. If your X is
+a Dataset that contains the target, <code class="docutils literal notranslate"><span class="pre">y</span></code> may be set to
+None.</p></li>
+<li><p><strong>**fit_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Additional parameters passed to the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of
+the module and to the <code class="docutils literal notranslate"><span class="pre">self.train_split</span></code> call.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L75-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGRegressor.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L82-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGRegressor.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+</dl>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Regressors predict regression targets, so output of this method
+can’t be interpreted as probabilities. We advise you to use
+<cite>predict</cite> method instead of <cite>predict_proba</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.EEGRegressor.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L132-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.EEGRegressor.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="subpackages">
+<h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this heading">#</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.augmentation.html">braindecode.augmentation package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.batch_size"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.batch_size</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.dataset"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.dataset</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.drop_last"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.drop_last</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.num_workers"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.num_workers</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.pin_memory"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.pin_memory_device"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory_device</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.prefetch_factor"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.prefetch_factor</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.sampler"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.sampler</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader.timeout"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.timeout</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Compose.forward"><code class="docutils literal notranslate"><span class="pre">Compose.forward()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Compose.operation"><code class="docutils literal notranslate"><span class="pre">Compose.operation</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FTSurrogate.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FTSurrogate.operation"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FrequencyShift"><code class="docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FrequencyShift.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FrequencyShift.operation"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.GaussianNoise.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.GaussianNoise.operation"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsXRotation"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsYRotation"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsZRotation"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SignFlip"><code class="docutils literal notranslate"><span class="pre">SignFlip</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SignFlip.operation"><code class="docutils literal notranslate"><span class="pre">SignFlip.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform.operation"><code class="docutils literal notranslate"><span class="pre">Transform.operation</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform.probability"><code class="docutils literal notranslate"><span class="pre">Transform.probability</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.base">braindecode.augmentation.base module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.batch_size"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.batch_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.dataset"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.dataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.drop_last"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.drop_last</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.num_workers"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.num_workers</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.pin_memory"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.pin_memory_device"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.pin_memory_device</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.prefetch_factor"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.prefetch_factor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.sampler"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.sampler</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.AugmentedDataLoader.timeout"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader.timeout</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Compose.forward"><code class="docutils literal notranslate"><span class="pre">Compose.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Compose.operation"><code class="docutils literal notranslate"><span class="pre">Compose.operation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Compose.training"><code class="docutils literal notranslate"><span class="pre">Compose.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.IdentityTransform.training"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform.operation"><code class="docutils literal notranslate"><span class="pre">Transform.operation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform.probability"><code class="docutils literal notranslate"><span class="pre">Transform.probability</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.base.Transform.training"><code class="docutils literal notranslate"><span class="pre">Transform.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.functional">braindecode.augmentation.functional module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.bandstop_filter"><code class="docutils literal notranslate"><span class="pre">bandstop_filter()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.channels_dropout"><code class="docutils literal notranslate"><span class="pre">channels_dropout()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.channels_permute"><code class="docutils literal notranslate"><span class="pre">channels_permute()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.channels_shuffle"><code class="docutils literal notranslate"><span class="pre">channels_shuffle()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.frequency_shift"><code class="docutils literal notranslate"><span class="pre">frequency_shift()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.ft_surrogate"><code class="docutils literal notranslate"><span class="pre">ft_surrogate()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.gaussian_noise"><code class="docutils literal notranslate"><span class="pre">gaussian_noise()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.mixup"><code class="docutils literal notranslate"><span class="pre">mixup()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.sensors_rotation"><code class="docutils literal notranslate"><span class="pre">sensors_rotation()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.sign_flip"><code class="docutils literal notranslate"><span class="pre">sign_flip()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.smooth_time_mask"><code class="docutils literal notranslate"><span class="pre">smooth_time_mask()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.functional.time_reverse"><code class="docutils literal notranslate"><span class="pre">time_reverse()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.transforms">braindecode.augmentation.transforms module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.BandstopFilter.training"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsDropout.training"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsShuffle.training"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.ChannelsSymmetry.training"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.operation"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FTSurrogate.training"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift"><code class="docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.operation"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.FrequencyShift.training"><code class="docutils literal notranslate"><span class="pre">FrequencyShift.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.operation"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.GaussianNoise.training"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.Mixup.training"><code class="docutils literal notranslate"><span class="pre">Mixup.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsXRotation"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsXRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsYRotation"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsYRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsZRotation"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SensorsZRotation.training"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip"><code class="docutils literal notranslate"><span class="pre">SignFlip</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip.operation"><code class="docutils literal notranslate"><span class="pre">SignFlip.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SignFlip.training"><code class="docutils literal notranslate"><span class="pre">SignFlip.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.SmoothTimeMask.training"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.transforms.TimeReverse.training"><code class="docutils literal notranslate"><span class="pre">TimeReverse.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.datasets.html">braindecode.datasets package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.datasets"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.datasets</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4.possible_subjects"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.possible_subjects</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.BNCI2014001</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001.datasets"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.datasets</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001.doc"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.datasets"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.datasets</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.description</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.target_transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.target_transform</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.description</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD"><code class="docutils literal notranslate"><span class="pre">HGD</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD.Schirrmeister2017"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD.Schirrmeister2017.data_path"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017.data_path()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">HGD.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD.datasets"><code class="docutils literal notranslate"><span class="pre">HGD.datasets</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD.doc"><code class="docutils literal notranslate"><span class="pre">HGD.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.MOABBDataset"><code class="docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.MOABBDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.MOABBDataset.datasets"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.SleepPhysionet.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.SleepPhysionet.datasets"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUH"><code class="docutils literal notranslate"><span class="pre">TUH</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUH.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUH.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUH.datasets"><code class="docutils literal notranslate"><span class="pre">TUH.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUHAbnormal.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.cumulative_sizes</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUHAbnormal.datasets"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.WindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.description</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.WindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.set_description()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.WindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_X_y"><code class="docutils literal notranslate"><span class="pre">create_from_X_y()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_mne_raw"><code class="docutils literal notranslate"><span class="pre">create_from_mne_raw()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.base">braindecode.datasets.base module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.datasets"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.datasets</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.target_transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.target_transform</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseConcatDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseDataset.description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.BaseDataset.transform"><code class="docutils literal notranslate"><span class="pre">BaseDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.set_description()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.EEGWindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">EEGWindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.description</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.set_description()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.base.WindowsDataset.transform"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.transform</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.bbci">braindecode.datasets.bbci module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset"><code class="docutils literal notranslate"><span class="pre">BBCIDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset.get_all_sensors"><code class="docutils literal notranslate"><span class="pre">BBCIDataset.get_all_sensors()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bbci.BBCIDataset.load"><code class="docutils literal notranslate"><span class="pre">BBCIDataset.load()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bbci.load_bbci_sets_from_folder"><code class="docutils literal notranslate"><span class="pre">load_bbci_sets_from_folder()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.bcicomp">braindecode.datasets.bcicomp module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.datasets"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.datasets</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.bcicomp.BCICompetitionIVDataset4.possible_subjects"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.possible_subjects</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.mne">braindecode.datasets.mne module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.mne.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.mne.create_from_mne_raw"><code class="docutils literal notranslate"><span class="pre">create_from_mne_raw()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.moabb">braindecode.datasets.moabb module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.BNCI2014001</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.datasets"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.datasets</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.BNCI2014001.doc"><code class="docutils literal notranslate"><span class="pre">BNCI2014001.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.HGD"><code class="docutils literal notranslate"><span class="pre">HGD</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.HGD.Schirrmeister2017"><code class="docutils literal notranslate"><span class="pre">HGD.Schirrmeister2017</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.HGD.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">HGD.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.HGD.datasets"><code class="docutils literal notranslate"><span class="pre">HGD.datasets</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.HGD.doc"><code class="docutils literal notranslate"><span class="pre">HGD.doc</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset"><code class="docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.MOABBDataset.datasets"><code class="docutils literal notranslate"><span class="pre">MOABBDataset.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.moabb.fetch_data_with_moabb"><code class="docutils literal notranslate"><span class="pre">fetch_data_with_moabb()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.sleep_physionet">braindecode.datasets.sleep_physionet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.sleep_physionet.SleepPhysionet.datasets"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet.datasets</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.tuh">braindecode.datasets.tuh module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUH"><code class="docutils literal notranslate"><span class="pre">TUH</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUH.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUH.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUH.datasets"><code class="docutils literal notranslate"><span class="pre">TUH.datasets</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal.cumulative_sizes"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.cumulative_sizes</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal.datasets"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal.datasets</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.xy">braindecode.datasets.xy module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.xy.create_from_X_y"><code class="docutils literal notranslate"><span class="pre">create_from_X_y()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.html">braindecode.datautil package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.mne">braindecode.datautil.mne module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.preprocess">braindecode.datautil.preprocess module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.serialization">braindecode.datautil.serialization module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datautil.html#braindecode.datautil.serialization.load_concat_dataset"><code class="docutils literal notranslate"><span class="pre">load_concat_dataset()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datautil.html#braindecode.datautil.serialization.save_concat_dataset"><code class="docutils literal notranslate"><span class="pre">save_concat_dataset()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.util">braindecode.datautil.util module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datautil.html#braindecode.datautil.util.ms_to_samples"><code class="docutils literal notranslate"><span class="pre">ms_to_samples()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datautil.html#braindecode.datautil.util.samples_to_ms"><code class="docutils literal notranslate"><span class="pre">samples_to_ms()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.windowers">braindecode.datautil.windowers module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.xy">braindecode.datautil.xy module</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.html">braindecode.models package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.atcnet">braindecode.models.atcnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.atcnet.ATCNet"><code class="docutils literal notranslate"><span class="pre">ATCNet</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.atcnet.ATCNet.forward"><code class="docutils literal notranslate"><span class="pre">ATCNet.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.atcnet.ATCNet.training"><code class="docutils literal notranslate"><span class="pre">ATCNet.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.base">braindecode.models.base module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.add_log_softmax"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.add_log_softmax</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.chs_info"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.chs_info</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.get_output_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_output_shape()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.get_torchinfo_statistics"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_torchinfo_statistics()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.input_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.input_shape</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.input_window_seconds"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.input_window_seconds</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.load_state_dict"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.load_state_dict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.mapping"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.mapping</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_chans"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_chans</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_outputs"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_outputs</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.n_times"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_times</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.sfreq"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.sfreq</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.EEGModuleMixin.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.to_dense_prediction_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.base.deprecated_args"><code class="docutils literal notranslate"><span class="pre">deprecated_args()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.deep4">braindecode.models.deep4 module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.deep4.Deep4Net"><code class="docutils literal notranslate"><span class="pre">Deep4Net</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.deepsleepnet">braindecode.models.deepsleepnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.deepsleepnet.DeepSleepNet"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.deepsleepnet.DeepSleepNet.forward"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegconformer">braindecode.models.eegconformer module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegconformer.EEGConformer"><code class="docutils literal notranslate"><span class="pre">EEGConformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegconformer.EEGConformer.forward"><code class="docutils literal notranslate"><span class="pre">EEGConformer.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegconformer.EEGConformer.get_fc_size"><code class="docutils literal notranslate"><span class="pre">EEGConformer.get_fc_size()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception">braindecode.models.eeginception module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eeginception.EEGInception"><code class="docutils literal notranslate"><span class="pre">EEGInception</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception_erp">braindecode.models.eeginception_erp module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eeginception_erp.EEGInceptionERP"><code class="docutils literal notranslate"><span class="pre">EEGInceptionERP</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception_mi">braindecode.models.eeginception_mi module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eeginception_mi.EEGInceptionMI"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eeginception_mi.EEGInceptionMI.forward"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegitnet">braindecode.models.eegitnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegitnet.EEGITNet"><code class="docutils literal notranslate"><span class="pre">EEGITNet</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegnet">braindecode.models.eegnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.bias"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.bias</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.dilation"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.dilation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.forward"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.groups"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.groups</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.in_channels"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.in_channels</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.kernel_size"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.kernel_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.out_channels"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.out_channels</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.output_padding"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.output_padding</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.padding"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.padding</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.padding_mode"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.padding_mode</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.stride"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.stride</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.transposed"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.transposed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.Conv2dWithConstraint.weight"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.EEGNetv1"><code class="docutils literal notranslate"><span class="pre">EEGNetv1</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegnet.EEGNetv4"><code class="docutils literal notranslate"><span class="pre">EEGNetv4</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegresnet">braindecode.models.eegresnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.eegresnet.EEGResNet"><code class="docutils literal notranslate"><span class="pre">EEGResNet</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.functions">braindecode.models.functions module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.functions.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.functions.safe_log"><code class="docutils literal notranslate"><span class="pre">safe_log()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.functions.square"><code class="docutils literal notranslate"><span class="pre">square()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.functions.squeeze_final_output"><code class="docutils literal notranslate"><span class="pre">squeeze_final_output()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.functions.transpose_time_to_spat"><code class="docutils literal notranslate"><span class="pre">transpose_time_to_spat()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.hybrid">braindecode.models.hybrid module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.hybrid.HybridNet"><code class="docutils literal notranslate"><span class="pre">HybridNet</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.hybrid.HybridNet.forward"><code class="docutils literal notranslate"><span class="pre">HybridNet.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.modules">braindecode.models.modules module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv.forward"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.AvgPool2dWithConv.training"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d"><code class="docutils literal notranslate"><span class="pre">CausalConv1d</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.bias"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.bias</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.dilation"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.dilation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.forward"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.groups"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.groups</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.in_channels"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.in_channels</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.kernel_size"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.kernel_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.out_channels"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.out_channels</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.output_padding"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.output_padding</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.padding"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.padding</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.padding_mode"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.padding_mode</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.stride"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.stride</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.transposed"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.transposed</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CausalConv1d.weight"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CombinedConv"><code class="docutils literal notranslate"><span class="pre">CombinedConv</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CombinedConv.forward"><code class="docutils literal notranslate"><span class="pre">CombinedConv.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.CombinedConv.training"><code class="docutils literal notranslate"><span class="pre">CombinedConv.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Ensure4d"><code class="docutils literal notranslate"><span class="pre">Ensure4d</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Ensure4d.forward"><code class="docutils literal notranslate"><span class="pre">Ensure4d.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Ensure4d.training"><code class="docutils literal notranslate"><span class="pre">Ensure4d.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Expression"><code class="docutils literal notranslate"><span class="pre">Expression</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Expression.forward"><code class="docutils literal notranslate"><span class="pre">Expression.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.Expression.training"><code class="docutils literal notranslate"><span class="pre">Expression.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper.forward"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.IntermediateOutputWrapper.training"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.MaxNormLinear"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.MaxNormLinear.forward"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.MaxNormLinear.in_features"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.in_features</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.MaxNormLinear.out_features"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.out_features</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.MaxNormLinear.weight"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.TimeDistributed"><code class="docutils literal notranslate"><span class="pre">TimeDistributed</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.TimeDistributed.forward"><code class="docutils literal notranslate"><span class="pre">TimeDistributed.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.modules.TimeDistributed.training"><code class="docutils literal notranslate"><span class="pre">TimeDistributed.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.shallow_fbcsp">braindecode.models.shallow_fbcsp module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.shallow_fbcsp.ShallowFBCSPNet"><code class="docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_blanco_2020">braindecode.models.sleep_stager_blanco_2020 module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_chambon_2018">braindecode.models.sleep_stager_chambon_2018 module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_eldele_2021">braindecode.models.sleep_stager_eldele_2021 module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.return_feats"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.return_feats</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.tcn">braindecode.models.tcn module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.Chomp1d"><code class="docutils literal notranslate"><span class="pre">Chomp1d</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.Chomp1d.extra_repr"><code class="docutils literal notranslate"><span class="pre">Chomp1d.extra_repr()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.Chomp1d.forward"><code class="docutils literal notranslate"><span class="pre">Chomp1d.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.Chomp1d.training"><code class="docutils literal notranslate"><span class="pre">Chomp1d.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.TCN"><code class="docutils literal notranslate"><span class="pre">TCN</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.TCN.forward"><code class="docutils literal notranslate"><span class="pre">TCN.forward()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.TemporalBlock"><code class="docutils literal notranslate"><span class="pre">TemporalBlock</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.TemporalBlock.forward"><code class="docutils literal notranslate"><span class="pre">TemporalBlock.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tcn.TemporalBlock.training"><code class="docutils literal notranslate"><span class="pre">TemporalBlock.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.tidnet">braindecode.models.tidnet module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.tidnet.TIDNet"><code class="docutils literal notranslate"><span class="pre">TIDNet</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tidnet.TIDNet.forward"><code class="docutils literal notranslate"><span class="pre">TIDNet.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.tidnet.TIDNet.num_features"><code class="docutils literal notranslate"><span class="pre">TIDNet.num_features</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.usleep">braindecode.models.usleep module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.usleep.USleep"><code class="docutils literal notranslate"><span class="pre">USleep</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#braindecode.models.usleep.USleep.forward"><code class="docutils literal notranslate"><span class="pre">USleep.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.util">braindecode.models.util module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.util.aggregate_probas"><code class="docutils literal notranslate"><span class="pre">aggregate_probas()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.util.get_output_shape"><code class="docutils literal notranslate"><span class="pre">get_output_shape()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html#braindecode.models.util.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">to_dense_prediction_model()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.html">braindecode.preprocessing package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Crop"><code class="docutils literal notranslate"><span class="pre">Crop</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Crop.fn"><code class="docutils literal notranslate"><span class="pre">Crop.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.DropChannels"><code class="docutils literal notranslate"><span class="pre">DropChannels</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.DropChannels.fn"><code class="docutils literal notranslate"><span class="pre">DropChannels.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Filter"><code class="docutils literal notranslate"><span class="pre">Filter</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Filter.fn"><code class="docutils literal notranslate"><span class="pre">Filter.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Pick"><code class="docutils literal notranslate"><span class="pre">Pick</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Pick.fn"><code class="docutils literal notranslate"><span class="pre">Pick.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Resample"><code class="docutils literal notranslate"><span class="pre">Resample</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Resample.fn"><code class="docutils literal notranslate"><span class="pre">Resample.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference"><code class="docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference.fn"><code class="docutils literal notranslate"><span class="pre">SetEEGReference.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows"><code class="docutils literal notranslate"><span class="pre">create_fixed_length_windows()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events"><code class="docutils literal notranslate"><span class="pre">create_windows_from_events()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_target_channels"><code class="docutils literal notranslate"><span class="pre">create_windows_from_target_channels()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_standardize"><code class="docutils literal notranslate"><span class="pre">exponential_moving_standardize()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess"><code class="docutils literal notranslate"><span class="pre">preprocess()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.mne_preprocess">braindecode.preprocessing.mne_preprocess module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Crop"><code class="docutils literal notranslate"><span class="pre">Crop</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Crop.fn"><code class="docutils literal notranslate"><span class="pre">Crop.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.DropChannels"><code class="docutils literal notranslate"><span class="pre">DropChannels</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.DropChannels.fn"><code class="docutils literal notranslate"><span class="pre">DropChannels.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Filter"><code class="docutils literal notranslate"><span class="pre">Filter</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Filter.fn"><code class="docutils literal notranslate"><span class="pre">Filter.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Pick"><code class="docutils literal notranslate"><span class="pre">Pick</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Pick.fn"><code class="docutils literal notranslate"><span class="pre">Pick.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Resample"><code class="docutils literal notranslate"><span class="pre">Resample</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.Resample.fn"><code class="docutils literal notranslate"><span class="pre">Resample.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.SetEEGReference"><code class="docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.mne_preprocess.SetEEGReference.fn"><code class="docutils literal notranslate"><span class="pre">SetEEGReference.fn</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess">braindecode.preprocessing.preprocess module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.exponential_moving_standardize"><code class="docutils literal notranslate"><span class="pre">exponential_moving_standardize()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess.preprocess"><code class="docutils literal notranslate"><span class="pre">preprocess()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.windowers">braindecode.preprocessing.windowers module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_fixed_length_windows"><code class="docutils literal notranslate"><span class="pre">create_fixed_length_windows()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_windows_from_events"><code class="docutils literal notranslate"><span class="pre">create_windows_from_events()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_windows_from_target_channels"><code class="docutils literal notranslate"><span class="pre">create_windows_from_target_channels()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.samplers.html">braindecode.samplers package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler.sample_class"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler.sample_class()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#id0"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler.sample_window"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_window()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler.presample"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler.presample()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.SequenceSampler"><code class="docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.SequenceSampler.info"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.info</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.SequenceSampler.file_ids"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.file_ids</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#module-braindecode.samplers.base">braindecode.samplers.base module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.BalancedSequenceSampler.sample_class"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler.sample_class()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#id4"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.RecordingSampler.sample_window"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_window()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.SequenceSampler"><code class="docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.SequenceSampler.info"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.info</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.base.SequenceSampler.file_ids"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.file_ids</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.samplers.html#module-braindecode.samplers.ssl">braindecode.samplers.ssl module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.ssl.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.ssl.RelativePositioningSampler.presample"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler.presample()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.training.html">braindecode.training package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedLoss"><code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedLoss.forward"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.forward()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedLoss.training"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.PostEpochTrainScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.TimeSeriesLoss"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.TimeSeriesLoss.forward"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.forward()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.TimeSeriesLoss.training"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.mixup_criterion"><code class="docutils literal notranslate"><span class="pre">mixup_criterion()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.predict_trials"><code class="docutils literal notranslate"><span class="pre">predict_trials()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#braindecode.training.trial_preds_from_window_preds"><code class="docutils literal notranslate"><span class="pre">trial_preds_from_window_preds()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.callbacks">braindecode.training.callbacks module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.callbacks.MaxNormConstraintCallback"><code class="docutils literal notranslate"><span class="pre">MaxNormConstraintCallback</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.callbacks.MaxNormConstraintCallback.on_batch_end"><code class="docutils literal notranslate"><span class="pre">MaxNormConstraintCallback.on_batch_end()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.losses">braindecode.training.losses module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.CroppedLoss"><code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.CroppedLoss.forward"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.CroppedLoss.training"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.TimeSeriesLoss"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.TimeSeriesLoss.forward"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.TimeSeriesLoss.training"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.losses.mixup_criterion"><code class="docutils literal notranslate"><span class="pre">mixup_criterion()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.scoring">braindecode.training.scoring module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.PostEpochTrainScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.predict_trials"><code class="docutils literal notranslate"><span class="pre">predict_trials()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html#braindecode.training.scoring.trial_preds_from_window_preds"><code class="docutils literal notranslate"><span class="pre">trial_preds_from_window_preds()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.html">braindecode.visualization package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.compute_amplitude_gradients"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.plot_confusion_matrix"><code class="docutils literal notranslate"><span class="pre">plot_confusion_matrix()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.visualization.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.visualization.html#module-braindecode.visualization.confusion_matrices">braindecode.visualization.confusion_matrices module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.confusion_matrices.plot_confusion_matrix"><code class="docutils literal notranslate"><span class="pre">plot_confusion_matrix()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.visualization.html#module-braindecode.visualization.gradients">braindecode.visualization.gradients module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.gradients.compute_amplitude_gradients"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.gradients.compute_amplitude_gradients_for_X"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients_for_X()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.classifier">
+<span id="braindecode-classifier-module"></span><h2>braindecode.classifier module<a class="headerlink" href="#module-braindecode.classifier" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.classifier.</span></span><span class="sig-name descname"><span class="pre">EEGClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion=&lt;class</span> <span class="pre">'torch.nn.modules.loss.CrossEntropyLoss'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L22-L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_EEGNeuralNet</span></code>, <a class="reference external" href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNetClassifier</span></code></a></p>
+<p>Classifier that does not assume softmax activation.
+Calls loss function directly without applying log or anything.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.NLLLoss</em><em>)</em>) – Negative log likelihood loss. Note that the module should return
+probabilities, the log is applied during <code class="docutils literal notranslate"><span class="pre">get_loss</span></code>.</p></li>
+<li><p><strong>classes</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default=None</em><em>)</em>) – If None, the <code class="docutils literal notranslate"><span class="pre">classes_</span></code> attribute will be inferred from the
+<code class="docutils literal notranslate"><span class="pre">y</span></code> data passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If a non-empty list is passed,
+that list will be returned as <code class="docutils literal notranslate"><span class="pre">classes_</span></code>. If the initial
+skorch behavior should be restored, i.e. raising an
+<code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>, pass an empty list.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.classes_">
+<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.classes_" title="Link to this definition">#</a></dt>
+<dd><p>A list of class labels known to the classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>array, shape (n_classes, )</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&quot;Classifier</span> <span class="pre">that</span> <span class="pre">does</span> <span class="pre">not</span> <span class="pre">assume</span> <span class="pre">softmax</span> <span class="pre">activation.\n</span>&#160;&#160;&#160; <span class="pre">Calls</span> <span class="pre">loss</span> <span class="pre">function</span> <span class="pre">directly</span> <span class="pre">without</span> <span class="pre">applying</span> <span class="pre">log</span> <span class="pre">or</span> <span class="pre">anything.\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">module:</span> <span class="pre">str</span> <span class="pre">or</span> <span class="pre">torch</span> <span class="pre">Module</span> <span class="pre">(class</span> <span class="pre">or</span> <span class="pre">instance)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Either</span> <span class="pre">the</span> <span class="pre">name</span> <span class="pre">of</span> <span class="pre">one</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">braindecode</span> <span class="pre">models</span> <span class="pre">(see\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:obj:`braindecode.models.util.models_dict`)</span> <span class="pre">or</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">PyTorch</span> <span class="pre">module.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">When</span> <span class="pre">passing</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">torch</span> <span class="pre">module,</span> <span class="pre">uninstantiated</span> <span class="pre">class</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">preferred,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">although</span> <span class="pre">instantiated</span> <span class="pre">modules</span> <span class="pre">will</span> <span class="pre">also</span> <span class="pre">work.\n\n</span>&#160;&#160;&#160; <span class="pre">cropped:</span> <span class="pre">bool</span> <span class="pre">(default=False)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">torch</span> <span class="pre">model</span> <span class="pre">passed</span> <span class="pre">to</span> <span class="pre">this</span> <span class="pre">class</span> <span class="pre">is</span> <span class="pre">cropped</span> <span class="pre">or</span> <span class="pre">not.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Currently</span> <span class="pre">used</span> <span class="pre">for</span> <span class="pre">callbacks</span> <span class="pre">definition.\n\n</span>&#160;&#160;&#160; <span class="pre">callbacks:</span> <span class="pre">None</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">Callback</span> <span class="pre">instances</span> <span class="pre">(default=None)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">More</span> <span class="pre">callbacks,</span> <span class="pre">in</span> <span class="pre">addition</span> <span class="pre">to</span> <span class="pre">those</span> <span class="pre">returned</span> <span class="pre">by\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``get_default_callbacks``.</span> <span class="pre">Each</span> <span class="pre">callback</span> <span class="pre">should</span> <span class="pre">inherit</span> <span class="pre">from\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:class:`skorch.callbacks.Callback`.</span> <span class="pre">If</span> <span class="pre">not</span> <span class="pre">``None``,</span> <span class="pre">callbacks</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">a\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">specifying</span> <span class="pre">`sklearn`</span> <span class="pre">scoring</span> <span class="pre">functions</span> <span class="pre">(for</span> <span class="pre">scoring\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">functions</span> <span class="pre">names</span> <span class="pre">see:</span> <span class="pre">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">or</span> <span class="pre">a</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">callbacks</span> <span class="pre">where</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">names</span> <span class="pre">are</span> <span class="pre">inferred</span> <span class="pre">from</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">class</span> <span class="pre">name.</span> <span class="pre">Name</span> <span class="pre">conflicts</span> <span class="pre">are</span> <span class="pre">resolved</span> <span class="pre">by</span> <span class="pre">appending</span> <span class="pre">a</span> <span class="pre">count</span> <span class="pre">suffix\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">starting</span> <span class="pre">with</span> <span class="pre">1,</span> <span class="pre">e.g.</span> <span class="pre">``EpochScoring_1``.</span> <span class="pre">Alternatively,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">a</span> <span class="pre">tuple</span> <span class="pre">``(name,</span> <span class="pre">callback)``</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">passed,</span> <span class="pre">where</span> <span class="pre">``name``\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">should</span> <span class="pre">be</span> <span class="pre">unique.</span> <span class="pre">Callbacks</span> <span class="pre">may</span> <span class="pre">or</span> <span class="pre">may</span> <span class="pre">not</span> <span class="pre">be</span> <span class="pre">instantiated.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">The</span> <span class="pre">callback</span> <span class="pre">name</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">used</span> <span class="pre">to</span> <span class="pre">set</span> <span class="pre">parameters</span> <span class="pre">on</span> <span class="pre">specific\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">callbacks</span> <span class="pre">(e.g.,</span> <span class="pre">for</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">with</span> <span class="pre">name</span> <span class="pre">``'print_log'``,</span> <span class="pre">use\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``net.set_params(callbacks__print_log__keys_ignored=['epoch',\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">'train_loss'])``).\n\n</span>&#160;&#160;&#160; <span class="pre">iterator_train__shuffle:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">will</span> <span class="pre">be</span> <span class="pre">shuffled.</span> <span class="pre">As</span> <span class="pre">skorch</span> <span class="pre">does</span> <span class="pre">not\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">shuffle</span> <span class="pre">the</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">by</span> <span class="pre">default</span> <span class="pre">this</span> <span class="pre">one</span> <span class="pre">overwrites</span> <span class="pre">this</span> <span class="pre">option.\n\n</span>&#160;&#160;&#160; <span class="pre">aggregate_predictions:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Whether</span> <span class="pre">to</span> <span class="pre">average</span> <span class="pre">cropped</span> <span class="pre">predictions</span> <span class="pre">to</span> <span class="pre">obtain</span> <span class="pre">window</span> <span class="pre">predictions.</span> <span class="pre">Used</span> <span class="pre">only</span> <span class="pre">in</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">cropped</span> <span class="pre">mode.\n\n</span>&#160;&#160;&#160; <span class="pre">&quot;</span></em><a class="headerlink" href="#braindecode.classifier.EEGClassifier.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L90-L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_loss">
+<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L142-L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_loss" title="Link to this definition">#</a></dt>
+<dd><p>Return the loss for this batch by calling NeuralNet get_loss.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_pred</strong> (<em>torch tensor</em>) – Predicted target values</p></li>
+<li><p><strong>y_true</strong> (<em>torch tensor</em>) – True target values.</p></li>
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L174-L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict" title="Link to this definition">#</a></dt>
+<dd><p>Return class labels for samples in X.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_pred</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L97-L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L200-L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.eegneuralnet">
+<span id="braindecode-eegneuralnet-module"></span><h2>braindecode.eegneuralnet module<a class="headerlink" href="#module-braindecode.eegneuralnet" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.regressor">
+<span id="braindecode-regressor-module"></span><h2>braindecode.regressor module<a class="headerlink" href="#module-braindecode.regressor" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.regressor.</span></span><span class="sig-name descname"><span class="pre">EEGRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L19-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">_EEGNeuralNet</span></code>, <a class="reference external" href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNetRegressor</span></code></a></p>
+<p>Regressor that calls loss function directly.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+uninstantiated class should be passed, although instantiated
+modules will also work.</p></li>
+<li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.MSELoss</em><em>)</em>) – Mean squared error loss.</p></li>
+<li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
+module</p></li>
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+may keyboard-interrupt training at any time.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
+which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
+a single batch with all the data will be used during training
+and validation.</p></li>
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+training data.</p></li>
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+validation and test data, i.e. during inference.</p></li>
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
+pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
+<code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> methods. The provided dataset should be capable of
+dealing with a lot of data types out of the box, so only change
+this if your data is not supported. You should generally pass the
+uninitialized <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> class and define additional arguments to
+X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
+to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
+arguments may be passed.</p></li>
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
+should be a function or callable that is called with X and y
+data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
+those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
+any of the callbacks, not even the default callbacks.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
+addition to the default callbacks. Each callback should be an
+instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
+<p>Callback names are inferred from the class
+name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p>
+</p></li>
+<li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
+‘auto’, infers the correct nonlinearity based on the criterion
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+or if the parameter is None, just use the identity
+function. Don’t pass a lambda function if you want the net to be
+pickleable.</p>
+<p>In case a callable is passed, it should accept the output of the
+module (the first output if there is more than one), which is a
+PyTorch tensor, and return the transformed PyTorch tensor.</p>
+<p>This can be useful, e.g., when
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code>
+should return probabilities but a criterion is used that does
+not expect probabilities. In that case, the module can return
+whatever is required by the criterion and the
+<code class="docutils literal notranslate"><span class="pre">predict_nonlinearity</span></code> transforms this output into
+probabilities.</p>
+<p>The nonlinearity is applied only when calling
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict()</span></code> or
+<code class="xref py py-func docutils literal notranslate"><span class="pre">predict_proba()</span></code> but
+not anywhere else – notably, the loss is unaffected by this
+nonlinearity.</p>
+</p></li>
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+module (cold start) or whether the module should be trained
+further (warm start).</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+the net and its callbacks. By setting this value to 0, e.g. the
+summary scores at the end of each epoch are no longer printed.
+This can be useful when running a hyperparameter search. The
+summary scores are always logged in the history attribute,
+regardless of the verbose setting.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+Currently used for callbacks definition.</p></li>
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
+functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
+or a list of callbacks where the callback names are inferred from the
+class name. Name conflicts are resolved by appending a count suffix
+starting with 1, e.g. <code class="docutils literal notranslate"><span class="pre">EpochScoring_1</span></code>. Alternatively,
+a tuple <code class="docutils literal notranslate"><span class="pre">(name,</span> <span class="pre">callback)</span></code> can be passed, where <code class="docutils literal notranslate"><span class="pre">name</span></code>
+should be unique. Callbacks may or may not be instantiated.
+The callback name can be used to set parameters on specific
+callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
+<code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
+<span class="pre">'train_loss'])</span></code>).</p></li>
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+shuffle the train dataset by default this one overwrites this option.</p></li>
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+cropped mode.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.prefixes_">
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.prefixes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains the prefixes to special parameters. E.g., since there
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
+<dd><p>Contains a list of all attribute prefixes whose values depend on a
+CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
+is unpickled on a machine without CUDA or with CUDA disabled, the
+listed attributes are mapped to CPU.  Expand this list if you
+want to add other cuda-dependent attributes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.initialized_">
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.initialized_" title="Link to this definition">#</a></dt>
+<dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.module_">
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.module_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch module (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.criterion_">
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.criterion_" title="Link to this definition">#</a></dt>
+<dd><p>The instantiated criterion.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch criterion (instance)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.callbacks_">
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.callbacks_" title="Link to this definition">#</a></dt>
+<dd><p>The complete (i.e. default and other), initialized callbacks, in
+a tuple with unique names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._modules">
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._modules" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all modules that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._criteria">
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._criteria" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all criteria that are torch modules. This list is
+collected dynamically when the net is initialized. Typically, there is no
+reason for a user to modify this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._optimizers">
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._optimizers" title="Link to this definition">#</a></dt>
+<dd><p>List of names of all optimizers. This list is collected dynamically when
+the net is initialized. Typically, there is no reason for a user to modify
+this list.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.doc">
+<span class="sig-name descname"><span class="pre">doc</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&quot;Regressor</span> <span class="pre">that</span> <span class="pre">calls</span> <span class="pre">loss</span> <span class="pre">function</span> <span class="pre">directly.\n\n</span>&#160;&#160;&#160; <span class="pre">Parameters\n</span>&#160;&#160;&#160; <span class="pre">----------\n</span>&#160;&#160;&#160; <span class="pre">module:</span> <span class="pre">str</span> <span class="pre">or</span> <span class="pre">torch</span> <span class="pre">Module</span> <span class="pre">(class</span> <span class="pre">or</span> <span class="pre">instance)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Either</span> <span class="pre">the</span> <span class="pre">name</span> <span class="pre">of</span> <span class="pre">one</span> <span class="pre">of</span> <span class="pre">the</span> <span class="pre">braindecode</span> <span class="pre">models</span> <span class="pre">(see\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:obj:`braindecode.models.util.models_dict`)</span> <span class="pre">or</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">PyTorch</span> <span class="pre">module.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">When</span> <span class="pre">passing</span> <span class="pre">directly</span> <span class="pre">a</span> <span class="pre">torch</span> <span class="pre">module,</span> <span class="pre">uninstantiated</span> <span class="pre">class</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">preferred,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">although</span> <span class="pre">instantiated</span> <span class="pre">modules</span> <span class="pre">will</span> <span class="pre">also</span> <span class="pre">work.\n\n</span>&#160;&#160;&#160; <span class="pre">cropped:</span> <span class="pre">bool</span> <span class="pre">(default=False)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">torch</span> <span class="pre">model</span> <span class="pre">passed</span> <span class="pre">to</span> <span class="pre">this</span> <span class="pre">class</span> <span class="pre">is</span> <span class="pre">cropped</span> <span class="pre">or</span> <span class="pre">not.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Currently</span> <span class="pre">used</span> <span class="pre">for</span> <span class="pre">callbacks</span> <span class="pre">definition.\n\n</span>&#160;&#160;&#160; <span class="pre">callbacks:</span> <span class="pre">None</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">or</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">Callback</span> <span class="pre">instances</span> <span class="pre">(default=None)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">More</span> <span class="pre">callbacks,</span> <span class="pre">in</span> <span class="pre">addition</span> <span class="pre">to</span> <span class="pre">those</span> <span class="pre">returned</span> <span class="pre">by\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``get_default_callbacks``.</span> <span class="pre">Each</span> <span class="pre">callback</span> <span class="pre">should</span> <span class="pre">inherit</span> <span class="pre">from\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">:class:`skorch.callbacks.Callback`.</span> <span class="pre">If</span> <span class="pre">not</span> <span class="pre">``None``,</span> <span class="pre">callbacks</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">a\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">list</span> <span class="pre">of</span> <span class="pre">strings</span> <span class="pre">specifying</span> <span class="pre">`sklearn`</span> <span class="pre">scoring</span> <span class="pre">functions</span> <span class="pre">(for</span> <span class="pre">scoring\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">functions</span> <span class="pre">names</span> <span class="pre">see:</span> <span class="pre">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">or</span> <span class="pre">a</span> <span class="pre">list</span> <span class="pre">of</span> <span class="pre">callbacks</span> <span class="pre">where</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">names</span> <span class="pre">are</span> <span class="pre">inferred</span> <span class="pre">from</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">class</span> <span class="pre">name.</span> <span class="pre">Name</span> <span class="pre">conflicts</span> <span class="pre">are</span> <span class="pre">resolved</span> <span class="pre">by</span> <span class="pre">appending</span> <span class="pre">a</span> <span class="pre">count</span> <span class="pre">suffix\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">starting</span> <span class="pre">with</span> <span class="pre">1,</span> <span class="pre">e.g.</span> <span class="pre">``EpochScoring_1``.</span> <span class="pre">Alternatively,\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">a</span> <span class="pre">tuple</span> <span class="pre">``(name,</span> <span class="pre">callback)``</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">passed,</span> <span class="pre">where</span> <span class="pre">``name``\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">should</span> <span class="pre">be</span> <span class="pre">unique.</span> <span class="pre">Callbacks</span> <span class="pre">may</span> <span class="pre">or</span> <span class="pre">may</span> <span class="pre">not</span> <span class="pre">be</span> <span class="pre">instantiated.\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">The</span> <span class="pre">callback</span> <span class="pre">name</span> <span class="pre">can</span> <span class="pre">be</span> <span class="pre">used</span> <span class="pre">to</span> <span class="pre">set</span> <span class="pre">parameters</span> <span class="pre">on</span> <span class="pre">specific\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">callbacks</span> <span class="pre">(e.g.,</span> <span class="pre">for</span> <span class="pre">the</span> <span class="pre">callback</span> <span class="pre">with</span> <span class="pre">name</span> <span class="pre">``'print_log'``,</span> <span class="pre">use\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">``net.set_params(callbacks__print_log__keys_ignored=['epoch',\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">'train_loss'])``).\n\n</span>&#160;&#160;&#160; <span class="pre">iterator_train__shuffle:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Defines</span> <span class="pre">whether</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">will</span> <span class="pre">be</span> <span class="pre">shuffled.</span> <span class="pre">As</span> <span class="pre">skorch</span> <span class="pre">does</span> <span class="pre">not\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">shuffle</span> <span class="pre">the</span> <span class="pre">train</span> <span class="pre">dataset</span> <span class="pre">by</span> <span class="pre">default</span> <span class="pre">this</span> <span class="pre">one</span> <span class="pre">overwrites</span> <span class="pre">this</span> <span class="pre">option.\n\n</span>&#160;&#160;&#160; <span class="pre">aggregate_predictions:</span> <span class="pre">bool</span> <span class="pre">(default=True)\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">Whether</span> <span class="pre">to</span> <span class="pre">average</span> <span class="pre">cropped</span> <span class="pre">predictions</span> <span class="pre">to</span> <span class="pre">obtain</span> <span class="pre">window</span> <span class="pre">predictions.</span> <span class="pre">Used</span> <span class="pre">only</span> <span class="pre">in</span> <span class="pre">the\n</span>&#160;&#160;&#160;&#160;&#160;&#160;&#160; <span class="pre">cropped</span> <span class="pre">mode.\n\n</span>&#160;&#160;&#160; <span class="pre">&quot;</span></em><a class="headerlink" href="#braindecode.regressor.EEGRegressor.doc" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L171-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.fit" title="Link to this definition">#</a></dt>
+<dd><p>Initialize and fit the module.</p>
+<p>If the module was already initialized, by calling fit, the
+module will be re-initialized (unless <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> is True).
+If possible, signal-related parameters are inferred from the
+data and passed to the module at initialisation.
+Depending on the type of input passed, the following parameters
+are inferred:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>mne.Epochs: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code>, <code class="docutils literal notranslate"><span class="pre">chs_info</span></code>,
+<code class="docutils literal notranslate"><span class="pre">sfreq</span></code>, <code class="docutils literal notranslate"><span class="pre">input_window_seconds</span></code></p></li>
+<li><p>numpy array: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>WindowsDataset with <code class="docutils literal notranslate"><span class="pre">targets_from='metadata'</span></code>
+(or BaseConcatDataset of such datasets): <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>other Dataset: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code></p></li>
+<li><p>other types: no parameters are inferred.</p></li>
+</ul>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul>
+<li><p>mne.Epochs</p></li>
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p></li>
+<li><p><strong>y</strong> (<em>target data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – The same data types as for <code class="docutils literal notranslate"><span class="pre">X</span></code> are supported. If your X is
+a Dataset that contains the target, <code class="docutils literal notranslate"><span class="pre">y</span></code> may be set to
+None.</p></li>
+<li><p><strong>**fit_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Additional parameters passed to the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of
+the module and to the <code class="docutils literal notranslate"><span class="pre">self.train_split</span></code> call.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L75-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L82-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_proba" title="Link to this definition">#</a></dt>
+<dd><p>Return the output of the module’s forward method as a numpy
+array. In case of cropped decoding returns averaged values for
+each trial.</p>
+<p>If the module’s forward method returns multiple outputs as a
+tuple, it is assumed that the first output contains the
+relevant information and the other values are ignored.
+If all values are relevant or module’s output for each crop
+is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
+instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>numpy arrays</p></li>
+<li><p>torch tensors</p></li>
+<li><p>pandas DataFrame or Series</p></li>
+<li><p>scipy sparse CSR matrices</p></li>
+<li><p>a dictionary of the former three</p></li>
+<li><p>a list/tuple of the former three</p></li>
+<li><p>a Dataset</p></li>
+</ul>
+</div></blockquote>
+<p>If this doesn’t work with your data, you have to pass a
+<code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
+</p>
+</dd>
+</dl>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Regressors predict regression targets, so output of this method
+can’t be interpreted as probabilities. We advise you to use
+<cite>predict</cite> method instead of <cite>predict_proba</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>y_proba</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>numpy ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_trials">
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L132-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.util">
+<span id="braindecode-util-module"></span><h2>braindecode.util module<a class="headerlink" href="#module-braindecode.util" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.util.ThrowAwayIndexLoader">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">ThrowAwayIndexLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_regression</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L345-L369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.ThrowAwayIndexLoader" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.corr">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">corr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L119-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.corr" title="Link to this definition">#</a></dt>
+<dd><p>Computes correlation only between terms of a and terms of b, not within
+a and b.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>2darray</em><em>, </em><em>features x samples</em>) – </p></li>
+<li><p><strong>b</strong> (<em>2darray</em><em>, </em><em>features x samples</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Correlation between features in x and features in y</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.cov">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L139-L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.cov" title="Link to this definition">#</a></dt>
+<dd><p>Computes covariance only between terms of a and terms of b, not within
+a and b.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>a</strong> (<em>2darray</em><em>, </em><em>features x samples</em>) – </p></li>
+<li><p><strong>b</strong> (<em>2darray</em><em>, </em><em>features x samples</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Covariance between features in x and features in y</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.create_mne_dummy_raw">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">create_mne_dummy_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_anns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savedir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fif'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L271-L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.create_mne_dummy_raw" title="Link to this definition">#</a></dt>
+<dd><p>Create an mne.io.RawArray with fake data, and optionally save it.</p>
+<p>This will overwrite already existing files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of samples.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency.</p></li>
+<li><p><strong>include_anns</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, also create annotations.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><em>None</em>) – List of descriptions used for creating annotations. It should contain
+10 elements.</p></li>
+<li><p><strong>savedir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – If provided as a string, the file will be saved under that directory.</p></li>
+<li><p><strong>save_format</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – If <cite>savedir</cite> is provided, this specifies the file format the data should
+be saved to. Can be ‘raw’ or ‘hdf5’, or a list containing both.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>RandomState</em>) – Random state for the generation of random data.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>raw</strong> (<em>mne.io.Raw</em>) – The created Raw object.</p></li>
+<li><p><strong>save_fname</strong> (<em>dict | None</em>) – Dictionary containing the name the raw data was saved to.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.get_balanced_batches">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">get_balanced_batches</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rng</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_batches</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L218-L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.get_balanced_batches" title="Link to this definition">#</a></dt>
+<dd><p>Create indices for batches balanced in size
+(batches will have maximum size difference of 1).
+Supply either batch size or number of batches. Resulting batches
+will not have the given batch size but rather the next largest batch size
+that allows to split the set into balanced batches (maximum size difference 1).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of set.</p></li>
+<li><p><strong>rng</strong> (<em>RandomState</em>) – </p></li>
+<li><p><strong>shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to shuffle indices before splitting set.</p></li>
+<li><p><strong>n_batches</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>batches</strong> – Indices for each batch.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.np_to_th">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">np_to_th</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">tensor_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L73-L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.np_to_th" title="Link to this definition">#</a></dt>
+<dd><p>Convenience function to transform numpy array to <cite>torch.Tensor</cite>.</p>
+<p>Converts <cite>X</cite> to ndarray using asarray if necessary.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> (<em>ndarray</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> or </em><em>number</em>) – Input arrays</p></li>
+<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – passed on to Variable constructor</p></li>
+<li><p><strong>dtype</strong> (<em>numpy dtype</em><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>var_kwargs</strong> – passed on to Variable constructor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>var</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><cite>torch.Tensor</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.np_to_var">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">np_to_var</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">tensor_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L63-L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.np_to_var" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.read_all_file_names">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">read_all_file_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extension</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L393-L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.read_all_file_names" title="Link to this definition">#</a></dt>
+<dd><p>Read all files with specified extension from given path and sorts them
+based on a given sorting key.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>directory</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory to be searched for files of the specified type.</p></li>
+<li><p><strong>extension</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – File extension, i.e. “.edf” or “.txt”.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>file_paths</strong> – List of all files found in (sub)directories of path.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a>(<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.set_random_seeds">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">set_random_seeds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudnn_benchmark</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L17-L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.set_random_seeds" title="Link to this definition">#</a></dt>
+<dd><p>Set seeds for python random module numpy.random and torch.</p>
+<p>For more details about reproducibility in pytorch see
+<a class="reference external" href="https://pytorch.org/docs/stable/notes/randomness.html">https://pytorch.org/docs/stable/notes/randomness.html</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random seed.</p></li>
+<li><p><strong>cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to set cuda seed with torch.</p></li>
+<li><p><strong>cudnn_benchmark</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=None</em><em>)</em>) – Whether pytorch will use cudnn benchmark. When set to <cite>None</cite> it will not modify
+torch.backends.cudnn.benchmark (displays warning in the case of possible lack of
+reproducibility). When set to True, results may not be reproducible (no warning displayed).
+When set to False it may slow down computations.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>In some cases setting environment variable <cite>PYTHONHASHSEED</cite> may be needed before running a
+script to ensure full reproducibility. See
+<a class="reference external" href="https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14">https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14</a></p>
+<p>Using this function may not ensure full reproducibility of the results as we do not set
+<cite>torch.use_deterministic_algorithms(True)</cite>.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.th_to_np">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">th_to_np</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">var</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L111-L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.th_to_np" title="Link to this definition">#</a></dt>
+<dd><p>Convenience function to transform <cite>torch.Tensor</cite> to numpy
+array.</p>
+<p>Should work both for CPU and GPU.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.update_estimator_docstring">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">update_estimator_docstring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">docstring</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L372-L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.update_estimator_docstring" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.var_to_np">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">var_to_np</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">var</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L106-L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.var_to_np" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.util.wrap_reshape_apply_fn">
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">wrap_reshape_apply_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stat_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis_b</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L171-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.wrap_reshape_apply_fn" title="Link to this definition">#</a></dt>
+<dd><p>Reshape two nd-arrays into 2d-arrays, apply function and reshape
+result back.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>stat_fn</strong> (<em>function</em>) – Function to apply to 2d-arrays</p></li>
+<li><p><strong>a</strong> (<em>nd-array: nd-array</em>) – </p></li>
+<li><p><strong>b</strong> (<em>nd-array</em>) – </p></li>
+<li><p><strong>axis_a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – sample axis</p></li>
+<li><p><strong>axis_b</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – sample axis</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>result</strong> – The result reshaped to remaining_dims_a + remaining_dims_b</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>nd-array</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.version">
+<span id="braindecode-version-module"></span><h2>braindecode.version module<a class="headerlink" href="#module-braindecode.version" title="Link to this heading">#</a></h2>
+</section>
+</section>
+
+
+  
+</div>
+
+              
+              
+                <footer class="bd-footer-article">
+                  
+<div class="footer-article-items footer-article__inner">
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+    <i class="fa-solid fa-list"></i> On this page
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.doc"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.doc</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.doc"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.doc</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#subpackages">Subpackages</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.classifier">braindecode.classifier module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.doc"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.doc</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.eegneuralnet">braindecode.eegneuralnet module</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.regressor">braindecode.regressor module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.doc"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.doc</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.util">braindecode.util module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.ThrowAwayIndexLoader"><code class="docutils literal notranslate"><span class="pre">ThrowAwayIndexLoader</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.corr"><code class="docutils literal notranslate"><span class="pre">corr()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.cov"><code class="docutils literal notranslate"><span class="pre">cov()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.create_mne_dummy_raw"><code class="docutils literal notranslate"><span class="pre">create_mne_dummy_raw()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.get_balanced_batches"><code class="docutils literal notranslate"><span class="pre">get_balanced_batches()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.np_to_th"><code class="docutils literal notranslate"><span class="pre">np_to_th()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.np_to_var"><code class="docutils literal notranslate"><span class="pre">np_to_var()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.read_all_file_names"><code class="docutils literal notranslate"><span class="pre">read_all_file_names()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.set_random_seeds"><code class="docutils literal notranslate"><span class="pre">set_random_seeds()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.th_to_np"><code class="docutils literal notranslate"><span class="pre">th_to_np()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.update_estimator_docstring"><code class="docutils literal notranslate"><span class="pre">update_estimator_docstring()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.var_to_np"><code class="docutils literal notranslate"><span class="pre">var_to_np()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.util.wrap_reshape_apply_fn"><code class="docutils literal notranslate"><span class="pre">wrap_reshape_apply_fn()</span></code></a></li>
+</ul>
+</li>
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+  <section id="module-braindecode.models">
+<span id="braindecode-models-package"></span><h1>braindecode.models package<a class="headerlink" href="#module-braindecode.models" title="Link to this heading">#</a></h1>
+<p>Some predefined network architectures for EEG decoding.</p>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.models.atcnet">
+<span id="braindecode-models-atcnet-module"></span><h2>braindecode.models.atcnet module<a class="headerlink" href="#module-braindecode.models.atcnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.atcnet.ATCNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.atcnet.</span></span><span class="sig-name descname"><span class="pre">ATCNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_depth_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_head_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_num_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm_const</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L14-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.atcnet.ATCNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>ATCNet model from <a class="reference internal" href="#r2748c43c01db-1" id="id1">[1]</a></p>
+<p>Pytorch implementation based on official tensorflow code <a class="reference internal" href="#r2748c43c01db-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Time length of inputs, in seconds. Defaults to 4.5 s, as in BCI-IV 2a
+dataset.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Sampling frequency of the inputs, in Hz. Default to 250 Hz, as in
+BCI-IV 2a dataset.</p></li>
+<li><p><strong>conv_block_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number temporal filters in the first convolutional layer of the
+convolutional block, denoted F1 in figure 2 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id3">[1]</a>. Defaults
+to 16 as in <a class="reference internal" href="#r2748c43c01db-1" id="id4">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the first convolutional layer of the
+convolutional block, denoted Kc in table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id5">[1]</a>. Defaults
+to 64 as in <a class="reference internal" href="#r2748c43c01db-1" id="id6">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the last convolutional layer of the
+convolutional block. Defaults to 16 as in <a class="reference internal" href="#r2748c43c01db-1" id="id7">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block.
+Defaults to 8 as in <a class="reference internal" href="#r2748c43c01db-1" id="id8">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block,
+denoted P2 in table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id9">[1]</a>. Defaults to 7 as in <a class="reference internal" href="#r2748c43c01db-1" id="id10">[1]</a>.</p></li>
+<li><p><strong>conv_block_depth_mult</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier of depthwise convolution in the convolutional block,
+denoted D in table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id11">[1]</a>. Defaults to 2 as in <a class="reference internal" href="#r2748c43c01db-1" id="id12">[1]</a>.</p></li>
+<li><p><strong>conv_block_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the convolution block, denoted pc in
+table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id13">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2748c43c01db-1" id="id14">[1]</a>.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sliding windows, denoted n in <a class="reference internal" href="#r2748c43c01db-1" id="id15">[1]</a>. Defaults to 5 as in <a class="reference internal" href="#r2748c43c01db-1" id="id16">[1]</a>.</p></li>
+<li><p><strong>att_head_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Embedding dimension used in each self-attention head, denoted dh in
+table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id17">[1]</a>. Defaults to 8 as in <a class="reference internal" href="#r2748c43c01db-1" id="id18">[1]</a>.</p></li>
+<li><p><strong>att_num_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads, denoted H in table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id19">[1]</a>.
+Defaults to 2 as in [<a href="#id94"><span class="problematic" id="id95">1_</span></a>.</p></li>
+<li><p><strong>att_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the attention block, denoted pa in table 1
+of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id20">[1]</a>. Defaults to 0.5 as in <a class="reference internal" href="#r2748c43c01db-1" id="id21">[1]</a>.</p></li>
+<li><p><strong>tcn_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth of Temporal Convolutional Network block (i.e. number of TCN
+Residual blocks), denoted L in table 1 of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id22">[1]</a>. Defaults to 2
+as in <a class="reference internal" href="#r2748c43c01db-1" id="id23">[1]</a>.</p></li>
+<li><p><strong>tcn_kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Temporal kernel size used in TCN block, denoted Kt in table 1 of the
+paper <a class="reference internal" href="#r2748c43c01db-1" id="id24">[1]</a>. Defaults to 4 as in <a class="reference internal" href="#r2748c43c01db-1" id="id25">[1]</a>.</p></li>
+<li><p><strong>tcn_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of filters used in TCN convolutional layers (Ft). Defaults to
+32 as in <a class="reference internal" href="#r2748c43c01db-1" id="id26">[1]</a>.</p></li>
+<li><p><strong>tcn_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the TCN block, denoted pt in table 1
+of the paper <a class="reference internal" href="#r2748c43c01db-1" id="id27">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2748c43c01db-1" id="id28">[1]</a>.</p></li>
+<li><p><strong>tcn_activation</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – Nonlinear activation to use. Defaults to nn.ELU().</p></li>
+<li><p><strong>concat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <code class="docutils literal notranslate"><span class="pre">True</span></code>, concatenates each slidding window embedding before
+feeding it to a fully-connected layer, as done in <a class="reference internal" href="#r2748c43c01db-1" id="id29">[1]</a>. When <code class="docutils literal notranslate"><span class="pre">False</span></code>,
+maps each slidding window to <cite>n_outputs</cite> logits and average them.
+Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code> contrary to what is reported in <a class="reference internal" href="#r2748c43c01db-1" id="id30">[1]</a>, but
+matching what the official code does <a class="reference internal" href="#r2748c43c01db-2" id="id31">[2]</a>.</p></li>
+<li><p><strong>max_norm_const</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Maximum L2-norm constraint imposed on weights of the last
+fully-connected layer. Defaults to 0.25.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2748c43c01db-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id3">2</a>,<a role="doc-backlink" href="#id4">3</a>,<a role="doc-backlink" href="#id5">4</a>,<a role="doc-backlink" href="#id6">5</a>,<a role="doc-backlink" href="#id7">6</a>,<a role="doc-backlink" href="#id8">7</a>,<a role="doc-backlink" href="#id9">8</a>,<a role="doc-backlink" href="#id10">9</a>,<a role="doc-backlink" href="#id11">10</a>,<a role="doc-backlink" href="#id12">11</a>,<a role="doc-backlink" href="#id13">12</a>,<a role="doc-backlink" href="#id14">13</a>,<a role="doc-backlink" href="#id15">14</a>,<a role="doc-backlink" href="#id16">15</a>,<a role="doc-backlink" href="#id17">16</a>,<a role="doc-backlink" href="#id18">17</a>,<a role="doc-backlink" href="#id19">18</a>,<a role="doc-backlink" href="#id20">19</a>,<a role="doc-backlink" href="#id21">20</a>,<a role="doc-backlink" href="#id22">21</a>,<a role="doc-backlink" href="#id23">22</a>,<a role="doc-backlink" href="#id24">23</a>,<a role="doc-backlink" href="#id25">24</a>,<a role="doc-backlink" href="#id26">25</a>,<a role="doc-backlink" href="#id27">26</a>,<a role="doc-backlink" href="#id28">27</a>,<a role="doc-backlink" href="#id29">28</a>,<a role="doc-backlink" href="#id30">29</a>)</span>
+<p>H. Altaheri, G. Muhammad and M. Alsulaiman, “Physics-informed
+attention temporal convolutional network for EEG-based motor imagery
+classification,” in IEEE Transactions on Industrial Informatics,
+2022, doi: 10.1109/TII.2022.3197419.</p>
+</div>
+<div class="citation" id="r2748c43c01db-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id31">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/Altaheri/EEG-ATCNet/blob/main/models.py">Altaheri/EEG-ATCNet</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.atcnet.ATCNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L239-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.atcnet.ATCNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.atcnet.ATCNet.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.atcnet.ATCNet.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.base">
+<span id="braindecode-models-base-module"></span><h2>braindecode.models.base module<a class="headerlink" href="#module-braindecode.models.base" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.base.</span></span><span class="sig-name descname"><span class="pre">EEGModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><span class="pre">Dict</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L34-L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Mixin class for all EEG models in braindecode.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.add_log_softmax">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">add_log_softmax</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.add_log_softmax" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.chs_info">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">chs_info</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.chs_info" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.get_output_shape">
+<span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L189-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns shape of neural network output for batch size equal 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Tuple[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.get_torchinfo_statistics">
+<span class="sig-name descname"><span class="pre">get_torchinfo_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('input_size',</span> <span class="pre">'output_size',</span> <span class="pre">'num_params',</span> <span class="pre">'kernel_size')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_settings</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('var_names',</span> <span class="pre">'depth')</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ModelStatistics</span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L277-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.get_torchinfo_statistics" title="Link to this definition">#</a></dt>
+<dd><p>Generate table describing the model using torchinfo.summary.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>col_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which columns to show in the output, see torchinfo for details, by default
+(“input_size”, “output_size”, “num_params”, “kernel_size”)</p></li>
+<li><p><strong>row_settings</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which features to show in a row, see torchinfo for details, by default
+(“var_names”, “depth”)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ModelStatistics generated by torchinfo.summary.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torchinfo.ModelStatistics</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.input_shape">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">input_shape</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.input_shape" title="Link to this definition">#</a></dt>
+<dd><p>Input data shape.</p>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.input_window_seconds">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">input_window_seconds</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.input_window_seconds" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.load_state_dict">
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L222-L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.load_state_dict" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.mapping">
+<span class="sig-name descname"><span class="pre">mapping</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">None</span></em><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.mapping" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.n_chans">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_chans</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.n_chans" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.n_outputs">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_outputs</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.n_outputs" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.n_times">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_times</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.n_times" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.sfreq">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sfreq</span></span><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.sfreq" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.base.EEGModuleMixin.to_dense_prediction_model">
+<span class="sig-name descname"><span class="pre">to_dense_prediction_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(2,</span> <span class="pre">3)</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L234-L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.EEGModuleMixin.to_dense_prediction_model" title="Link to this definition">#</a></dt>
+<dd><p>Transform a sequential model with strides to a model that outputs
+dense predictions by removing the strides and instead inserting dilations.
+Modifies model in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>axis</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Axis to transform (in terms of intermediate output axes)
+can either be 2, 3, or (2,3).</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Does not yet work correctly for average pooling.
+Prior to version 0.1.7, there had been a bug that could move strides
+backwards one layer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.base.deprecated_args">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.base.</span></span><span class="sig-name descname"><span class="pre">deprecated_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">old_new_args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L17-L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.base.deprecated_args" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</section>
+<section id="module-braindecode.models.deep4">
+<span id="braindecode-models-deep4-module"></span><h2>braindecode.models.deep4 module<a class="headerlink" href="#module-braindecode.models.deep4" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.deep4.Deep4Net">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.deep4.</span></span><span class="sig-name descname"><span class="pre">Deep4Net</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_2=50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_2=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_3=100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_3=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_4=200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_4=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_before_pool=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deep4.py#L15-L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.deep4.Deep4Net" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>Deep ConvNet model from Schirrmeister et al 2017.</p>
+<p>Model described in <a class="reference internal" href="#r908a11a34b75-schirrmeister2017" id="id32">[Schirrmeister2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, n_times must not be None. Default: “auto”.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 1.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>n_filters_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 2.</p></li>
+<li><p><strong>filter_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 2.</p></li>
+<li><p><strong>n_filters_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 3.</p></li>
+<li><p><strong>filter_length_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 3.</p></li>
+<li><p><strong>n_filters_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 4.</p></li>
+<li><p><strong>filter_length_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 4.</p></li>
+<li><p><strong>first_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in layer 1.</p></li>
+<li><p><strong>first_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in layer 1. “max” or “mean”.</p></li>
+<li><p><strong>first_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in layer 1.</p></li>
+<li><p><strong>later_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in later layers.</p></li>
+<li><p><strong>later_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in later layers. “max” or “mean”.</p></li>
+<li><p><strong>later_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in later layers.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+There would be no non-linearity between the split layers.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>stride_before_pool</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Stride before pooling.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r908a11a34b75-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id32">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.deepsleepnet">
+<span id="braindecode-models-deepsleepnet-module"></span><h2>braindecode.models.deepsleepnet module<a class="headerlink" href="#module-braindecode.models.deepsleepnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.deepsleepnet.DeepSleepNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.deepsleepnet.</span></span><span class="sig-name descname"><span class="pre">DeepSleepNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L167-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.deepsleepnet.DeepSleepNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Sleep staging architecture from Supratak et al 2017.</p>
+<p>Convolutional neural network and bidirectional-Long Short-Term
+for single channels sleep staging described in <a class="reference internal" href="#rfe2755f8dd5a-supratak2017" id="id33">[Supratak2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rfe2755f8dd5a-supratak2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id33">Supratak2017</a><span class="fn-bracket">]</span></span>
+<p>Supratak, A., Dong, H., Wu, C., &amp; Guo, Y. (2017).
+DeepSleepNet: A model for automatic sleep stage scoring based
+on raw single-channel EEG. IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 25(11), 1998-2008.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.deepsleepnet.DeepSleepNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L232-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.deepsleepnet.DeepSleepNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eegconformer">
+<span id="braindecode-models-eegconformer-module"></span><h2>braindecode.models.eegconformer module<a class="headerlink" href="#module-braindecode.models.eegconformer" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegconformer.EEGConformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegconformer.</span></span><span class="sig-name descname"><span class="pre">EEGConformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_fc_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2440</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L14-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegconformer.EEGConformer" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>EEG Conformer.</p>
+<p>Convolutional Transformer for EEG decoding.</p>
+<p>The paper and original code with more details about the methodological
+choices are available at the <a class="reference internal" href="#r11f96b6266b3-song2022" id="id34">[Song2022]</a> and <a class="reference internal" href="#r11f96b6266b3-conformercode" id="id35">[ConformerCode]</a>.</p>
+<p>This neural network architecture receives a traditional braindecode input.
+The input shape should be three-dimensional matrix representing the EEG
+signals.</p>
+<blockquote>
+<div><p><cite>(batch_size, n_channels, n_timesteps)</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>The EEG Conformer architecture is composed of three modules:</dt><dd><ul class="simple">
+<li><p>PatchEmbedding</p></li>
+<li><p>TransformerEncoder</p></li>
+<li><p>ClassificationHead</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters, defines also embedding size.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the convolutional layer.</p></li>
+<li><p><strong>att_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of self-attention layers.</p></li>
+<li><p><strong>att_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads.</p></li>
+<li><p><strong>att_drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the self-attention layer.</p></li>
+<li><p><strong>final_fc_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The dimension of the fully connected layer.</p></li>
+<li><p><strong>return_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the forward method returns the features before the
+last classification layer. Defaults to False.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The authors recommend using data augmentation before using Conformer,
+e.g. segmentation and recombination,
+Please refer to the original paper and code for more details.</p>
+<p>The model was initially tuned on 4 seconds of 250 Hz data.
+Please adjust the scale of the temporal convolutional layer,
+and the pooling layer for better performance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.8.</span></p>
+</div>
+<p>We aggregate the parameters based on the parts of the models, or
+when the parameters were used first, e.g. n_filters_time.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r11f96b6266b3-song2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id34">Song2022</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering,
+31, pp.710-719. <a class="reference external" href="https://ieeexplore.ieee.org/document/9991178">https://ieeexplore.ieee.org/document/9991178</a></p>
+</div>
+<div class="citation" id="r11f96b6266b3-conformercode" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id35">ConformerCode</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+<a class="github reference external" href="https://github.com/eeyhsong/EEG-Conformer">eeyhsong/EEG-Conformer</a>.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.eegconformer.EEGConformer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L163-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegconformer.EEGConformer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.eegconformer.EEGConformer.get_fc_size">
+<span class="sig-name descname"><span class="pre">get_fc_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L171-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegconformer.EEGConformer.get_fc_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eeginception">
+<span id="braindecode-models-eeginception-module"></span><h2>braindecode.models.eeginception module<a class="headerlink" href="#module-braindecode.models.eeginception" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eeginception.EEGInception">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eeginception.</span></span><span class="sig-name descname"><span class="pre">EEGInception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception.py#L17-L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eeginception.EEGInception" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>EEG Inception for ERP-based classification</p>
+<p>–&gt; DEPRECATED &lt;–
+THIS CLASS IS DEPRECATED AND WILL BE REMOVED IN THE RELEASE 0.9 OF
+BRAINDECODE. PLEASE USE braindecode.models.EEGInceptionERP INSTEAD IN THE
+FUTURE.</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#reb937fb413bc-santamaria2020" id="id36">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#reb937fb413bc-2" id="id37">[2]</a>.</p>
+<p>The model is strongly based on the original InceptionNet for an image. The main goal is
+to extract features in parallel with different scales. The authors extracted three scales
+proportional to the window sample size. The network had three parts:
+1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
+for classification.</p>
+<p>One advantage of the EEG-Inception block is that it allows a network
+to learn simultaneous components of low and high frequency associated with the signal.
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id38">[Reb937fb413bc-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#reb937fb413bc-santamaria2020" id="id39">[Santamaria2020]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate inside all the network.</p></li>
+<li><p><strong>scales_samples_s</strong> – The description is missing.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 8 in <a class="reference internal" href="#reb937fb413bc-santamaria2020" id="id40">[Santamaria2020]</a>.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function, default: ELU activation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier for the depthwise convolution.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Pooling sizes for the inception block.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#reb937fb413bc-2" id="id41">[2]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="reb937fb413bc-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id36">1</a>,<a role="doc-backlink" href="#id39">2</a>,<a role="doc-backlink" href="#id40">3</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
+EEG-inception: A novel deep convolutional neural network for assistive
+ERP-based brain-computer interfaces.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
+Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
+</div>
+<div class="citation" id="reb937fb413bc-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id37">1</a>,<a role="doc-backlink" href="#id41">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
+Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eeginception_erp">
+<span id="braindecode-models-eeginception-erp-module"></span><h2>braindecode.models.eeginception_erp module<a class="headerlink" href="#module-braindecode.models.eeginception_erp" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eeginception_erp.EEGInceptionERP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eeginception_erp.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionERP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_erp.py#L16-L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eeginception_erp.EEGInceptionERP" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>EEG Inception for ERP-based classification</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id42">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#r6ac5fd57fa8e-2" id="id43">[2]</a>.</p>
+<p>The model is strongly based on the original InceptionNet for an image. The main goal is
+to extract features in parallel with different scales. The authors extracted three scales
+proportional to the window sample size. The network had three parts:
+1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
+for classification.</p>
+<p>One advantage of the EEG-Inception block is that it allows a network
+to learn simultaneous components of low and high frequency associated with the signal.
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id44">[R6ac5fd57fa8e-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id45">[Santamaria2020]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Size of the input, in number of samples. Set to 128 (1s) as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id46">[Santamaria2020]</a>.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency. Defaults to 128 as in <a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id47">[Santamaria2020]</a>.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Dropout rate inside all the network. Defaults to 0.5 as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id48">[Santamaria2020]</a>.</p></li>
+<li><p><strong>scales_samples_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>)</em><em>, </em><em>optional</em>) – Windows for inception block. Temporal scale (s) of the convolutions on
+each Inception module. This parameter determines the kernel sizes of
+the filters. Defaults to 0.5, 0.25, 0.125 seconds, as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id49">[Santamaria2020]</a>.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Initial number of convolutional filters. Defaults to 8 as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id50">[Santamaria2020]</a>.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – Activation function. Defaults to ELU activation as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id51">[Santamaria2020]</a>.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Momentum for BatchNorm2d. Defaults to 0.01.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Depth multiplier for the depthwise convolution. Defaults to 2 as in
+<a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id52">[Santamaria2020]</a>.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em><em>, </em><em>optional</em>) – Pooling sizes for the inception blocks. Defaults to 4, 2, 2 and 2, as
+in <a class="reference internal" href="#r6ac5fd57fa8e-santamaria2020" id="id53">[Santamaria2020]</a>.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r6ac5fd57fa8e-2" id="id54">[2]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r6ac5fd57fa8e-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id42">1</a>,<a role="doc-backlink" href="#id45">2</a>,<a role="doc-backlink" href="#id46">3</a>,<a role="doc-backlink" href="#id47">4</a>,<a role="doc-backlink" href="#id48">5</a>,<a role="doc-backlink" href="#id49">6</a>,<a role="doc-backlink" href="#id50">7</a>,<a role="doc-backlink" href="#id51">8</a>,<a role="doc-backlink" href="#id52">9</a>,<a role="doc-backlink" href="#id53">10</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
+EEG-inception: A novel deep convolutional neural network for assistive
+ERP-based brain-computer interfaces.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
+Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
+</div>
+<div class="citation" id="r6ac5fd57fa8e-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id43">1</a>,<a role="doc-backlink" href="#id54">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
+Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eeginception_mi">
+<span id="braindecode-models-eeginception-mi-module"></span><h2>braindecode.models.eeginception_mi module<a class="headerlink" href="#module-braindecode.models.eeginception_mi" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eeginception_mi.EEGInceptionMI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eeginception_mi.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionMI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_convs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">48</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_unit_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ReLU()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L13-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eeginception_mi.EEGInceptionMI" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>EEG Inception for Motor Imagery, as proposed in <a class="reference internal" href="#ra141f1d2e317-1" id="id55">[1]</a></p>
+<p>The model is strongly based on the original InceptionNet for computer
+vision. The main goal is to extract features in parallel with different
+scales. The network has two blocks made of 3 inception modules with a skip
+connection.</p>
+<p>The model is fully described in <a class="reference internal" href="#ra141f1d2e317-1" id="id56">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size of the input, in seconds. Set to 4.5 s as in <a class="reference internal" href="#ra141f1d2e317-1" id="id57">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency in Hz. Defaults to 250 Hz as in <a class="reference internal" href="#ra141f1d2e317-1" id="id58">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>n_convs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolution per inception wide branching. Defaults to 5 as
+in <a class="reference internal" href="#ra141f1d2e317-1" id="id59">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolutional filters for all layers of this type. Set to 48
+as in <a class="reference internal" href="#ra141f1d2e317-1" id="id60">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>kernel_unit_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size in seconds of the basic 1D convolutional kernel used in inception
+modules. Each convolutional layer in such modules have kernels of
+increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7,
+0.9 here for <a href="#id61"><span class="problematic" id="id62">`</span></a>n_convs`=5). Defaults to 0.1 s.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function. Defaults to ReLU activation.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented bosed on the paper <a class="reference internal" href="#ra141f1d2e317-1" id="id63">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra141f1d2e317-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id55">1</a>,<a role="doc-backlink" href="#id56">2</a>,<a role="doc-backlink" href="#id57">3</a>,<a role="doc-backlink" href="#id58">4</a>,<a role="doc-backlink" href="#id59">5</a>,<a role="doc-backlink" href="#id60">6</a>,<a role="doc-backlink" href="#id63">7</a>)</span>
+<p>Zhang, C., Kim, Y. K., &amp; Eskandarian, A. (2021).
+EEG-inception: an accurate and robust end-to-end neural network
+for EEG-based motor imagery classification.
+Journal of Neural Engineering, 18(4), 046014.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.eeginception_mi.EEGInceptionMI.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L185-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eeginception_mi.EEGInceptionMI.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eegitnet">
+<span id="braindecode-models-eegitnet-module"></span><h2>braindecode.models.eegitnet module<a class="headerlink" href="#module-braindecode.models.eegitnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegitnet.EEGITNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegitnet.</span></span><span class="sig-name descname"><span class="pre">EEGITNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegitnet.py#L89-L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegitnet.EEGITNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<dl class="simple">
+<dt>EEG-ITNet: An Explainable Inception Temporal</dt><dd><p>Convolutional Network for motor imagery classification from
+Salami et. al 2022.</p>
+</dd>
+</dl>
+<p>See <a class="reference internal" href="#r2dafa93814d8-salami2022" id="id64">[Salami2022]</a> for details.</p>
+<p>Code adapted from <a class="github reference external" href="https://github.com/abbassalami/eeg-itnet">abbassalami/eeg-itnet</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on author implementation.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2dafa93814d8-salami2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id64">Salami2022</a><span class="fn-bracket">]</span></span>
+<ol class="upperalpha simple">
+<li><p>Salami, J. Andreu-Perez and H. Gillmeister, “EEG-ITNet: An Explainable</p></li>
+</ol>
+</div>
+</div>
+<p>Inception Temporal Convolutional Network for motor imagery classification,” in IEEE Access,
+doi: 10.1109/ACCESS.2022.3161489.</p>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eegnet">
+<span id="braindecode-models-eegnet-module"></span><h2>braindecode.models.eegnet module<a class="headerlink" href="#module-braindecode.models.eegnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegnet.</span></span><span class="sig-name descname"><span class="pre">Conv2dWithConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L15-L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html#torch.nn.Conv2d" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a></p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.bias">
+<span class="sig-name descname"><span class="pre">bias</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.bias" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.dilation">
+<span class="sig-name descname"><span class="pre">dilation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.dilation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L20-L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.groups">
+<span class="sig-name descname"><span class="pre">groups</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.groups" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.in_channels">
+<span class="sig-name descname"><span class="pre">in_channels</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.in_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.kernel_size">
+<span class="sig-name descname"><span class="pre">kernel_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.kernel_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.out_channels">
+<span class="sig-name descname"><span class="pre">out_channels</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.out_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.output_padding">
+<span class="sig-name descname"><span class="pre">output_padding</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.output_padding" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.padding">
+<span class="sig-name descname"><span class="pre">padding</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.padding" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.padding_mode">
+<span class="sig-name descname"><span class="pre">padding_mode</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.padding_mode" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.stride">
+<span class="sig-name descname"><span class="pre">stride</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.stride" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.transposed">
+<span class="sig-name descname"><span class="pre">transposed</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.transposed" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.Conv2dWithConstraint.weight">
+<span class="sig-name descname"><span class="pre">weight</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></em><a class="headerlink" href="#braindecode.models.eegnet.Conv2dWithConstraint.weight" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.EEGNetv1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegnet.</span></span><span class="sig-name descname"><span class="pre">EEGNetv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">32)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L217-L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegnet.EEGNetv1" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>EEGNet model from Lawhern et al. 2016.</p>
+<p>See details in <a class="reference internal" href="#r3264ba5e2172-eegnet" id="id65">[EEGNet]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> – The description is missing.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>second_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper description.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r3264ba5e2172-eegnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id65">EEGNet</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+S. M., Hung, C. P., &amp; Lance, B. J. (2016).
+EEGNet: A Compact Convolutional Network for EEG-based
+Brain-Computer Interfaces.
+arXiv preprint arXiv:1611.08024.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegnet.EEGNetv4">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegnet.</span></span><span class="sig-name descname"><span class="pre">EEGNetv4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L27-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegnet.EEGNetv4" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>EEGNet v4 model from Lawhern et al 2018.</p>
+<p>See details in <a class="reference internal" href="#r854ca6541292-eegnet4" id="id66">[EEGNet4]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>&quot;auto&quot;</em>) – If int, final length of convolutional filters.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>F1</strong> – The description is missing.</p></li>
+<li><p><strong>D</strong> – The description is missing.</p></li>
+<li><p><strong>F2</strong> – The description is missing.</p></li>
+<li><p><strong>kernel_length</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper description.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r854ca6541292-eegnet4" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id66">EEGNet4</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+S. M., Hung, C. P., &amp; Lance, B. J. (2018).
+EEGNet: A Compact Convolutional Network for EEG-based
+Brain-Computer Interfaces.
+arXiv preprint arXiv:1611.08024.</p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.eegresnet">
+<span id="braindecode-models-eegresnet-module"></span><h2>braindecode.models.eegresnet module<a class="headerlink" href="#module-braindecode.models.eegresnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.eegresnet.EEGResNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.eegresnet.</span></span><span class="sig-name descname"><span class="pre">EEGResNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_pool_length=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_first_filters=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layers_per_block=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_filter_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_epsilon=0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_weight_init_fn=&lt;function</span> <span class="pre">EEGResNet.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegresnet.py#L19-L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.eegresnet.EEGResNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>Residual Network for EEG.</p>
+<p>XXX missing reference</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_pool_length</strong> – The description is missing.</p></li>
+<li><p><strong>n_first_filters</strong> – The description is missing.</p></li>
+<li><p><strong>n_layers_per_block</strong> – The description is missing.</p></li>
+<li><p><strong>first_filter_length</strong> – The description is missing.</p></li>
+<li><p><strong>nonlinearity</strong> – The description is missing.</p></li>
+<li><p><strong>split_first_layer</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_alpha</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_epsilon</strong> – The description is missing.</p></li>
+<li><p><strong>conv_weight_init_fn</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – <p>Alias for <cite>n_chans</cite>.
+n_classes :</p>
+<blockquote>
+<div><p>Alias for <cite>n_outputs</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>input_window_samples :</dt><dd><p>Alias for <cite>n_times</cite>.</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.functions">
+<span id="braindecode-models-functions-module"></span><h2>braindecode.models.functions module<a class="headerlink" href="#module-braindecode.models.functions" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.functions.identity">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.functions.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/functions.py#L17-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.functions.identity" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.functions.safe_log">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.functions.</span></span><span class="sig-name descname"><span class="pre">safe_log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/functions.py#L12-L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.functions.safe_log" title="Link to this definition">#</a></dt>
+<dd><p>Prevents <span class="math notranslate nohighlight">\(log(0)\)</span> by using <span class="math notranslate nohighlight">\(log(max(x, eps))\)</span>.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.functions.square">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.functions.</span></span><span class="sig-name descname"><span class="pre">square</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/functions.py#L8-L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.functions.square" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.functions.squeeze_final_output">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.functions.</span></span><span class="sig-name descname"><span class="pre">squeeze_final_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/functions.py#L21-L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.functions.squeeze_final_output" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Removes empty dimension at end and potentially removes empty time</dt><dd><p>dimension. It does  not just use squeeze as we never want to remove
+first dimension.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – squeezed tensor</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)">torch.Tensor</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.functions.transpose_time_to_spat">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.functions.</span></span><span class="sig-name descname"><span class="pre">transpose_time_to_spat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/functions.py#L39-L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.functions.transpose_time_to_spat" title="Link to this definition">#</a></dt>
+<dd><p>Swap time and spatial dimensions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – tensor in which last and first dimensions are swapped</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)">torch.Tensor</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.hybrid">
+<span id="braindecode-models-hybrid-module"></span><h2>braindecode.models.hybrid module<a class="headerlink" href="#module-braindecode.models.hybrid" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.hybrid.HybridNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.hybrid.</span></span><span class="sig-name descname"><span class="pre">HybridNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L15-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.hybrid.HybridNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Hybrid ConvNet model from Schirrmeister et al 2017.</p>
+<p>See <a class="reference internal" href="#r2f4f6e3b6319-schirrmeister2017" id="id67">[Schirrmeister2017]</a> for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – The description is missing.</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2f4f6e3b6319-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id67">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.hybrid.HybridNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L117-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.hybrid.HybridNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.modules">
+<span id="braindecode-models-modules-module"></span><h2>braindecode.models.modules module<a class="headerlink" href="#module-braindecode.models.modules" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.AvgPool2dWithConv">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">AvgPool2dWithConv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L52-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.AvgPool2dWithConv" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute average pooling using a convolution, to have the dilation parameter.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kernel_size</strong> (<em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Size of the pooling region.</p></li>
+<li><p><strong>stride</strong> (<em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Stride of the pooling operation.</p></li>
+<li><p><strong>dilation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Dilation applied to the pooling filter.</p></li>
+<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Padding applied before the pooling operation.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.AvgPool2dWithConv.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L79-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.AvgPool2dWithConv.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.AvgPool2dWithConv.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.AvgPool2dWithConv.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">CausalConv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L191-L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.CausalConv1d" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv1d" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a></p>
+<p>Causal 1-dimensional convolution</p>
+<p>Code modified from <a class="reference internal" href="#r31b025adc63e-1" id="id68">[1]</a> and <a class="reference internal" href="#r31b025adc63e-2" id="id69">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Input channels.</p></li>
+<li><p><strong>out_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output channels (number of filters).</p></li>
+<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Kernel size.</p></li>
+<li><p><strong>dilation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Dilation (number of elements to skip within kernel multiplication).
+Default to 1.</p></li>
+<li><p><strong>**kwargs</strong> – Other keyword arguments to pass to torch.nn.Conv1d, except for
+<cite>padding</cite>!!</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r31b025adc63e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id68">1</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://discuss.pytorch.org/t/causal-convolution/3456/4">https://discuss.pytorch.org/t/causal-convolution/3456/4</a></p>
+</div>
+<div class="citation" id="r31b025adc63e-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id69">2</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://gist.github.com/paultsw/7a9d6e3ce7b70e9e2c61bc9287addefc">https://gist.github.com/paultsw/7a9d6e3ce7b70e9e2c61bc9287addefc</a></p>
+</div>
+</div>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.bias">
+<span class="sig-name descname"><span class="pre">bias</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.bias" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.dilation">
+<span class="sig-name descname"><span class="pre">dilation</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.dilation" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L240-L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.groups">
+<span class="sig-name descname"><span class="pre">groups</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.groups" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.in_channels">
+<span class="sig-name descname"><span class="pre">in_channels</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.in_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.kernel_size">
+<span class="sig-name descname"><span class="pre">kernel_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.kernel_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.out_channels">
+<span class="sig-name descname"><span class="pre">out_channels</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.out_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.output_padding">
+<span class="sig-name descname"><span class="pre">output_padding</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.output_padding" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.padding">
+<span class="sig-name descname"><span class="pre">padding</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.padding" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.padding_mode">
+<span class="sig-name descname"><span class="pre">padding_mode</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.padding_mode" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.stride">
+<span class="sig-name descname"><span class="pre">stride</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.stride" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.transposed">
+<span class="sig-name descname"><span class="pre">transposed</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.transposed" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CausalConv1d.weight">
+<span class="sig-name descname"><span class="pre">weight</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></em><a class="headerlink" href="#braindecode.models.modules.CausalConv1d.weight" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.CombinedConv">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">CombinedConv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_spat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L292-L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.CombinedConv" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Merged convolutional layer for temporal and spatial convs in Deep4/ShallowFBCSP</p>
+<p>Numerically equivalent to the separate sequential approach, but this should be faster.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG input channels.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>bias_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use bias in the temporal conv</p></li>
+<li><p><strong>bias_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use bias in the spatial conv</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.CombinedConv.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L333-L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.CombinedConv.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.CombinedConv.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.CombinedConv.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.Ensure4d">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">Ensure4d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L14-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.Ensure4d" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.Ensure4d.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L15-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.Ensure4d.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.Ensure4d.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.Ensure4d.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.Expression">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">Expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expression_fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L21-L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.Expression" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute given expression on forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>expression_fn</strong> (<em>callable</em>) – Should accept variable number of objects of type
+<cite>torch.autograd.Variable</cite> to compute its output.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.Expression.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L35-L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.Expression.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.Expression.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.Expression.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.IntermediateOutputWrapper">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">IntermediateOutputWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_select</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L113-L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.IntermediateOutputWrapper" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Wraps network model such that outputs of intermediate layers can be returned.
+forward() returns list of intermediate activations in a network during forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>to_select</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – list of module names for which activation should be returned</p></li>
+<li><p><strong>model</strong> (<em>model object</em>) – network model</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">Deep4Net</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">select_modules</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;conv_spat&#39;</span><span class="p">,</span><span class="s1">&#39;conv_2&#39;</span><span class="p">,</span><span class="s1">&#39;conv_3&#39;</span><span class="p">,</span><span class="s1">&#39;conv_4&#39;</span><span class="p">]</span> <span class="c1"># Specify intermediate outputs</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">model_pert</span> <span class="o">=</span> <span class="n">IntermediateOutputWrapper</span><span class="p">(</span><span class="n">select_modules</span><span class="p">,</span><span class="n">model</span><span class="p">)</span> <span class="c1"># Wrap model</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.IntermediateOutputWrapper.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L143-L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.IntermediateOutputWrapper.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.IntermediateOutputWrapper.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.IntermediateOutputWrapper.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.MaxNormLinear">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">MaxNormLinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L245-L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.MaxNormLinear" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
+<p>Linear layer with MaxNorm constraining on weights.</p>
+<p>Equivalent of Keras tf.keras.Dense(…, kernel_constraint=max_norm())
+[1, 2]_. Implemented as advised in <a class="reference internal" href="#rb2af2ffc6e21-3" id="id70">[3]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of each input sample.</p></li>
+<li><p><strong>out_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of each output sample.</p></li>
+<li><p><strong>bias</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – If set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the layer will not learn an additive bias.
+Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb2af2ffc6e21-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://keras.io/api/layers/core_layers/dense/#dense-class">https://keras.io/api/layers/core_layers/dense/#dense-class</a></p>
+</div>
+<div class="citation" id="rb2af2ffc6e21-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/constraints/">https://www.tensorflow.org/api_docs/python/tf/keras/constraints/</a>
+MaxNorm</p>
+</div>
+<div class="citation" id="rb2af2ffc6e21-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id70">3</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://discuss.pytorch.org/t/how-to-correctly-implement-in-place">https://discuss.pytorch.org/t/how-to-correctly-implement-in-place</a>-
+max-norm-constraint/96769</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.MaxNormLinear.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L279-L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.MaxNormLinear.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.MaxNormLinear.in_features">
+<span class="sig-name descname"><span class="pre">in_features</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.modules.MaxNormLinear.in_features" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.MaxNormLinear.out_features">
+<span class="sig-name descname"><span class="pre">out_features</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></em><a class="headerlink" href="#braindecode.models.modules.MaxNormLinear.out_features" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.MaxNormLinear.weight">
+<span class="sig-name descname"><span class="pre">weight</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></em><a class="headerlink" href="#braindecode.models.modules.MaxNormLinear.weight" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.modules.TimeDistributed">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.modules.</span></span><span class="sig-name descname"><span class="pre">TimeDistributed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L153-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.TimeDistributed" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Apply module on multiple windows.</p>
+<p>Apply the provided module on a sequence of windows and return their
+concatenation.
+Useful with sequence-to-prediction models (e.g. sleep stager which must map
+a sequence of consecutive windows to the label of the middle window in the
+sequence).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>nn.Module</em>) – Module to be applied to the input windows. Must accept an input of
+shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.modules.TimeDistributed.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L173-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.modules.TimeDistributed.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Sequence of windows, of shape (batch_size, seq_len, n_channels,
+n_times).</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Shape (batch_size, seq_len, output_size).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)">torch.Tensor</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.modules.TimeDistributed.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.modules.TimeDistributed.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.shallow_fbcsp">
+<span id="braindecode-models-shallow-fbcsp-module"></span><h2>braindecode.models.shallow_fbcsp module<a class="headerlink" href="#module-braindecode.models.shallow_fbcsp" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.shallow_fbcsp.ShallowFBCSPNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.shallow_fbcsp.</span></span><span class="sig-name descname"><span class="pre">ShallowFBCSPNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_nonlin=&lt;function</span> <span class="pre">square&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode='mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_nonlin=&lt;function</span> <span class="pre">safe_log&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/shallow_fbcsp.py#L14-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.shallow_fbcsp.ShallowFBCSPNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code></a></p>
+<p>Shallow ConvNet model from Schirrmeister et al 2017.</p>
+<p>Model described in <a class="reference internal" href="#r186f673612a1-schirrmeister2017" id="id71">[Schirrmeister2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, length of the input signal must be specified.</p></li>
+<li><p><strong>conv_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after convolution layers.</p></li>
+<li><p><strong>pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Method to use on pooling layers. “max” or “mean”.</p></li>
+<li><p><strong>pool_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after pooling layers.</p></li>
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+There would be no non-linearity between the split layers.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Alias for <cite>n_times</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r186f673612a1-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id71">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+&amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.sleep_stager_blanco_2020">
+<span id="braindecode-models-sleep-stager-blanco-2020-module"></span><h2>braindecode.models.sleep_stager_blanco_2020 module<a class="headerlink" href="#module-braindecode.models.sleep_stager_blanco_2020" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.sleep_stager_blanco_2020.</span></span><span class="sig-name descname"><span class="pre">SleepStagerBlanco2020</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L11-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Sleep staging architecture from Blanco et al 2020.</p>
+<p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#r0566cbf0e1df-blanco2020" id="id72">[Blanco2020]</a>.
+A series of seven convolutional layers with kernel sizes running down from 7 to 3,
+in an attempt to extract more general features at the beginning, while more specific
+and complex features were extracted in the final stages.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 20 in <a class="reference internal" href="#r0566cbf0e1df-blanco2020" id="id73">[Blanco2020]</a>.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of groups for the convolution. Set to 2 in <a class="reference internal" href="#r0566cbf0e1df-blanco2020" id="id74">[Blanco2020]</a> for 2 Channel EEG.
+controls the connections between inputs and outputs. n_channels and n_conv_chans must be
+divisible by n_groups.</p></li>
+<li><p><strong>max_pool_size</strong> – The description is missing.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+layers.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0566cbf0e1df-blanco2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Blanco2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id72">1</a>,<a role="doc-backlink" href="#id73">2</a>,<a role="doc-backlink" href="#id74">3</a>)</span>
+<p>Fernandez-Blanco, E., Rivero, D. &amp; Pazos, A. Convolutional
+neural networks for sleep stage scoring on a two-channel EEG signal.
+Soft Comput 24, 4067–4079 (2020). <a class="reference external" href="https://doi.org/10.1007/s00500-019-04174-1">https://doi.org/10.1007/s00500-019-04174-1</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L144-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.sleep_stager_chambon_2018">
+<span id="braindecode-models-sleep-stager-chambon-2018-module"></span><h2>braindecode.models.sleep_stager_chambon_2018 module<a class="headerlink" href="#module-braindecode.models.sleep_stager_chambon_2018" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.sleep_stager_chambon_2018.</span></span><span class="sig-name descname"><span class="pre">SleepStagerChambon2018</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L11-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Sleep staging architecture from Chambon et al 2018.</p>
+<p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#ra47708a796a9-chambon2018" id="id75">[Chambon2018]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 8 in <a class="reference internal" href="#ra47708a796a9-chambon2018" id="id76">[Chambon2018]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of filters in temporal convolution layers, in seconds. Set to 0.5
+in <a class="reference internal" href="#ra47708a796a9-chambon2018" id="id77">[Chambon2018]</a> (64 samples at sfreq=128).</p></li>
+<li><p><strong>max_pool_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Max pooling size, in seconds. Set to 0.125 in <a class="reference internal" href="#ra47708a796a9-chambon2018" id="id78">[Chambon2018]</a> (16
+samples at sfreq=128).</p></li>
+<li><p><strong>pad_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Padding size, in seconds. Set to 0.25 in <a class="reference internal" href="#ra47708a796a9-chambon2018" id="id79">[Chambon2018]</a> (half the
+temporal convolution kernel size).</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+layers.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>n_classes</strong> – Alias for <cite>n_outputs</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra47708a796a9-chambon2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Chambon2018<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id75">1</a>,<a role="doc-backlink" href="#id76">2</a>,<a role="doc-backlink" href="#id77">3</a>,<a role="doc-backlink" href="#id78">4</a>,<a role="doc-backlink" href="#id79">5</a>)</span>
+<p>Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &amp;
+Gramfort, A. (2018). A deep learning architecture for temporal sleep
+stage classification using multivariate and multimodal time series.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering,
+26(4), 758-769.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L135-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.sleep_stager_eldele_2021">
+<span id="braindecode-models-sleep-stager-eldele-2021-module"></span><h2>braindecode.models.sleep_stager_eldele_2021 module<a class="headerlink" href="#module-braindecode.models.sleep_stager_eldele_2021" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.sleep_stager_eldele_2021.</span></span><span class="sig-name descname"><span class="pre">SleepStagerEldele2021</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">80</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_ff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_attn_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">after_reduced_cnn_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L16-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Sleep Staging Architecture from Eldele et al 2021.</p>
+<p>Attention based Neural Net for sleep staging as described in <a class="reference internal" href="#r720b75a78560-eldele2021" id="id80">[Eldele2021]</a>.
+The code for the paper and this model is also available at <a class="reference internal" href="#r720b75a78560-1" id="id81">[1]</a>.
+Takes single channel EEG as input.
+Feature extraction module based on multi-resolution convolutional neural network (MRCNN)
+and adaptive feature recalibration (AFR).
+The second module is the temporal context encoder (TCE) that leverages a multi-head attention
+mechanism to capture the temporal dependencies among the extracted features.</p>
+<p>Warning - This model was designed for signals of 30 seconds at 100Hz or 125Hz (in which case
+the reference architecture from <a class="reference internal" href="#r720b75a78560-1" id="id82">[1]</a> which was validated on SHHS dataset <a class="reference internal" href="#r720b75a78560-2" id="id83">[2]</a> will be used)
+to use any other input is likely to make the model perform in unintended ways.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_tce</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of TCE clones.</p></li>
+<li><p><strong>d_model</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Input dimension for the TCE.
+Also the input dimension of the first FC layer in the feed forward
+and the output of the second FC layer in the same.
+Increase for higher sampling rate/signal length.
+It should be divisible by n_attn_heads</p></li>
+<li><p><strong>d_ff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output dimension of the first FC layer in the feed forward and the
+input dimension of the second FC layer in the same.</p></li>
+<li><p><strong>n_attn_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads. It should be a factor of d_model</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate in the PositionWiseFeedforward layer and the TCE layers.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>after_reduced_cnn_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of output channels produced by the convolution in the AFR module.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r720b75a78560-eldele2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id80">Eldele2021</a><span class="fn-bracket">]</span></span>
+<p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
+Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
+Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
+</div>
+<div class="citation" id="r720b75a78560-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id81">1</a>,<a role="doc-backlink" href="#id82">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/emadeldeen24/AttnSleep">emadeldeen24/AttnSleep</a></p>
+</div>
+<div class="citation" id="r720b75a78560-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id83">2</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://sleepdata.org/datasets/shhs">https://sleepdata.org/datasets/shhs</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L145-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.return_feats">
+<span class="sig-name descname"><span class="pre">return_feats</span></span><a class="headerlink" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.return_feats" title="Link to this definition">#</a></dt>
+<dd><p>if return_feats:
+raise ValueError(“return_feat == True is not accepted anymore”)</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.tcn">
+<span id="braindecode-models-tcn-module"></span><h2>braindecode.models.tcn module<a class="headerlink" href="#module-braindecode.models.tcn" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.tcn.Chomp1d">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.tcn.</span></span><span class="sig-name descname"><span class="pre">Chomp1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chomp_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L194-L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.Chomp1d" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.tcn.Chomp1d.extra_repr">
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L199-L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.Chomp1d.extra_repr" title="Link to this definition">#</a></dt>
+<dd><p>Set the extra representation of the module</p>
+<p>To print customized extra information, you should re-implement
+this method in your own modules. Both single-line and multi-line
+strings are acceptable.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.tcn.Chomp1d.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L202-L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.Chomp1d.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.tcn.Chomp1d.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.tcn.Chomp1d.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.tcn.TCN">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.tcn.</span></span><span class="sig-name descname"><span class="pre">TCN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L15-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.TCN" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Temporal Convolutional Network (TCN) from Bai et al 2018.</p>
+<p>See <a class="reference internal" href="#r469ca352fa78-bai2018" id="id84">[Bai2018]</a> for details.</p>
+<p>Code adapted from <a class="github reference external" href="https://github.com/locuslab/TCN/blob/master/TCN/tcn.py">locuslab/TCN</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_blocks</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of temporal blocks in the network</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of output filters of each convolution</p></li>
+<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – kernel size of the convolutions</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – dropout probability</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r469ca352fa78-bai2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id84">Bai2018</a><span class="fn-bracket">]</span></span>
+<p>Bai, S., Kolter, J. Z., &amp; Koltun, V. (2018).
+An empirical evaluation of generic convolutional and recurrent networks
+for sequence modeling.
+arXiv preprint arXiv:1803.01271.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.tcn.TCN.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L105-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.TCN.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.tcn.TemporalBlock">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.tcn.</span></span><span class="sig-name descname"><span class="pre">TemporalBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L154-L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.TemporalBlock" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.tcn.TemporalBlock.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L181-L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tcn.TemporalBlock.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.models.tcn.TemporalBlock.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.models.tcn.TemporalBlock.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.tidnet">
+<span id="braindecode-models-tidnet-module"></span><h2>braindecode.models.tidnet module<a class="headerlink" href="#module-braindecode.models.tidnet" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.tidnet.TIDNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.tidnet.</span></span><span class="sig-name descname"><span class="pre">TIDNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_growth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spat_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_span</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bottleneck</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">summary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L169-L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tidnet.TIDNet" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Thinker Invariance DenseNet model from Kostas et al 2020.</p>
+<p>See <a class="reference internal" href="#r685f21f904a2-tidnet" id="id85">[TIDNet]</a> for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>s_growth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – DenseNet-style growth factor (added filters per DenseFilter)</p></li>
+<li><p><strong>t_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability</p></li>
+<li><p><strong>pooling</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Max temporal pooling (width and stride)</p></li>
+<li><p><strong>temp_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal layers</p></li>
+<li><p><strong>spat_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of DenseFilters</p></li>
+<li><p><strong>temp_span</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Percentage of n_times that defines the temporal filter length:
+temp_len = ceil(temp_span * n_times)
+e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal
+filter of length 75.</p></li>
+<li><p><strong>bottleneck</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Bottleneck factor within Densefilter</p></li>
+<li><p><strong>summary</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated
+automatically (n_times // pooling).</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Code adapted from: <a class="github reference external" href="https://github.com/SPOClab-ca/ThinkerInvariance/">SPOClab-ca/ThinkerInvariance</a></p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r685f21f904a2-tidnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id85">TIDNet</a><span class="fn-bracket">]</span></span>
+<p>Kostas, D. &amp; Rudzicz, F.
+Thinker invariance: enabling deep neural networks for BCI across more
+people.
+J. Neural Eng. 17, 056008 (2020).
+doi: 10.1088/1741-2552/abb7a7.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.tidnet.TIDNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L267-L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.tidnet.TIDNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="braindecode.models.tidnet.TIDNet.num_features">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_features</span></span><a class="headerlink" href="#braindecode.models.tidnet.TIDNet.num_features" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.usleep">
+<span id="braindecode-models-usleep-module"></span><h2>braindecode.models.usleep module<a class="headerlink" href="#module-braindecode.models.usleep" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.usleep.USleep">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.usleep.</span></span><span class="sig-name descname"><span class="pre">USleep</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_time_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">complexity_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.67</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_skip_connection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0703125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_odd_conv_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L108-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.usleep.USleep" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>, <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Sleep staging architecture from Perslev et al 2021.</p>
+<p>U-Net (autoencoder with skip connections) feature-extractor for sleep
+staging described in <a class="reference internal" href="#r12594e0d08b9-1" id="id86">[1]</a>.</p>
+<dl>
+<dt>For the encoder (‘down’):</dt><dd><p>– the temporal dimension shrinks (via maxpooling in the time-domain)
+– the spatial dimension expands (via more conv1d filters in the</p>
+<blockquote>
+<div><p>time-domain)</p>
+</div></blockquote>
+</dd>
+<dt>For the decoder (‘up’):</dt><dd><p>– the temporal dimension expands (via upsampling in the time-domain)
+– the spatial dimension shrinks (via fewer conv1d filters in the</p>
+<blockquote>
+<div><p>time-domain)</p>
+</div></blockquote>
+</dd>
+</dl>
+<p>Both do so at exponential rates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG or EOG channels. Set to 2 in <a class="reference internal" href="#r12594e0d08b9-1" id="id87">[1]</a> (1 EEG, 1 EOG).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – EEG sampling frequency. Set to 128 in <a class="reference internal" href="#r12594e0d08b9-1" id="id88">[1]</a>.</p></li>
+<li><p><strong>depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of conv blocks in encoding layer (number of 2x2 max pools)
+Note: each block halve the spatial dimensions of the features.</p></li>
+<li><p><strong>n_time_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 5 in <a class="reference internal" href="#r12594e0d08b9-1" id="id89">[1]</a>.</p></li>
+<li><p><strong>complexity_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Multiplicative factor for number of channels at each layer of the U-Net.
+Set to 2 in <a class="reference internal" href="#r12594e0d08b9-1" id="id90">[1]</a>.</p></li>
+<li><p><strong>with_skip_connection</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, use skip connections in decoder blocks.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs/classes. Set to 5.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the input, in seconds. Set to 30 in <a class="reference internal" href="#r12594e0d08b9-1" id="id91">[1]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the temporal convolution kernel, in seconds. Set to 9 / 128 in
+<a class="reference internal" href="#r12594e0d08b9-1" id="id92">[1]</a>.</p></li>
+<li><p><strong>ensure_odd_conv_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True and the size of the convolutional kernel is an even number, one
+will be added to it to ensure it is odd, so that the decoder blocks can
+work. This can ne useful when using different sampling rates from 128
+or 100 Hz.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r12594e0d08b9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id86">1</a>,<a role="doc-backlink" href="#id87">2</a>,<a role="doc-backlink" href="#id88">3</a>,<a role="doc-backlink" href="#id89">4</a>,<a role="doc-backlink" href="#id90">5</a>,<a role="doc-backlink" href="#id91">6</a>,<a role="doc-backlink" href="#id92">7</a>)</span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.usleep.USleep.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L297-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.usleep.USleep.forward" title="Link to this definition">#</a></dt>
+<dd><p>If input x has shape (B, S, C, T), return y_pred of shape (B, n_classes, S).
+If input x has shape (B, C, T), return y_pred of shape (B, n_classes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.models.util">
+<span id="braindecode-models-util-module"></span><h2>braindecode.models.util module<a class="headerlink" href="#module-braindecode.models.util" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.util.aggregate_probas">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.util.</span></span><span class="sig-name descname"><span class="pre">aggregate_probas</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/util.py#L119-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.util.aggregate_probas" title="Link to this definition">#</a></dt>
+<dd><p>Aggregate predicted probabilities with self-ensembling.</p>
+<p>Aggregate window-wise predicted probabilities obtained on overlapping
+sequences of windows using multiplicative voting as described in
+<a class="reference internal" href="#rb7afb86da4f7-phan2018" id="id93">[Phan2018]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>logits</strong> (<em>np.ndarray</em>) – Array of shape (n_sequences, n_classes, n_windows) containing the
+logits (i.e. the raw unnormalized scores for each class) for each
+window of each sequence.</p></li>
+<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of windows between two consecutive sequences. Default is 1
+(maximally overlapping sequences).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Array of shape ((n_rows - 1) * stride + n_windows, n_classes)
+containing the aggregated predicted probabilities for each window
+contained in the input sequences.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rb7afb86da4f7-phan2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id93">Phan2018</a><span class="fn-bracket">]</span></span>
+<p>Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., &amp;
+De Vos, M. (2018). Joint classification and prediction CNN framework
+for automatic sleep stage classification. IEEE Transactions on
+Biomedical Engineering, 66(5), 1285-1296.</p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.util.get_output_shape">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.util.</span></span><span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py#L65-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.util.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns shape of neural network output for batch size equal 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.models.util.to_dense_prediction_model">
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.util.</span></span><span class="sig-name descname"><span class="pre">to_dense_prediction_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">3)</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../braindecode-2023/venv/lib/python3.9/site-packages/sklearn/utils/deprecation.py#L15-L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.util.to_dense_prediction_model" title="Link to this definition">#</a></dt>
+<dd><p>Transform a sequential model with strides to a model that outputs
+dense predictions by removing the strides and instead inserting dilations.
+Modifies model in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – Model which modules will be modified</p></li>
+<li><p><strong>axis</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Axis to transform (in terms of intermediate output axes)
+can either be 2, 3, or (2,3).</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Does not yet work correctly for average pooling.
+Prior to version 0.1.7, there had been a bug that could move strides
+backwards one layer.</p>
+</dd></dl>
+
+</section>
+</section>
+
+
+  
+</div>
+
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.atcnet">braindecode.models.atcnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.atcnet.ATCNet"><code class="docutils literal notranslate"><span class="pre">ATCNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.atcnet.ATCNet.forward"><code class="docutils literal notranslate"><span class="pre">ATCNet.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.atcnet.ATCNet.training"><code class="docutils literal notranslate"><span class="pre">ATCNet.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.base">braindecode.models.base module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.add_log_softmax"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.add_log_softmax</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.chs_info"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.chs_info</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.get_output_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_output_shape()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.get_torchinfo_statistics"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_torchinfo_statistics()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.input_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.input_shape</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.input_window_seconds"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.input_window_seconds</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.load_state_dict"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.load_state_dict()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.mapping"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.mapping</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.n_chans"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_chans</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.n_outputs"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_outputs</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.n_times"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.n_times</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.sfreq"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.sfreq</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.EEGModuleMixin.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.to_dense_prediction_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.base.deprecated_args"><code class="docutils literal notranslate"><span class="pre">deprecated_args()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.deep4">braindecode.models.deep4 module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.deep4.Deep4Net"><code class="docutils literal notranslate"><span class="pre">Deep4Net</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.deepsleepnet">braindecode.models.deepsleepnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.deepsleepnet.DeepSleepNet"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.deepsleepnet.DeepSleepNet.forward"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eegconformer">braindecode.models.eegconformer module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegconformer.EEGConformer"><code class="docutils literal notranslate"><span class="pre">EEGConformer</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegconformer.EEGConformer.forward"><code class="docutils literal notranslate"><span class="pre">EEGConformer.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegconformer.EEGConformer.get_fc_size"><code class="docutils literal notranslate"><span class="pre">EEGConformer.get_fc_size()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eeginception">braindecode.models.eeginception module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eeginception.EEGInception"><code class="docutils literal notranslate"><span class="pre">EEGInception</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eeginception_erp">braindecode.models.eeginception_erp module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eeginception_erp.EEGInceptionERP"><code class="docutils literal notranslate"><span class="pre">EEGInceptionERP</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eeginception_mi">braindecode.models.eeginception_mi module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eeginception_mi.EEGInceptionMI"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eeginception_mi.EEGInceptionMI.forward"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eegitnet">braindecode.models.eegitnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegitnet.EEGITNet"><code class="docutils literal notranslate"><span class="pre">EEGITNet</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eegnet">braindecode.models.eegnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.bias"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.bias</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.dilation"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.dilation</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.forward"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.groups"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.groups</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.in_channels"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.in_channels</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.kernel_size"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.kernel_size</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.out_channels"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.out_channels</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.output_padding"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.output_padding</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.padding"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.padding</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.padding_mode"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.padding_mode</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.stride"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.stride</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.transposed"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.transposed</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.Conv2dWithConstraint.weight"><code class="docutils literal notranslate"><span class="pre">Conv2dWithConstraint.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.EEGNetv1"><code class="docutils literal notranslate"><span class="pre">EEGNetv1</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegnet.EEGNetv4"><code class="docutils literal notranslate"><span class="pre">EEGNetv4</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.eegresnet">braindecode.models.eegresnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.eegresnet.EEGResNet"><code class="docutils literal notranslate"><span class="pre">EEGResNet</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.functions">braindecode.models.functions module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.functions.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.functions.safe_log"><code class="docutils literal notranslate"><span class="pre">safe_log()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.functions.square"><code class="docutils literal notranslate"><span class="pre">square()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.functions.squeeze_final_output"><code class="docutils literal notranslate"><span class="pre">squeeze_final_output()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.functions.transpose_time_to_spat"><code class="docutils literal notranslate"><span class="pre">transpose_time_to_spat()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.hybrid">braindecode.models.hybrid module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.hybrid.HybridNet"><code class="docutils literal notranslate"><span class="pre">HybridNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.hybrid.HybridNet.forward"><code class="docutils literal notranslate"><span class="pre">HybridNet.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.modules">braindecode.models.modules module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.AvgPool2dWithConv"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.AvgPool2dWithConv.forward"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.AvgPool2dWithConv.training"><code class="docutils literal notranslate"><span class="pre">AvgPool2dWithConv.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d"><code class="docutils literal notranslate"><span class="pre">CausalConv1d</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.bias"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.bias</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.dilation"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.dilation</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.forward"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.groups"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.groups</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.in_channels"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.in_channels</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.kernel_size"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.kernel_size</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.out_channels"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.out_channels</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.output_padding"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.output_padding</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.padding"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.padding</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.padding_mode"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.padding_mode</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.stride"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.stride</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.transposed"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.transposed</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CausalConv1d.weight"><code class="docutils literal notranslate"><span class="pre">CausalConv1d.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CombinedConv"><code class="docutils literal notranslate"><span class="pre">CombinedConv</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CombinedConv.forward"><code class="docutils literal notranslate"><span class="pre">CombinedConv.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.CombinedConv.training"><code class="docutils literal notranslate"><span class="pre">CombinedConv.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Ensure4d"><code class="docutils literal notranslate"><span class="pre">Ensure4d</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Ensure4d.forward"><code class="docutils literal notranslate"><span class="pre">Ensure4d.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Ensure4d.training"><code class="docutils literal notranslate"><span class="pre">Ensure4d.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Expression"><code class="docutils literal notranslate"><span class="pre">Expression</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Expression.forward"><code class="docutils literal notranslate"><span class="pre">Expression.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.Expression.training"><code class="docutils literal notranslate"><span class="pre">Expression.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.IntermediateOutputWrapper"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.IntermediateOutputWrapper.forward"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.IntermediateOutputWrapper.training"><code class="docutils literal notranslate"><span class="pre">IntermediateOutputWrapper.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.MaxNormLinear"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.MaxNormLinear.forward"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.MaxNormLinear.in_features"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.in_features</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.MaxNormLinear.out_features"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.out_features</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.MaxNormLinear.weight"><code class="docutils literal notranslate"><span class="pre">MaxNormLinear.weight</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.TimeDistributed"><code class="docutils literal notranslate"><span class="pre">TimeDistributed</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.TimeDistributed.forward"><code class="docutils literal notranslate"><span class="pre">TimeDistributed.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.modules.TimeDistributed.training"><code class="docutils literal notranslate"><span class="pre">TimeDistributed.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.shallow_fbcsp">braindecode.models.shallow_fbcsp module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.shallow_fbcsp.ShallowFBCSPNet"><code class="docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.sleep_stager_blanco_2020">braindecode.models.sleep_stager_blanco_2020 module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_blanco_2020.SleepStagerBlanco2020.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.sleep_stager_chambon_2018">braindecode.models.sleep_stager_chambon_2018 module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_chambon_2018.SleepStagerChambon2018.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.sleep_stager_eldele_2021">braindecode.models.sleep_stager_eldele_2021 module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.sleep_stager_eldele_2021.SleepStagerEldele2021.return_feats"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.return_feats</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.tcn">braindecode.models.tcn module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.Chomp1d"><code class="docutils literal notranslate"><span class="pre">Chomp1d</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.Chomp1d.extra_repr"><code class="docutils literal notranslate"><span class="pre">Chomp1d.extra_repr()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.Chomp1d.forward"><code class="docutils literal notranslate"><span class="pre">Chomp1d.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.Chomp1d.training"><code class="docutils literal notranslate"><span class="pre">Chomp1d.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.TCN"><code class="docutils literal notranslate"><span class="pre">TCN</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.TCN.forward"><code class="docutils literal notranslate"><span class="pre">TCN.forward()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.TemporalBlock"><code class="docutils literal notranslate"><span class="pre">TemporalBlock</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.TemporalBlock.forward"><code class="docutils literal notranslate"><span class="pre">TemporalBlock.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tcn.TemporalBlock.training"><code class="docutils literal notranslate"><span class="pre">TemporalBlock.training</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.tidnet">braindecode.models.tidnet module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tidnet.TIDNet"><code class="docutils literal notranslate"><span class="pre">TIDNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tidnet.TIDNet.forward"><code class="docutils literal notranslate"><span class="pre">TIDNet.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.tidnet.TIDNet.num_features"><code class="docutils literal notranslate"><span class="pre">TIDNet.num_features</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.usleep">braindecode.models.usleep module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.usleep.USleep"><code class="docutils literal notranslate"><span class="pre">USleep</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.usleep.USleep.forward"><code class="docutils literal notranslate"><span class="pre">USleep.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.models.util">braindecode.models.util module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.util.aggregate_probas"><code class="docutils literal notranslate"><span class="pre">aggregate_probas()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.util.get_output_shape"><code class="docutils literal notranslate"><span class="pre">get_output_shape()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.util.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">to_dense_prediction_model()</span></code></a></li>
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+  
+  <section id="module-braindecode.preprocessing">
+<span id="braindecode-preprocessing-package"></span><h1>braindecode.preprocessing package<a class="headerlink" href="#module-braindecode.preprocessing" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Crop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Crop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Crop" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Crop raw data file.</p>
+<blockquote>
+<div><p>Limit the data from the raw file to go between specific times. Note
+that the new <code class="docutils literal notranslate"><span class="pre">tmin</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">t=0</span></code> for all subsequently
+called functions (e.g., <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.time_as_index" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">time_as_index()</span></code></a>, or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Epochs</span></code></a>). New <a class="reference external" href="https://mne.tools/stable/glossary.html#term-first_samp" title="(in MNE v1.5)"><span class="xref std std-term">first_samp</span></a> and <a class="reference external" href="https://mne.tools/stable/glossary.html#term-last_samp" title="(in MNE v1.5)"><span class="xref std std-term">last_samp</span></a>
+are set accordingly.</p>
+<p>Thus function operates in-place on the instance.
+Use <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.copy" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.copy()</span></code></a> if operation on a copy is desired.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>tmin</strong><span class="classifier">float</span></dt><dd><p>Start time of the raw data to use in seconds (must be &gt;= 0).</p>
+</dd>
+<dt><strong>tmax</strong><span class="classifier">float</span></dt><dd><p>End time of the raw data to use in seconds (cannot exceed data duration).</p>
+</dd>
+<dt><strong>include_tmax</strong><span class="classifier">bool</span></dt><dd><p>If True (default), include tmax. If False, exclude tmax (similar to how
+Python indexing typically works).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>raw</strong><span class="classifier">instance of Raw</span></dt><dd><p>The cropped raw object, modified in-place.</p>
+</dd>
+<dt>See more details in mne.io.base.crop</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Crop.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'crop'</span></em><a class="headerlink" href="#braindecode.preprocessing.Crop.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.DropChannels">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">DropChannels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.DropChannels" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Drop channel(s).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ch_names</strong><span class="classifier">iterable or str</span></dt><dd><p>Iterable (e.g. list) of channel name(s) or channel name to remove.</p>
+</dd>
+<dt><strong>on_missing</strong><span class="classifier">‘raise’ | ‘warn’ | ‘ignore’</span></dt><dd><p>Can be <code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (default) to raise an error, <code class="docutils literal notranslate"><span class="pre">'warn'</span></code> to emit a
+warning, or <code class="docutils literal notranslate"><span class="pre">'ignore'</span></code> to ignore when entries in ch_names are not present in the raw instance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.23.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">reorder_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_types</span></code></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.channels.channels.drop_channels</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.DropChannels.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'drop_channels'</span></em><a class="headerlink" href="#braindecode.preprocessing.DropChannels.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Filter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">l_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fir'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iir_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hamming'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_design</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'firwin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_by_annotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Filter" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Filter a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>l_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the lower pass-band edge; for IIR filters, the lower
+cutoff frequency. If None the data are only low-passed.</p>
+</dd>
+<dt><strong>h_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the upper pass-band edge; for IIR filters, the upper
+cutoff frequency. If None the data are only high-passed.</p>
+</dd>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all data channels. Note that channels
+in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>filter_length</strong><span class="classifier">str | int</span></dt><dd><p>Length of the FIR filter to use (if applicable):</p>
+<ul class="simple">
+<li><p><strong>‘auto’ (default)</strong>: The filter length is chosen based
+on the size of the transition regions (6.6 times the reciprocal
+of the shortest transition band for fir_window=’hamming’
+and fir_design=”firwin2”, and half that for “firwin”).</p></li>
+<li><p><strong>str</strong>: A human-readable time in
+units of “s” or “ms” (e.g., “10s” or “5500ms”) will be
+converted to that number of samples if <code class="docutils literal notranslate"><span class="pre">phase=&quot;zero&quot;</span></code>, or
+the shortest power-of-two length at least that duration for
+<code class="docutils literal notranslate"><span class="pre">phase=&quot;zero-double&quot;</span></code>.</p></li>
+<li><p><strong>int</strong>: Specified length in samples. For fir_design=”firwin”,
+this should not be used.</p></li>
+</ul>
+</dd>
+<dt><strong>l_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the low cut-off frequency in Hz
+(high pass or cutoff 1 in bandpass). Can be “auto”
+(default) to use a multiple of <code class="docutils literal notranslate"><span class="pre">l_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">l_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">l_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>h_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the high cut-off frequency in Hz
+(low pass or cutoff 2 in bandpass). Can be “auto”
+(default in 0.14) to use a multiple of <code class="docutils literal notranslate"><span class="pre">h_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">h_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">2.</span><span class="p">),</span> <span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">2.</span> <span class="o">-</span> <span class="n">h_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly and method=’fir’.</p>
+</dd>
+<dt><strong>method</strong><span class="classifier">str</span></dt><dd><p>‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR
+forward-backward filtering (via filtfilt).</p>
+</dd>
+<dt><strong>iir_params</strong><span class="classifier">dict | None</span></dt><dd><p>Dictionary of parameters to use for IIR filtering.
+If iir_params is None and method=”iir”, 4th order Butterworth will be used.
+For more information, see <a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter()</span></code></a>.</p>
+</dd>
+<dt><strong>phase</strong><span class="classifier">str</span></dt><dd><p>Phase of the filter.
+When <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>, symmetric linear-phase FIR filters are constructed,
+and if <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default), the delay of this filter is compensated
+for, making it non-causal. If <code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code>,
+then this filter is applied twice, once forward, and once backward
+(also making it non-causal). If <code class="docutils literal notranslate"><span class="pre">'minimum'</span></code>, then a minimum-phase filter
+will be constructed and applied, which is causal but has weaker stop-band
+suppression.
+When <code class="docutils literal notranslate"><span class="pre">method='iir'</span></code>, <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default) or
+<code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code> constructs and applies IIR filter twice, once
+forward, and once backward (making it non-causal) using filtfilt.
+If <code class="docutils literal notranslate"><span class="pre">phase='forward'</span></code>, it constructs and applies forward IIR filter using
+lfilter.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.13.</span></p>
+</div>
+</dd>
+<dt><strong>fir_window</strong><span class="classifier">str</span></dt><dd><p>The window to use in FIR design, can be “hamming” (default),
+“hann” (default in 0.13), or “blackman”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>fir_design</strong><span class="classifier">str</span></dt><dd><p>Can be “firwin” (default) to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin.html#scipy.signal.firwin" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin()</span></code></a>,
+or “firwin2” to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin2.html#scipy.signal.firwin2" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin2()</span></code></a>. “firwin” uses
+a time-domain design technique that generally gives improved
+attenuation using fewer samples than “firwin2”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>skip_by_annotation</strong><span class="classifier">str | list of str</span></dt><dd><p>If a string (or list of str), any annotation segment that begins
+with the given string will not be included in filtering, and
+segments on either side of the given excluded annotated segment
+will be filtered separately (i.e., as independent signals).
+The default (<code class="docutils literal notranslate"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></code> will separately filter
+any segments that were concatenated by <a class="reference external" href="https://mne.tools/stable/generated/mne.concatenate_raws.html#mne.concatenate_raws" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.concatenate_raws()</span></code></a>
+or <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.append" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.append()</span></code></a>, or separated during acquisition.
+To disable, provide an empty list. Only used if <code class="docutils literal notranslate"><span class="pre">inst</span></code> is raw.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.16..</span></p>
+</div>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.</p>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Epochs, Evoked, or Raw</span></dt><dd><p>The filtered data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.Evoked.html#mne.Evoked.savgol_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.Evoked.savgol_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.notch_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.notch_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.resample" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.resample</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.filter_data.html#mne.filter.filter_data" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.filter_data</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.io.base.filter</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Filter.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'filter'</span></em><a class="headerlink" href="#braindecode.preprocessing.Filter.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Pick">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Pick</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">picks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Pick" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Pick a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all channels. Note that channels in
+<code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>exclude</strong><span class="classifier">list | str</span></dt><dd><p>Set of channels to exclude, only used when picking based on
+types (e.g., exclude=”bads” when picks=”meg”).</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.24.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+<dt>See more details in mne.channels.channels.pick</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Pick.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'pick'</span></em><a class="headerlink" href="#braindecode.preprocessing.Pick.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L27-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Preprocessor for an MNE Raw or Epochs object.</p>
+<p>Applies the provided preprocessing function to the data of a Raw or Epochs
+object.
+If the function is provided as a string, the method with that name will be
+used (e.g., ‘pick_channels’, ‘filter’, etc.).
+If it is provided as a callable and <cite>apply_on_array</cite> is True, the
+<cite>apply_function</cite> method of Raw and Epochs object will be used to apply the
+function on the internal arrays of Raw and Epochs.
+If <cite>apply_on_array</cite> is False, the callable must directly modify the Raw or
+Epochs object (e.g., by calling its method(s) or modifying its attributes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
+If callable, directly apply the callable to the object.</p></li>
+<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
+and Epochs object will be used to run <cite>fn</cite> on the underlying arrays
+directly. If False, <cite>fn</cite> must directly modify the Raw or Epochs object.</p></li>
+<li><p><strong>kwargs</strong> – Keyword arguments to be forwarded to the MNE function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L66-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor.apply" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Resample">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">npad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'boxcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Resample" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Resample an array.</p>
+<blockquote>
+<div><p>Operates along the last dimension of the array.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>x</strong><span class="classifier">ndarray</span></dt><dd><p>Signal to resample.</p>
+</dd>
+<dt><strong>up</strong><span class="classifier">float</span></dt><dd><p>Factor to upsample by.</p>
+</dd>
+<dt><strong>down</strong><span class="classifier">float</span></dt><dd><p>Factor to downsample by.</p>
+</dd>
+<dt><strong>npad</strong><span class="classifier">int | str</span></dt><dd><p>Amount to pad the start and end of the data.
+Can also be “auto” to use a padding that will result in
+a power-of-two size (can be much faster).</p>
+</dd>
+<dt><strong>axis</strong><span class="classifier">int</span></dt><dd><p>Axis along which to resample (default is the last axis).</p>
+</dd>
+<dt><strong>window</strong><span class="classifier">str | tuple</span></dt><dd><p>Frequency-domain window to use in resampling.
+See <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html#scipy.signal.resample" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.resample()</span></code></a>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly.</p>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.
+The default is <code class="docutils literal notranslate"><span class="pre">'reflect_limited'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>The x array resampled.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See more details in mne.filter.resample</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Resample.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'resample'</span></em><a class="headerlink" href="#braindecode.preprocessing.Resample.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.SetEEGReference">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">SetEEGReference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">joint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.SetEEGReference" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Specify which reference to use for EEG data.</p>
+<blockquote>
+<div><p>Use this function to explicitly specify the desired reference for EEG.
+This can be either an existing electrode or a new virtual channel.
+This function will re-reference the data according to the desired
+reference.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ref_channels</strong><span class="classifier">list of str | str</span></dt><dd><p>Can be:</p>
+<ul class="simple">
+<li><p>The name(s) of the channel(s) used to construct the reference.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'average'</span></code> to apply an average reference (default)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'REST'</span></code> to use the Reference Electrode Standardization Technique
+infinity reference <a href="#id1"><span class="problematic" id="id2">:footcite:`Yao2001`</span></a>.</p></li>
+<li><p>An empty list, in which case MNE will not attempt any re-referencing of
+the data</p></li>
+</ul>
+</dd>
+<dt><strong>projection</strong><span class="classifier">bool</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> this argument specifies if the
+average reference should be computed as a projection (True) or not
+(False; default). If <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>, the average reference is
+added as a projection and is not applied to the data (it can be
+applied afterwards with the <code class="docutils literal notranslate"><span class="pre">apply_proj</span></code> method). If
+<code class="docutils literal notranslate"><span class="pre">projection=False</span></code>, the average reference is directly applied to
+the data. If <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> is not <code class="docutils literal notranslate"><span class="pre">'average'</span></code>, <code class="docutils literal notranslate"><span class="pre">projection</span></code>
+must be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> (the default in this case).</p>
+</dd>
+<dt><strong>ch_type</strong><span class="classifier">list of str | str</span></dt><dd><p>The name of the channel type to apply the reference to.
+Valid channel types are <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, <code class="docutils literal notranslate"><span class="pre">'eeg'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ecog'</span></code>, <code class="docutils literal notranslate"><span class="pre">'seeg'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'dbs'</span></code>. If <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, the first channel type of eeg, ecog, seeg or dbs
+that is found (in that order) will be selected.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">list-of-str</span></code> is now supported with <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+</div>
+</dd>
+<dt><strong>forward</strong><span class="classifier">instance of Forward | None</span></dt><dd><p>Forward solution to use. Only used with <code class="docutils literal notranslate"><span class="pre">ref_channels='REST'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.21.</span></p>
+</div>
+</dd>
+<dt><strong>joint</strong><span class="classifier">bool</span></dt><dd><p>How to handle list-of-str <code class="docutils literal notranslate"><span class="pre">ch_type</span></code>. If False (default), one projector
+is created per channel type. If True, one projector is created across
+all channel types. This is only used when <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.2.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw | Epochs | Evoked</span></dt><dd><p>Data with EEG channels re-referenced. If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code> a projection will be added instead of
+directly re-referencing the data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.set_bipolar_reference.html#mne.set_bipolar_reference" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.set_bipolar_reference</span></code></a></dt><dd><p>Convenience function for creating bipolar references.</p>
+</dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Some common referencing schemes and the corresponding value for the
+<code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> parameter:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>Average reference:</dt><dd><p>A new virtual reference electrode is created by averaging the current
+EEG signal by setting <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>. Bad EEG channels are
+automatically excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>A single electrode:</dt><dd><p>Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list containing the name of the channel that
+will act as the new reference, for example <code class="docutils literal notranslate"><span class="pre">ref_channels=['Cz']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>The mean of multiple electrodes:</dt><dd><p>A new virtual reference electrode is created by computing the average
+of the current EEG signal recorded from two or more selected channels.
+Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list of channel names, indicating which
+channels to use. For example, to apply an average mastoid reference,
+when using the 10-20 naming scheme, set <code class="docutils literal notranslate"><span class="pre">ref_channels=['M1',</span> <span class="pre">'M2']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>REST</dt><dd><p>The given EEG electrodes are referenced to a point at infinity using the
+lead fields in <code class="docutils literal notranslate"><span class="pre">forward</span></code>, which helps standardize the signals.</p>
+</dd>
+</dl>
+</li>
+</ul>
+<ol class="arabic simple">
+<li><p>If a reference is requested that is not the average reference, this
+function removes any pre-existing average reference projections.</p></li>
+<li><p>During source localization, the EEG signal should have an average
+reference.</p></li>
+<li><p>In order to apply a reference, the data must be preloaded. This is not
+necessary if <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p></li>
+<li><p>For an average or REST reference, bad EEG channels are automatically
+excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p></li>
+</ol>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.9.0.</span></p>
+</div>
+<p>See more details in mne.channels.channels.set_eeg_reference</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.SetEEGReference.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'set_eeg_reference'</span></em><a class="headerlink" href="#braindecode.preprocessing.SetEEGReference.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.create_fixed_length_windows">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_fixed_length_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L146-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_fixed_length_windows" title="Link to this definition">#</a></dt>
+<dd><p>Windower that creates sliding windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<em>ConcatDataset</em>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from beginning of recording in samples.</p></li>
+<li><p><strong>stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stop offset from beginning of recording in samples. If None, set to be
+the end of the recording.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size in samples. If None, set to be the maximum possible window size, ie length of
+the recording, once offsets are accounted for.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
+windows will not overlap.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><em>None</em>) – Whether or not have a last overlapping window, when windows do not
+equally divide the continuous signal. Must be set to a bool if window size and stride are
+not None.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.create_windows_from_events">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L29-L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_events" title="Link to this definition">#</a></dt>
+<dd><p>Create windows based on events in mne.Raw.</p>
+<p>This function extracts windows of size window_size_samples in the interval
+[trial onset + trial_start_offset_samples, trial onset + trial duration +
+trial_stop_offset_samples] around each trial, with a separation of
+window_stride_samples between consecutive windows. If the last window
+around an event does not end at trial_stop_offset_samples and
+drop_last_window is set to False, an additional overlapping window that
+ends at trial_stop_offset_samples is created.</p>
+<p>Windows are extracted from the interval defined by the following:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>                                        <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+                <span class="n">trial</span> <span class="n">onset</span>                <span class="n">duration</span>
+<span class="o">|--------------------|------------------------|-----------------------|</span>
+<span class="n">trial</span> <span class="n">onset</span> <span class="o">-</span>                                             <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+<span class="n">trial_start_offset_samples</span>                                   <span class="n">duration</span> <span class="o">+</span>
+                                            <span class="n">trial_stop_offset_samples</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from original trial onsets, in samples. Defaults to zero.</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Stop offset from original trial stop, in samples. Defaults to zero.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size. If None, the window size is inferred from the original
+trial size of the first trial and trial_start_offset_samples and
+trial_stop_offset_samples.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows, in samples. If None, the window stride is
+inferred from the original trial size of the first trial and
+trial_start_offset_samples and trial_stop_offset_samples.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If False, an additional overlapping window that ends at
+trial_stop_offset_samples will be extracted around each event when the
+last window does not end exactly at trial_stop_offset_samples.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to numerical target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects. This is useful to
+reduce disk reading overhead when returning windows in a training
+scenario, however very large data might not fit into memory.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials with inconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.create_windows_from_target_channels">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_target_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L426-L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_target_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_demean">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_demean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L314-L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_demean" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving demeanining.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Deman the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Demean data before to this index with regular demeaning.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>demeaned</strong> – Demeaned data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_standardize">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L264-L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_standardize" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving standardization.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Then, compute exponential moving variance <span class="math notranslate nohighlight">\(v_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}\)</span>.</p>
+<p>Finally, standardize the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t) / max(\sqrt{-&gt;v_t}, eps)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Standardize data before to this index with regular standardization.</p></li>
+<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Stabilizer for division by zero variance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>standardized</strong> – Standardized data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.filterbank">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">filterbank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_bands</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_original_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by_frequency_band</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">mne_filter_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L349-L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.filterbank" title="Link to this definition">#</a></dt>
+<dd><p>Applies multiple bandpass filters to the signals in raw. The raw will be
+modified in-place and number of channels in raw will be updated to
+len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if
+drop_original_signals).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – The raw signals to be filtered.</p></li>
+<li><p><strong>frequency_bands</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>)</em>) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</p></li>
+<li><p><strong>drop_original_signals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to drop the original unfiltered signals</p></li>
+<li><p><strong>order_by_frequency_band</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True will return channels ordered by frequency bands, so if there
+are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned
+channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order
+will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].</p></li>
+<li><p><strong>mne_filter_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Keyword arguments for filtering supported by mne.io.Raw.filter().
+Please refer to mne for a detailed explanation.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">preprocess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L88-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess" title="Link to this definition">#</a></dt>
+<dd><p>Apply preprocessors to a concat dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of BaseDataset or WindowsDataset datasets to be preprocessed.</p></li>
+<li><p><strong>preprocessors</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference internal" href="#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><em>Preprocessor</em></a><em>)</em>) – List of Preprocessor objects to apply to the dataset.</p></li>
+<li><p><strong>save_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – If a string, the preprocessed data will be saved under the specified
+directory and the datasets in <code class="docutils literal notranslate"><span class="pre">concat_ds</span></code> will be reloaded with
+<cite>preload=False</cite>.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <cite>save_dir</cite> is provided, controls whether to delete the old
+subdirectories that will be written to under <cite>save_dir</cite>. If False and
+the corresponding subdirectories already exist, a <code class="docutils literal notranslate"><span class="pre">FileExistsError</span></code>
+will be raised.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Number of jobs for parallel execution. See <cite>joblib.Parallel</cite> for
+a more detailed explanation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Preprocessed dataset.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.preprocessing.mne_preprocess">
+<span id="braindecode-preprocessing-mne-preprocess-module"></span><h2>braindecode.preprocessing.mne_preprocess module<a class="headerlink" href="#module-braindecode.preprocessing.mne_preprocess" title="Link to this heading">#</a></h2>
+<p>Preprocessor objects based on mne methods.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Crop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">Crop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Crop" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Crop raw data file.</p>
+<blockquote>
+<div><p>Limit the data from the raw file to go between specific times. Note
+that the new <code class="docutils literal notranslate"><span class="pre">tmin</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">t=0</span></code> for all subsequently
+called functions (e.g., <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.time_as_index" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">time_as_index()</span></code></a>, or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Epochs</span></code></a>). New <a class="reference external" href="https://mne.tools/stable/glossary.html#term-first_samp" title="(in MNE v1.5)"><span class="xref std std-term">first_samp</span></a> and <a class="reference external" href="https://mne.tools/stable/glossary.html#term-last_samp" title="(in MNE v1.5)"><span class="xref std std-term">last_samp</span></a>
+are set accordingly.</p>
+<p>Thus function operates in-place on the instance.
+Use <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.copy" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.copy()</span></code></a> if operation on a copy is desired.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>tmin</strong><span class="classifier">float</span></dt><dd><p>Start time of the raw data to use in seconds (must be &gt;= 0).</p>
+</dd>
+<dt><strong>tmax</strong><span class="classifier">float</span></dt><dd><p>End time of the raw data to use in seconds (cannot exceed data duration).</p>
+</dd>
+<dt><strong>include_tmax</strong><span class="classifier">bool</span></dt><dd><p>If True (default), include tmax. If False, exclude tmax (similar to how
+Python indexing typically works).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>raw</strong><span class="classifier">instance of Raw</span></dt><dd><p>The cropped raw object, modified in-place.</p>
+</dd>
+<dt>See more details in mne.io.base.crop</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Crop.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'crop'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Crop.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.DropChannels">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">DropChannels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.DropChannels" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Drop channel(s).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ch_names</strong><span class="classifier">iterable or str</span></dt><dd><p>Iterable (e.g. list) of channel name(s) or channel name to remove.</p>
+</dd>
+<dt><strong>on_missing</strong><span class="classifier">‘raise’ | ‘warn’ | ‘ignore’</span></dt><dd><p>Can be <code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (default) to raise an error, <code class="docutils literal notranslate"><span class="pre">'warn'</span></code> to emit a
+warning, or <code class="docutils literal notranslate"><span class="pre">'ignore'</span></code> to ignore when entries in ch_names are not present in the raw instance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.23.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">reorder_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_types</span></code></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.channels.channels.drop_channels</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.DropChannels.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'drop_channels'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.DropChannels.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Filter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">Filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">l_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fir'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iir_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hamming'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_design</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'firwin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_by_annotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Filter" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Filter a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>l_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the lower pass-band edge; for IIR filters, the lower
+cutoff frequency. If None the data are only low-passed.</p>
+</dd>
+<dt><strong>h_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the upper pass-band edge; for IIR filters, the upper
+cutoff frequency. If None the data are only high-passed.</p>
+</dd>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all data channels. Note that channels
+in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>filter_length</strong><span class="classifier">str | int</span></dt><dd><p>Length of the FIR filter to use (if applicable):</p>
+<ul class="simple">
+<li><p><strong>‘auto’ (default)</strong>: The filter length is chosen based
+on the size of the transition regions (6.6 times the reciprocal
+of the shortest transition band for fir_window=’hamming’
+and fir_design=”firwin2”, and half that for “firwin”).</p></li>
+<li><p><strong>str</strong>: A human-readable time in
+units of “s” or “ms” (e.g., “10s” or “5500ms”) will be
+converted to that number of samples if <code class="docutils literal notranslate"><span class="pre">phase=&quot;zero&quot;</span></code>, or
+the shortest power-of-two length at least that duration for
+<code class="docutils literal notranslate"><span class="pre">phase=&quot;zero-double&quot;</span></code>.</p></li>
+<li><p><strong>int</strong>: Specified length in samples. For fir_design=”firwin”,
+this should not be used.</p></li>
+</ul>
+</dd>
+<dt><strong>l_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the low cut-off frequency in Hz
+(high pass or cutoff 1 in bandpass). Can be “auto”
+(default) to use a multiple of <code class="docutils literal notranslate"><span class="pre">l_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">l_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">l_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>h_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the high cut-off frequency in Hz
+(low pass or cutoff 2 in bandpass). Can be “auto”
+(default in 0.14) to use a multiple of <code class="docutils literal notranslate"><span class="pre">h_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">h_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">2.</span><span class="p">),</span> <span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">2.</span> <span class="o">-</span> <span class="n">h_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly and method=’fir’.</p>
+</dd>
+<dt><strong>method</strong><span class="classifier">str</span></dt><dd><p>‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR
+forward-backward filtering (via filtfilt).</p>
+</dd>
+<dt><strong>iir_params</strong><span class="classifier">dict | None</span></dt><dd><p>Dictionary of parameters to use for IIR filtering.
+If iir_params is None and method=”iir”, 4th order Butterworth will be used.
+For more information, see <a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter()</span></code></a>.</p>
+</dd>
+<dt><strong>phase</strong><span class="classifier">str</span></dt><dd><p>Phase of the filter.
+When <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>, symmetric linear-phase FIR filters are constructed,
+and if <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default), the delay of this filter is compensated
+for, making it non-causal. If <code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code>,
+then this filter is applied twice, once forward, and once backward
+(also making it non-causal). If <code class="docutils literal notranslate"><span class="pre">'minimum'</span></code>, then a minimum-phase filter
+will be constructed and applied, which is causal but has weaker stop-band
+suppression.
+When <code class="docutils literal notranslate"><span class="pre">method='iir'</span></code>, <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default) or
+<code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code> constructs and applies IIR filter twice, once
+forward, and once backward (making it non-causal) using filtfilt.
+If <code class="docutils literal notranslate"><span class="pre">phase='forward'</span></code>, it constructs and applies forward IIR filter using
+lfilter.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.13.</span></p>
+</div>
+</dd>
+<dt><strong>fir_window</strong><span class="classifier">str</span></dt><dd><p>The window to use in FIR design, can be “hamming” (default),
+“hann” (default in 0.13), or “blackman”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>fir_design</strong><span class="classifier">str</span></dt><dd><p>Can be “firwin” (default) to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin.html#scipy.signal.firwin" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin()</span></code></a>,
+or “firwin2” to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin2.html#scipy.signal.firwin2" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin2()</span></code></a>. “firwin” uses
+a time-domain design technique that generally gives improved
+attenuation using fewer samples than “firwin2”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>skip_by_annotation</strong><span class="classifier">str | list of str</span></dt><dd><p>If a string (or list of str), any annotation segment that begins
+with the given string will not be included in filtering, and
+segments on either side of the given excluded annotated segment
+will be filtered separately (i.e., as independent signals).
+The default (<code class="docutils literal notranslate"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></code> will separately filter
+any segments that were concatenated by <a class="reference external" href="https://mne.tools/stable/generated/mne.concatenate_raws.html#mne.concatenate_raws" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.concatenate_raws()</span></code></a>
+or <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.append" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.append()</span></code></a>, or separated during acquisition.
+To disable, provide an empty list. Only used if <code class="docutils literal notranslate"><span class="pre">inst</span></code> is raw.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.16..</span></p>
+</div>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.</p>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Epochs, Evoked, or Raw</span></dt><dd><p>The filtered data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.Evoked.html#mne.Evoked.savgol_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.Evoked.savgol_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.notch_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.notch_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.resample" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.resample</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.filter_data.html#mne.filter.filter_data" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.filter_data</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.io.base.filter</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Filter.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'filter'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Filter.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Pick">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">Pick</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">picks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Pick" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Pick a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all channels. Note that channels in
+<code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>exclude</strong><span class="classifier">list | str</span></dt><dd><p>Set of channels to exclude, only used when picking based on
+types (e.g., exclude=”bads” when picks=”meg”).</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.24.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+<dt>See more details in mne.channels.channels.pick</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Pick.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'pick'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Pick.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Preprocessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L27-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Preprocessor" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Preprocessor for an MNE Raw or Epochs object.</p>
+<p>Applies the provided preprocessing function to the data of a Raw or Epochs
+object.
+If the function is provided as a string, the method with that name will be
+used (e.g., ‘pick_channels’, ‘filter’, etc.).
+If it is provided as a callable and <cite>apply_on_array</cite> is True, the
+<cite>apply_function</cite> method of Raw and Epochs object will be used to apply the
+function on the internal arrays of Raw and Epochs.
+If <cite>apply_on_array</cite> is False, the callable must directly modify the Raw or
+Epochs object (e.g., by calling its method(s) or modifying its attributes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
+If callable, directly apply the callable to the object.</p></li>
+<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
+and Epochs object will be used to run <cite>fn</cite> on the underlying arrays
+directly. If False, <cite>fn</cite> must directly modify the Raw or Epochs object.</p></li>
+<li><p><strong>kwargs</strong> – Keyword arguments to be forwarded to the MNE function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Preprocessor.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L66-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Preprocessor.apply" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Resample">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">Resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">npad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'boxcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Resample" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Resample an array.</p>
+<blockquote>
+<div><p>Operates along the last dimension of the array.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>x</strong><span class="classifier">ndarray</span></dt><dd><p>Signal to resample.</p>
+</dd>
+<dt><strong>up</strong><span class="classifier">float</span></dt><dd><p>Factor to upsample by.</p>
+</dd>
+<dt><strong>down</strong><span class="classifier">float</span></dt><dd><p>Factor to downsample by.</p>
+</dd>
+<dt><strong>npad</strong><span class="classifier">int | str</span></dt><dd><p>Amount to pad the start and end of the data.
+Can also be “auto” to use a padding that will result in
+a power-of-two size (can be much faster).</p>
+</dd>
+<dt><strong>axis</strong><span class="classifier">int</span></dt><dd><p>Axis along which to resample (default is the last axis).</p>
+</dd>
+<dt><strong>window</strong><span class="classifier">str | tuple</span></dt><dd><p>Frequency-domain window to use in resampling.
+See <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html#scipy.signal.resample" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.resample()</span></code></a>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly.</p>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.
+The default is <code class="docutils literal notranslate"><span class="pre">'reflect_limited'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>The x array resampled.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See more details in mne.filter.resample</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.Resample.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'resample'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.Resample.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.SetEEGReference">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.mne_preprocess.</span></span><span class="sig-name descname"><span class="pre">SetEEGReference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">joint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.SetEEGReference" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></p>
+<p>Specify which reference to use for EEG data.</p>
+<blockquote>
+<div><p>Use this function to explicitly specify the desired reference for EEG.
+This can be either an existing electrode or a new virtual channel.
+This function will re-reference the data according to the desired
+reference.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ref_channels</strong><span class="classifier">list of str | str</span></dt><dd><p>Can be:</p>
+<ul class="simple">
+<li><p>The name(s) of the channel(s) used to construct the reference.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'average'</span></code> to apply an average reference (default)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'REST'</span></code> to use the Reference Electrode Standardization Technique
+infinity reference <a href="#id3"><span class="problematic" id="id4">:footcite:`Yao2001`</span></a>.</p></li>
+<li><p>An empty list, in which case MNE will not attempt any re-referencing of
+the data</p></li>
+</ul>
+</dd>
+<dt><strong>projection</strong><span class="classifier">bool</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> this argument specifies if the
+average reference should be computed as a projection (True) or not
+(False; default). If <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>, the average reference is
+added as a projection and is not applied to the data (it can be
+applied afterwards with the <code class="docutils literal notranslate"><span class="pre">apply_proj</span></code> method). If
+<code class="docutils literal notranslate"><span class="pre">projection=False</span></code>, the average reference is directly applied to
+the data. If <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> is not <code class="docutils literal notranslate"><span class="pre">'average'</span></code>, <code class="docutils literal notranslate"><span class="pre">projection</span></code>
+must be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> (the default in this case).</p>
+</dd>
+<dt><strong>ch_type</strong><span class="classifier">list of str | str</span></dt><dd><p>The name of the channel type to apply the reference to.
+Valid channel types are <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, <code class="docutils literal notranslate"><span class="pre">'eeg'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ecog'</span></code>, <code class="docutils literal notranslate"><span class="pre">'seeg'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'dbs'</span></code>. If <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, the first channel type of eeg, ecog, seeg or dbs
+that is found (in that order) will be selected.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">list-of-str</span></code> is now supported with <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+</div>
+</dd>
+<dt><strong>forward</strong><span class="classifier">instance of Forward | None</span></dt><dd><p>Forward solution to use. Only used with <code class="docutils literal notranslate"><span class="pre">ref_channels='REST'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.21.</span></p>
+</div>
+</dd>
+<dt><strong>joint</strong><span class="classifier">bool</span></dt><dd><p>How to handle list-of-str <code class="docutils literal notranslate"><span class="pre">ch_type</span></code>. If False (default), one projector
+is created per channel type. If True, one projector is created across
+all channel types. This is only used when <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.2.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw | Epochs | Evoked</span></dt><dd><p>Data with EEG channels re-referenced. If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code> a projection will be added instead of
+directly re-referencing the data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.set_bipolar_reference.html#mne.set_bipolar_reference" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.set_bipolar_reference</span></code></a></dt><dd><p>Convenience function for creating bipolar references.</p>
+</dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Some common referencing schemes and the corresponding value for the
+<code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> parameter:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>Average reference:</dt><dd><p>A new virtual reference electrode is created by averaging the current
+EEG signal by setting <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>. Bad EEG channels are
+automatically excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>A single electrode:</dt><dd><p>Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list containing the name of the channel that
+will act as the new reference, for example <code class="docutils literal notranslate"><span class="pre">ref_channels=['Cz']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>The mean of multiple electrodes:</dt><dd><p>A new virtual reference electrode is created by computing the average
+of the current EEG signal recorded from two or more selected channels.
+Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list of channel names, indicating which
+channels to use. For example, to apply an average mastoid reference,
+when using the 10-20 naming scheme, set <code class="docutils literal notranslate"><span class="pre">ref_channels=['M1',</span> <span class="pre">'M2']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>REST</dt><dd><p>The given EEG electrodes are referenced to a point at infinity using the
+lead fields in <code class="docutils literal notranslate"><span class="pre">forward</span></code>, which helps standardize the signals.</p>
+</dd>
+</dl>
+</li>
+</ul>
+<ol class="arabic simple">
+<li><p>If a reference is requested that is not the average reference, this
+function removes any pre-existing average reference projections.</p></li>
+<li><p>During source localization, the EEG signal should have an average
+reference.</p></li>
+<li><p>In order to apply a reference, the data must be preloaded. This is not
+necessary if <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p></li>
+<li><p>For an average or REST reference, bad EEG channels are automatically
+excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p></li>
+</ol>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.9.0.</span></p>
+</div>
+<p>See more details in mne.channels.channels.set_eeg_reference</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.preprocessing.mne_preprocess.SetEEGReference.fn">
+<span class="sig-name descname"><span class="pre">fn</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'set_eeg_reference'</span></em><a class="headerlink" href="#braindecode.preprocessing.mne_preprocess.SetEEGReference.fn" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.preprocessing.preprocess">
+<span id="braindecode-preprocessing-preprocess-module"></span><h2>braindecode.preprocessing.preprocess module<a class="headerlink" href="#module-braindecode.preprocessing.preprocess" title="Link to this heading">#</a></h2>
+<p>Preprocessors that work on Raw or Epochs objects.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.Preprocessor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.preprocess.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L27-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.Preprocessor" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Preprocessor for an MNE Raw or Epochs object.</p>
+<p>Applies the provided preprocessing function to the data of a Raw or Epochs
+object.
+If the function is provided as a string, the method with that name will be
+used (e.g., ‘pick_channels’, ‘filter’, etc.).
+If it is provided as a callable and <cite>apply_on_array</cite> is True, the
+<cite>apply_function</cite> method of Raw and Epochs object will be used to apply the
+function on the internal arrays of Raw and Epochs.
+If <cite>apply_on_array</cite> is False, the callable must directly modify the Raw or
+Epochs object (e.g., by calling its method(s) or modifying its attributes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
+If callable, directly apply the callable to the object.</p></li>
+<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
+and Epochs object will be used to run <cite>fn</cite> on the underlying arrays
+directly. If False, <cite>fn</cite> must directly modify the Raw or Epochs object.</p></li>
+<li><p><strong>kwargs</strong> – Keyword arguments to be forwarded to the MNE function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.Preprocessor.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L66-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.Preprocessor.apply" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.exponential_moving_demean">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.preprocess.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_demean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L314-L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.exponential_moving_demean" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving demeanining.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Deman the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Demean data before to this index with regular demeaning.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>demeaned</strong> – Demeaned data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.exponential_moving_standardize">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.preprocess.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L264-L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.exponential_moving_standardize" title="Link to this definition">#</a></dt>
+<dd><p>Perform exponential moving standardization.</p>
+<p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
+<p>Then, compute exponential moving variance <span class="math notranslate nohighlight">\(v_t\)</span> at time <cite>t</cite> as
+<span class="math notranslate nohighlight">\(v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}\)</span>.</p>
+<p>Finally, standardize the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
+<span class="math notranslate nohighlight">\(x'_t=(x_t - m_t) / max(\sqrt{-&gt;v_t}, eps)\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Standardize data before to this index with regular standardization.</p></li>
+<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Stabilizer for division by zero variance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>standardized</strong> – Standardized data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.filterbank">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.preprocess.</span></span><span class="sig-name descname"><span class="pre">filterbank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_bands</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_original_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by_frequency_band</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">mne_filter_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L349-L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.filterbank" title="Link to this definition">#</a></dt>
+<dd><p>Applies multiple bandpass filters to the signals in raw. The raw will be
+modified in-place and number of channels in raw will be updated to
+len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if
+drop_original_signals).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – The raw signals to be filtered.</p></li>
+<li><p><strong>frequency_bands</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>)</em>) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</p></li>
+<li><p><strong>drop_original_signals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to drop the original unfiltered signals</p></li>
+<li><p><strong>order_by_frequency_band</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True will return channels ordered by frequency bands, so if there
+are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned
+channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order
+will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].</p></li>
+<li><p><strong>mne_filter_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Keyword arguments for filtering supported by mne.io.Raw.filter().
+Please refer to mne for a detailed explanation.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.preprocess.preprocess">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.preprocess.</span></span><span class="sig-name descname"><span class="pre">preprocess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L88-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess.preprocess" title="Link to this definition">#</a></dt>
+<dd><p>Apply preprocessors to a concat dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of BaseDataset or WindowsDataset datasets to be preprocessed.</p></li>
+<li><p><strong>preprocessors</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference internal" href="#braindecode.preprocessing.preprocess.Preprocessor" title="braindecode.preprocessing.preprocess.Preprocessor"><em>Preprocessor</em></a><em>)</em>) – List of Preprocessor objects to apply to the dataset.</p></li>
+<li><p><strong>save_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – If a string, the preprocessed data will be saved under the specified
+directory and the datasets in <code class="docutils literal notranslate"><span class="pre">concat_ds</span></code> will be reloaded with
+<cite>preload=False</cite>.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <cite>save_dir</cite> is provided, controls whether to delete the old
+subdirectories that will be written to under <cite>save_dir</cite>. If False and
+the corresponding subdirectories already exist, a <code class="docutils literal notranslate"><span class="pre">FileExistsError</span></code>
+will be raised.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Number of jobs for parallel execution. See <cite>joblib.Parallel</cite> for
+a more detailed explanation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Preprocessed dataset.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.preprocessing.windowers">
+<span id="braindecode-preprocessing-windowers-module"></span><h2>braindecode.preprocessing.windowers module<a class="headerlink" href="#module-braindecode.preprocessing.windowers" title="Link to this heading">#</a></h2>
+<p>Get epochs from mne.Raw</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.windowers.create_fixed_length_windows">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.windowers.</span></span><span class="sig-name descname"><span class="pre">create_fixed_length_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L146-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.windowers.create_fixed_length_windows" title="Link to this definition">#</a></dt>
+<dd><p>Windower that creates sliding windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<em>ConcatDataset</em>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from beginning of recording in samples.</p></li>
+<li><p><strong>stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stop offset from beginning of recording in samples. If None, set to be
+the end of the recording.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size in samples. If None, set to be the maximum possible window size, ie length of
+the recording, once offsets are accounted for.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
+windows will not overlap.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><em>None</em>) – Whether or not have a last overlapping window, when windows do not
+equally divide the continuous signal. Must be set to a bool if window size and stride are
+not None.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.windowers.create_windows_from_events">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.windowers.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L29-L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.windowers.create_windows_from_events" title="Link to this definition">#</a></dt>
+<dd><p>Create windows based on events in mne.Raw.</p>
+<p>This function extracts windows of size window_size_samples in the interval
+[trial onset + trial_start_offset_samples, trial onset + trial duration +
+trial_stop_offset_samples] around each trial, with a separation of
+window_stride_samples between consecutive windows. If the last window
+around an event does not end at trial_stop_offset_samples and
+drop_last_window is set to False, an additional overlapping window that
+ends at trial_stop_offset_samples is created.</p>
+<p>Windows are extracted from the interval defined by the following:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>                                        <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+                <span class="n">trial</span> <span class="n">onset</span>                <span class="n">duration</span>
+<span class="o">|--------------------|------------------------|-----------------------|</span>
+<span class="n">trial</span> <span class="n">onset</span> <span class="o">-</span>                                             <span class="n">trial</span> <span class="n">onset</span> <span class="o">+</span>
+<span class="n">trial_start_offset_samples</span>                                   <span class="n">duration</span> <span class="o">+</span>
+                                            <span class="n">trial_stop_offset_samples</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of base datasets each holding raw and description.</p></li>
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from original trial onsets, in samples. Defaults to zero.</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Stop offset from original trial stop, in samples. Defaults to zero.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size. If None, the window size is inferred from the original
+trial size of the first trial and trial_start_offset_samples and
+trial_stop_offset_samples.</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows, in samples. If None, the window stride is
+inferred from the original trial size of the first trial and
+trial_start_offset_samples and trial_stop_offset_samples.</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If False, an additional overlapping window that ends at
+trial_stop_offset_samples will be extracted around each event when the
+last window does not end exactly at trial_stop_offset_samples.</p></li>
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to numerical target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects. This is useful to
+reduce disk reading overhead when returning windows in a training
+scenario, however very large data might not fit into memory.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+step allows identifying e.g., windows that fall outside of the
+continuous recording. It is suggested to run this step here as otherwise
+the BaseConcatDataset has to be updated as well.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+mne.Epochs.</p></li>
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+rejection based on flatness is done. See mne.Epochs.</p></li>
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials with inconsistent length in a raw. If
+the number of trials whose length is exceeded by the window size is
+smaller than this, then only the corresponding trials are dropped, but
+the computation continues. Otherwise, an error is raised. Defaults to
+0.0 (raise an error).</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.preprocessing.windowers.create_windows_from_target_channels">
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.windowers.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_target_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L426-L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.windowers.create_windows_from_target_channels" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+
+
+  
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Crop"><code class="docutils literal notranslate"><span class="pre">Crop</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Crop.fn"><code class="docutils literal notranslate"><span class="pre">Crop.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.DropChannels"><code class="docutils literal notranslate"><span class="pre">DropChannels</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.DropChannels.fn"><code class="docutils literal notranslate"><span class="pre">DropChannels.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Filter"><code class="docutils literal notranslate"><span class="pre">Filter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Filter.fn"><code class="docutils literal notranslate"><span class="pre">Filter.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Pick"><code class="docutils literal notranslate"><span class="pre">Pick</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Pick.fn"><code class="docutils literal notranslate"><span class="pre">Pick.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Resample"><code class="docutils literal notranslate"><span class="pre">Resample</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.Resample.fn"><code class="docutils literal notranslate"><span class="pre">Resample.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.SetEEGReference"><code class="docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.SetEEGReference.fn"><code class="docutils literal notranslate"><span class="pre">SetEEGReference.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.create_fixed_length_windows"><code class="docutils literal notranslate"><span class="pre">create_fixed_length_windows()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.create_windows_from_events"><code class="docutils literal notranslate"><span class="pre">create_windows_from_events()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.create_windows_from_target_channels"><code class="docutils literal notranslate"><span class="pre">create_windows_from_target_channels()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.exponential_moving_standardize"><code class="docutils literal notranslate"><span class="pre">exponential_moving_standardize()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess"><code class="docutils literal notranslate"><span class="pre">preprocess()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.preprocessing.mne_preprocess">braindecode.preprocessing.mne_preprocess module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Crop"><code class="docutils literal notranslate"><span class="pre">Crop</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Crop.fn"><code class="docutils literal notranslate"><span class="pre">Crop.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.DropChannels"><code class="docutils literal notranslate"><span class="pre">DropChannels</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.DropChannels.fn"><code class="docutils literal notranslate"><span class="pre">DropChannels.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Filter"><code class="docutils literal notranslate"><span class="pre">Filter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Filter.fn"><code class="docutils literal notranslate"><span class="pre">Filter.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Pick"><code class="docutils literal notranslate"><span class="pre">Pick</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Pick.fn"><code class="docutils literal notranslate"><span class="pre">Pick.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Resample"><code class="docutils literal notranslate"><span class="pre">Resample</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.Resample.fn"><code class="docutils literal notranslate"><span class="pre">Resample.fn</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.SetEEGReference"><code class="docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.mne_preprocess.SetEEGReference.fn"><code class="docutils literal notranslate"><span class="pre">SetEEGReference.fn</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.preprocessing.preprocess">braindecode.preprocessing.preprocess module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.Preprocessor.apply"><code class="docutils literal notranslate"><span class="pre">Preprocessor.apply()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.exponential_moving_standardize"><code class="docutils literal notranslate"><span class="pre">exponential_moving_standardize()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.preprocess.preprocess"><code class="docutils literal notranslate"><span class="pre">preprocess()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.preprocessing.windowers">braindecode.preprocessing.windowers module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.windowers.create_fixed_length_windows"><code class="docutils literal notranslate"><span class="pre">create_fixed_length_windows()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.windowers.create_windows_from_events"><code class="docutils literal notranslate"><span class="pre">create_windows_from_events()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.preprocessing.windowers.create_windows_from_target_channels"><code class="docutils literal notranslate"><span class="pre">create_windows_from_target_channels()</span></code></a></li>
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+  
+  <section id="module-braindecode.samplers">
+<span id="braindecode-samplers-package"></span><h1>braindecode.samplers package<a class="headerlink" href="#module-braindecode.samplers" title="Link to this heading">#</a></h1>
+<p>Classes to sample examples.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">BalancedSequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sequences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L174-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Balanced sampling of sequences of consecutive windows with categorical
+targets.</p>
+<p>Balanced sampling of sequences inspired by the approach of <a class="reference internal" href="#r3d821f159c06-perslev2021" id="id1">[Perslev2021]</a>:
+1. Uniformly sample a recording out of the available ones.
+2. Uniformly sample one of the classes.
+3. Sample a window of the corresponding class in the selected recording.
+4. Extract a sequence of windows around the sampled window.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.
+Must contain a column <cite>target</cite> with categorical targets.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_sequences</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sequences to sample.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r3d821f159c06-perslev2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Perslev2021</a><span class="fn-bracket">]</span></span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,
+Igel C. U-Sleep: resilient high-frequency sleep staging. npj Digit.
+Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler.sample_class">
+<span class="sig-name descname"><span class="pre">sample_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L210-L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler.sample_class" title="Link to this definition">#</a></dt>
+<dd><p>Return a random class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>rec_ind</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Index to the recording to sample from. If None, the recording will
+be uniformly sampled across available recordings.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>int</em> – Sampled class.</p></li>
+<li><p><em>int</em> – Index to the recording the class was sampled from.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RecordingSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L15-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code></a></p>
+<p>Base sampler simplifying sampling from recordings.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – <p>DataFrame with at least one of {subject, session, run} columns for each
+window in the BaseConcatDataset to sample examples from. Normally
+obtained with <cite>BaseConcatDataset.get_metadata()</cite>. For instance,
+<cite>metadata.head()</cite> might look like this:</p>
+<blockquote>
+<div><p>i_window_in_trial  i_start_in_trial  i_stop_in_trial  target  subject    session    run</p>
+</div></blockquote>
+<p>0                  0                 0              500      -1        4  session_T  run_0
+1                  1               500             1000      -1        4  session_T  run_0
+2                  2              1000             1500      -1        4  session_T  run_0
+3                  3              1500             2000      -1        4  session_T  run_0
+4                  4              2000             2500      -1        4  session_T  run_0</p>
+</p></li>
+<li><p><strong>random_state</strong> (<em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>Series with MultiIndex index which contains the subject, session, run
+and window indices information in an easily accessible structure for
+quick sampling of windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.n_recordings">
+<span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.n_recordings" title="Link to this definition">#</a></dt>
+<dd><p>Number of recordings available.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#id0" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_recording">
+<span class="sig-name descname"><span class="pre">sample_recording</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L86-L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_recording" title="Link to this definition">#</a></dt>
+<dd><p>Return a random recording index.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_window">
+<span class="sig-name descname"><span class="pre">sample_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L92-L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_window" title="Link to this definition">#</a></dt>
+<dd><p>Return a specific window.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RelativePositioningSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">same_rec_neg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L14-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Sample examples for the relative positioning task from <a class="reference internal" href="#r0467437a2408-banville2020" id="id2">[Banville2020]</a>.</p>
+<p>Sample examples as tuples of two window indices, with a label indicating
+whether the windows are close or far, as defined by tau_pos and tau_neg.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>tau_pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the positive context, in samples. A positive pair contains two
+windows x1 and x2 which are separated by at most <cite>tau_pos</cite> samples.</p></li>
+<li><p><strong>tau_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the negative context, in samples. A negative pair contains two
+windows x1 and x2 which are separated by at least <cite>tau_neg</cite> samples and
+at most <cite>tau_max</cite> samples. Ignored if <cite>same_rec_neg</cite> is False.</p></li>
+<li><p><strong>n_examples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of pairs to extract.</p></li>
+<li><p><strong>tau_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – See <cite>tau_neg</cite>.</p></li>
+<li><p><strong>same_rec_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample negative pairs from within the same recording. If
+False, sample negative pairs from two different recordings.</p></li>
+<li><p><strong>random_state</strong> (<em>None</em><em> | </em><em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r0467437a2408-banville2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">Banville2020</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,
+&amp; Gramfort, A. (2020). Uncovering the structure of clinical EEG
+signals with self-supervised learning.
+arXiv preprint arXiv:2007.16104.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler.presample">
+<span class="sig-name descname"><span class="pre">presample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L94-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler.presample" title="Link to this definition">#</a></dt>
+<dd><p>Presample examples.</p>
+<p>Once presampled, the examples are the same from one epoch to another.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">SequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L109-L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.SequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Sample sequences of consecutive windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of windows between two consecutive sequences.</p></li>
+<li><p><strong>random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample sequences randomly. If False, sample sequences in
+order.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>See RecordingSampler.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.file_ids">
+<span class="sig-name descname"><span class="pre">file_ids</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.file_ids" title="Link to this definition">#</a></dt>
+<dd><p>Array of shape (n_sequences,) that indicates from which file each
+sequence comes from. Useful e.g. to do self-ensembling.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.samplers.base">
+<span id="braindecode-samplers-base-module"></span><h2>braindecode.samplers.base module<a class="headerlink" href="#module-braindecode.samplers.base" title="Link to this heading">#</a></h2>
+<p>Sampler classes.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.base.BalancedSequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.base.</span></span><span class="sig-name descname"><span class="pre">BalancedSequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sequences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L174-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.BalancedSequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Balanced sampling of sequences of consecutive windows with categorical
+targets.</p>
+<p>Balanced sampling of sequences inspired by the approach of <a class="reference internal" href="#rff6d9861384d-perslev2021" id="id3">[Perslev2021]</a>:
+1. Uniformly sample a recording out of the available ones.
+2. Uniformly sample one of the classes.
+3. Sample a window of the corresponding class in the selected recording.
+4. Extract a sequence of windows around the sampled window.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.
+Must contain a column <cite>target</cite> with categorical targets.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_sequences</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sequences to sample.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rff6d9861384d-perslev2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">Perslev2021</a><span class="fn-bracket">]</span></span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,
+Igel C. U-Sleep: resilient high-frequency sleep staging. npj Digit.
+Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.base.BalancedSequenceSampler.sample_class">
+<span class="sig-name descname"><span class="pre">sample_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L210-L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.BalancedSequenceSampler.sample_class" title="Link to this definition">#</a></dt>
+<dd><p>Return a random class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>rec_ind</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Index to the recording to sample from. If None, the recording will
+be uniformly sampled across available recordings.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>int</em> – Sampled class.</p></li>
+<li><p><em>int</em> – Index to the recording the class was sampled from.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.base.RecordingSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.base.</span></span><span class="sig-name descname"><span class="pre">RecordingSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L15-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.RecordingSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sampler</span></code></a></p>
+<p>Base sampler simplifying sampling from recordings.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – <p>DataFrame with at least one of {subject, session, run} columns for each
+window in the BaseConcatDataset to sample examples from. Normally
+obtained with <cite>BaseConcatDataset.get_metadata()</cite>. For instance,
+<cite>metadata.head()</cite> might look like this:</p>
+<blockquote>
+<div><p>i_window_in_trial  i_start_in_trial  i_stop_in_trial  target  subject    session    run</p>
+</div></blockquote>
+<p>0                  0                 0              500      -1        4  session_T  run_0
+1                  1               500             1000      -1        4  session_T  run_0
+2                  2              1000             1500      -1        4  session_T  run_0
+3                  3              1500             2000      -1        4  session_T  run_0
+4                  4              2000             2500      -1        4  session_T  run_0</p>
+</p></li>
+<li><p><strong>random_state</strong> (<em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.base.RecordingSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.base.RecordingSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>Series with MultiIndex index which contains the subject, session, run
+and window indices information in an easily accessible structure for
+quick sampling of windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.base.RecordingSampler.n_recordings">
+<span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#braindecode.samplers.base.RecordingSampler.n_recordings" title="Link to this definition">#</a></dt>
+<dd><p>Number of recordings available.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="id4">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#id4" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.base.RecordingSampler.sample_recording">
+<span class="sig-name descname"><span class="pre">sample_recording</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L86-L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.RecordingSampler.sample_recording" title="Link to this definition">#</a></dt>
+<dd><p>Return a random recording index.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.base.RecordingSampler.sample_window">
+<span class="sig-name descname"><span class="pre">sample_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L92-L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.RecordingSampler.sample_window" title="Link to this definition">#</a></dt>
+<dd><p>Return a specific window.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.base.SequenceSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.base.</span></span><span class="sig-name descname"><span class="pre">SequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L109-L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.base.SequenceSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Sample sequences of consecutive windows.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of windows between two consecutive sequences.</p></li>
+<li><p><strong>random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample sequences randomly. If False, sample sequences in
+order.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.base.SequenceSampler.info">
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.base.SequenceSampler.info" title="Link to this definition">#</a></dt>
+<dd><p>See RecordingSampler.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.samplers.base.SequenceSampler.file_ids">
+<span class="sig-name descname"><span class="pre">file_ids</span></span><a class="headerlink" href="#braindecode.samplers.base.SequenceSampler.file_ids" title="Link to this definition">#</a></dt>
+<dd><p>Array of shape (n_sequences,) that indicates from which file each
+sequence comes from. Useful e.g. to do self-ensembling.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray of ints</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.samplers.ssl">
+<span id="braindecode-samplers-ssl-module"></span><h2>braindecode.samplers.ssl module<a class="headerlink" href="#module-braindecode.samplers.ssl" title="Link to this heading">#</a></h2>
+<p>Self-supervised learning samplers.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.samplers.ssl.RelativePositioningSampler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.ssl.</span></span><span class="sig-name descname"><span class="pre">RelativePositioningSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">same_rec_neg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L14-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.ssl.RelativePositioningSampler" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.samplers.base.RecordingSampler" title="braindecode.samplers.base.RecordingSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></p>
+<p>Sample examples for the relative positioning task from <a class="reference internal" href="#r4e250e680b51-banville2020" id="id5">[Banville2020]</a>.</p>
+<p>Sample examples as tuples of two window indices, with a label indicating
+whether the windows are close or far, as defined by tau_pos and tau_neg.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
+<li><p><strong>tau_pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the positive context, in samples. A positive pair contains two
+windows x1 and x2 which are separated by at most <cite>tau_pos</cite> samples.</p></li>
+<li><p><strong>tau_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the negative context, in samples. A negative pair contains two
+windows x1 and x2 which are separated by at least <cite>tau_neg</cite> samples and
+at most <cite>tau_max</cite> samples. Ignored if <cite>same_rec_neg</cite> is False.</p></li>
+<li><p><strong>n_examples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of pairs to extract.</p></li>
+<li><p><strong>tau_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – See <cite>tau_neg</cite>.</p></li>
+<li><p><strong>same_rec_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample negative pairs from within the same recording. If
+False, sample negative pairs from two different recordings.</p></li>
+<li><p><strong>random_state</strong> (<em>None</em><em> | </em><em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random state.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4e250e680b51-banville2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">Banville2020</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,
+&amp; Gramfort, A. (2020). Uncovering the structure of clinical EEG
+signals with self-supervised learning.
+arXiv preprint arXiv:2007.16104.</p>
+</div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.samplers.ssl.RelativePositioningSampler.presample">
+<span class="sig-name descname"><span class="pre">presample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L94-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.ssl.RelativePositioningSampler.presample" title="Link to this definition">#</a></dt>
+<dd><p>Presample examples.</p>
+<p>Once presampled, the examples are the same from one epoch to another.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
+
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.BalancedSequenceSampler.sample_class"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler.sample_class()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id0"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_window"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_window()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RelativePositioningSampler.presample"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler.presample()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.SequenceSampler"><code class="docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.SequenceSampler.info"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.info</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.SequenceSampler.file_ids"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.file_ids</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.samplers.base">braindecode.samplers.base module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.BalancedSequenceSampler.sample_class"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler.sample_class()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id4"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.RecordingSampler.sample_window"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_window()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.SequenceSampler"><code class="docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.SequenceSampler.info"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.info</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.base.SequenceSampler.file_ids"><code class="docutils literal notranslate"><span class="pre">SequenceSampler.file_ids</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.samplers.ssl">braindecode.samplers.ssl module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.ssl.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.ssl.RelativePositioningSampler.presample"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler.presample()</span></code></a></li>
+</ul>
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+  <section id="module-braindecode.training">
+<span id="braindecode-training-package"></span><h1>braindecode.training package<a class="headerlink" href="#module-braindecode.training" title="Link to this heading">#</a></h1>
+<p>Functionality for skorch-based training.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L11-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute Loss after averaging predictions across time.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L20-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.training.CroppedLoss.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.training.CroppedLoss.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTimeSeriesEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L229-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.training.scoring.CroppedTrialEpochScoring" title="braindecode.training.scoring.CroppedTrialEpochScoring"><code class="xref py py-class docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a></p>
+<p>Class to compute scores for trials from a model that predicts (super)crops with
+time series target.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L234-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTrialEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L116-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code></p>
+<p>Class to compute scores for trials from a model that predicts (super)crops.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_batch_end">
+<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L150-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each batch.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L160-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">PostEpochTrainScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L312-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code></p>
+<p>Epoch Scoring class that recomputes predictions after the epoch
+on the training in validation mode.</p>
+<p>Note: For unknown reasons, this affects global random generator and
+therefore all results may change slightly if you add this scoring callback.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>scoring</strong> (<em>None</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, or </em><em>callable</em><em> (</em><em>default=None</em><em>)</em>) – If None, use the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of the model. If str, it
+should be a valid sklearn scorer (e.g. “f1”, “accuracy”). If a
+callable, it should have the signature (model, X, y), and it
+should return a scalar. This works analogously to the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter in sklearn’s <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> et al.</p></li>
+<li><p><strong>lower_is_better</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether lower scores should be considered better or worse.</p></li>
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – If not an explicit string, tries to infer the name from the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> argument.</p></li>
+<li><p><strong>target_extractor</strong> (<em>callable</em><em> (</em><em>default=to_numpy</em><em>)</em>) – This is called on y before it is passed to scoring.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L353-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L35-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute Loss between timeseries targets and predictions.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)
+Assumes targets are in shape:
+n_batch size x n_classes x window_len (in time)
+If the targets contain NaNs, the NaNs will be masked out and the loss will be only computed for
+predictions valid corresponding to valid target values.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L48-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.training.TimeSeriesLoss.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.mixup_criterion">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">mixup_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L69-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.mixup_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Implements loss for Mixup for EEG data. See <a class="reference internal" href="#r54dff61a9a59-1" id="id1">[1]</a>.</p>
+<p>Implementation based on <a class="reference internal" href="#r54dff61a9a59-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Predictions from the model.</p></li>
+<li><p><strong>target</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – For predictions without mixup, the targets as a tensor. If mixup has
+been applied, a list containing the targets of the two mixed
+samples and the mixing coefficients as tensors.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r54dff61a9a59-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+mixup: Beyond Empirical Risk Minimization
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="r54dff61a9a59-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.predict_trials">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L403-L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset given module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – A pytorch model implementing forward.</p></li>
+<li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The batch size used to iterate the dataset.</p></li>
+<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of workers used in DataLoader to iterate the dataset.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.trial_preds_from_window_preds">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">trial_preds_from_window_preds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_window_in_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_stop_in_trials</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L21-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.trial_preds_from_window_preds" title="Link to this definition">#</a></dt>
+<dd><p>Assigning window predictions to trials  while removing duplicate
+predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>ndarrays</em><em> (</em><em>at least 2darrays</em><em>)</em>) – <dl class="simple">
+<dt>List of window predictions, in each window prediction</dt><dd><p>time is in axis=1</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>i_window_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Index/number of window in trial</p></li>
+<li><p><strong>i_stop_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – stop position of window in trial</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>preds_per_trial</strong> – Predictions in each trial, duplicates removed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of ndarrays</p>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.training.callbacks">
+<span id="braindecode-training-callbacks-module"></span><h2>braindecode.training.callbacks module<a class="headerlink" href="#module-braindecode.training.callbacks" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.callbacks.MaxNormConstraintCallback">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.callbacks.</span></span><span class="sig-name descname"><span class="pre">MaxNormConstraintCallback</span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/callbacks.py#L9-L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.callbacks.MaxNormConstraintCallback" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.callbacks.MaxNormConstraintCallback.on_batch_end">
+<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/callbacks.py#L10-L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.callbacks.MaxNormConstraintCallback.on_batch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each batch.</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-braindecode.training.losses">
+<span id="braindecode-training-losses-module"></span><h2>braindecode.training.losses module<a class="headerlink" href="#module-braindecode.training.losses" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.losses.CroppedLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.losses.</span></span><span class="sig-name descname"><span class="pre">CroppedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L11-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.losses.CroppedLoss" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute Loss after averaging predictions across time.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.losses.CroppedLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L20-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.losses.CroppedLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.training.losses.CroppedLoss.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.training.losses.CroppedLoss.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.losses.TimeSeriesLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.losses.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L35-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.losses.TimeSeriesLoss" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a></p>
+<p>Compute Loss between timeseries targets and predictions.
+Assumes predictions are in shape:
+n_batch size x n_classes x n_predictions (in time)
+Assumes targets are in shape:
+n_batch size x n_classes x window_len (in time)
+If the targets contain NaNs, the NaNs will be masked out and the loss will be only computed for
+predictions valid corresponding to valid target values.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.losses.TimeSeriesLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L48-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.losses.TimeSeriesLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="braindecode.training.losses.TimeSeriesLoss.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></em><a class="headerlink" href="#braindecode.training.losses.TimeSeriesLoss.training" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.losses.mixup_criterion">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.losses.</span></span><span class="sig-name descname"><span class="pre">mixup_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L69-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.losses.mixup_criterion" title="Link to this definition">#</a></dt>
+<dd><p>Implements loss for Mixup for EEG data. See <a class="reference internal" href="#ra734c2cd66a5-1" id="id3">[1]</a>.</p>
+<p>Implementation based on <a class="reference internal" href="#ra734c2cd66a5-2" id="id4">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Predictions from the model.</p></li>
+<li><p><strong>target</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – For predictions without mixup, the targets as a tensor. If mixup has
+been applied, a list containing the targets of the two mixed
+samples and the mixing coefficients as tensors.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="ra734c2cd66a5-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+mixup: Beyond Empirical Risk Minimization
+Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
+</div>
+<div class="citation" id="ra734c2cd66a5-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.training.scoring">
+<span id="braindecode-training-scoring-module"></span><h2>braindecode.training.scoring module<a class="headerlink" href="#module-braindecode.training.scoring" title="Link to this heading">#</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.scoring.CroppedTimeSeriesEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.scoring.</span></span><span class="sig-name descname"><span class="pre">CroppedTimeSeriesEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L229-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.CroppedTimeSeriesEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#braindecode.training.scoring.CroppedTrialEpochScoring" title="braindecode.training.scoring.CroppedTrialEpochScoring"><code class="xref py py-class docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a></p>
+<p>Class to compute scores for trials from a model that predicts (super)crops with
+time series target.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.scoring.CroppedTimeSeriesEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L234-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.CroppedTimeSeriesEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.scoring.CroppedTrialEpochScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.scoring.</span></span><span class="sig-name descname"><span class="pre">CroppedTrialEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L116-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.CroppedTrialEpochScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code></p>
+<p>Class to compute scores for trials from a model that predicts (super)crops.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.scoring.CroppedTrialEpochScoring.on_batch_end">
+<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L150-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.CroppedTrialEpochScoring.on_batch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each batch.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.scoring.CroppedTrialEpochScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L160-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.CroppedTrialEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.training.scoring.PostEpochTrainScoring">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.scoring.</span></span><span class="sig-name descname"><span class="pre">PostEpochTrainScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L312-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.PostEpochTrainScoring" title="Link to this definition">#</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code></p>
+<p>Epoch Scoring class that recomputes predictions after the epoch
+on the training in validation mode.</p>
+<p>Note: For unknown reasons, this affects global random generator and
+therefore all results may change slightly if you add this scoring callback.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>scoring</strong> (<em>None</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, or </em><em>callable</em><em> (</em><em>default=None</em><em>)</em>) – If None, use the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of the model. If str, it
+should be a valid sklearn scorer (e.g. “f1”, “accuracy”). If a
+callable, it should have the signature (model, X, y), and it
+should return a scalar. This works analogously to the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter in sklearn’s <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> et al.</p></li>
+<li><p><strong>lower_is_better</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether lower scores should be considered better or worse.</p></li>
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – If not an explicit string, tries to infer the name from the
+<code class="docutils literal notranslate"><span class="pre">scoring</span></code> argument.</p></li>
+<li><p><strong>target_extractor</strong> (<em>callable</em><em> (</em><em>default=to_numpy</em><em>)</em>) – This is called on y before it is passed to scoring.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.training.scoring.PostEpochTrainScoring.on_epoch_end">
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L353-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.PostEpochTrainScoring.on_epoch_end" title="Link to this definition">#</a></dt>
+<dd><p>Called at the end of each epoch.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.scoring.predict_trials">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.scoring.</span></span><span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L403-L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.predict_trials" title="Link to this definition">#</a></dt>
+<dd><p>Create trialwise predictions and optionally also return trialwise
+labels from cropped dataset given module.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – A pytorch model implementing forward.</p></li>
+<li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The batch size used to iterate the dataset.</p></li>
+<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of workers used in DataLoader to iterate the dataset.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
+the number of predictions depend on the chosen window size and the
+receptive field of the network.</p></li>
+<li><p><strong>trial_labels</strong> (<em>np.ndarray</em>) – 2-dimensional array (n_trials x n_targets) where the number of
+targets depends on the decoding paradigm and can be either a single
+value, multiple values, or a sequence.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.training.scoring.trial_preds_from_window_preds">
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.scoring.</span></span><span class="sig-name descname"><span class="pre">trial_preds_from_window_preds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_window_in_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_stop_in_trials</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L21-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.scoring.trial_preds_from_window_preds" title="Link to this definition">#</a></dt>
+<dd><p>Assigning window predictions to trials  while removing duplicate
+predictions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>ndarrays</em><em> (</em><em>at least 2darrays</em><em>)</em>) – <dl class="simple">
+<dt>List of window predictions, in each window prediction</dt><dd><p>time is in axis=1</p>
+</dd>
+</dl>
+</p></li>
+<li><p><strong>i_window_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Index/number of window in trial</p></li>
+<li><p><strong>i_stop_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – stop position of window in trial</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>preds_per_trial</strong> – Predictions in each trial, duplicates removed</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of ndarrays</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
+  
+</div>
+
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+    <i class="fa-solid fa-list"></i> On this page
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedLoss"><code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedLoss.forward"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedLoss.training"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.TimeSeriesLoss"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.TimeSeriesLoss.forward"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.TimeSeriesLoss.training"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.mixup_criterion"><code class="docutils literal notranslate"><span class="pre">mixup_criterion()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.predict_trials"><code class="docutils literal notranslate"><span class="pre">predict_trials()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.trial_preds_from_window_preds"><code class="docutils literal notranslate"><span class="pre">trial_preds_from_window_preds()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.training.callbacks">braindecode.training.callbacks module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.callbacks.MaxNormConstraintCallback"><code class="docutils literal notranslate"><span class="pre">MaxNormConstraintCallback</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.callbacks.MaxNormConstraintCallback.on_batch_end"><code class="docutils literal notranslate"><span class="pre">MaxNormConstraintCallback.on_batch_end()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.training.losses">braindecode.training.losses module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.CroppedLoss"><code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.CroppedLoss.forward"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.CroppedLoss.training"><code class="docutils literal notranslate"><span class="pre">CroppedLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.TimeSeriesLoss"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.TimeSeriesLoss.forward"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.forward()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.TimeSeriesLoss.training"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss.training</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.losses.mixup_criterion"><code class="docutils literal notranslate"><span class="pre">mixup_criterion()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.training.scoring">braindecode.training.scoring module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.PostEpochTrainScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring.on_epoch_end()</span></code></a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.predict_trials"><code class="docutils literal notranslate"><span class="pre">predict_trials()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.scoring.trial_preds_from_window_preds"><code class="docutils literal notranslate"><span class="pre">trial_preds_from_window_preds()</span></code></a></li>
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+  <section id="module-braindecode.visualization">
+<span id="braindecode-visualization-package"></span><h1>braindecode.visualization package<a class="headerlink" href="#module-braindecode.visualization" title="Link to this heading">#</a></h1>
+<p>Functions for visualisations, especially of the ConvNets.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.compute_amplitude_gradients">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">compute_amplitude_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/gradients.py#L10-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.compute_amplitude_gradients" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.plot_confusion_matrix">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">plot_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colormap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textcolor='black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontweight='normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_row_labels=90</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_col_labels=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_f1_score=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_axes=(0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_precision=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names_fontsize=12</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/confusion_matrices.py#L10-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.plot_confusion_matrix" title="Link to this definition">#</a></dt>
+<dd><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="#r8046536b33dd-1" id="id1">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>confusion_mat</strong> (<em>2d numpy array</em>) – A confusion matrix, e.g. sklearn confusion matrix:
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html">https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html</a></p></li>
+<li><p><strong>class_names</strong> (<em>array</em><em>, </em><em>optional</em>) – List of classes/targets.</p></li>
+<li><p><strong>figsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size of the generated confusion matrix figure.</p></li>
+<li><p><strong>colormap</strong> (<em>matplotlib cm colormap</em><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>textcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Color  of the text in the figure.</p></li>
+<li><p><strong>vmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>vmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>fontweight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Weight of the font in the figure:
+[ ‘normal’ | ‘bold’ | ‘heavy’ | ‘light’ | ‘ultrabold’ | ‘ultralight’]</p></li>
+<li><p><strong>rotate_row_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the row labels</p></li>
+<li><p><strong>rotate_col_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the column labels</p></li>
+<li><p><strong>with_f1_score</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>norm_axes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>rotate_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>class_names_fontsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib figure</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r8046536b33dd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017. Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading">#</a></h2>
+</section>
+<section id="module-braindecode.visualization.confusion_matrices">
+<span id="braindecode-visualization-confusion-matrices-module"></span><h2>braindecode.visualization.confusion_matrices module<a class="headerlink" href="#module-braindecode.visualization.confusion_matrices" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.confusion_matrices.plot_confusion_matrix">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.confusion_matrices.</span></span><span class="sig-name descname"><span class="pre">plot_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colormap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textcolor='black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontweight='normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_row_labels=90</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_col_labels=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_f1_score=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_axes=(0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_precision=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names_fontsize=12</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/confusion_matrices.py#L10-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.confusion_matrices.plot_confusion_matrix" title="Link to this definition">#</a></dt>
+<dd><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="#r4988b7a7f52e-1" id="id2">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>confusion_mat</strong> (<em>2d numpy array</em>) – A confusion matrix, e.g. sklearn confusion matrix:
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html">https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html</a></p></li>
+<li><p><strong>class_names</strong> (<em>array</em><em>, </em><em>optional</em>) – List of classes/targets.</p></li>
+<li><p><strong>figsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size of the generated confusion matrix figure.</p></li>
+<li><p><strong>colormap</strong> (<em>matplotlib cm colormap</em><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>textcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Color  of the text in the figure.</p></li>
+<li><p><strong>vmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>vmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>fontweight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Weight of the font in the figure:
+[ ‘normal’ | ‘bold’ | ‘heavy’ | ‘light’ | ‘ultrabold’ | ‘ultralight’]</p></li>
+<li><p><strong>rotate_row_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the row labels</p></li>
+<li><p><strong>rotate_col_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the column labels</p></li>
+<li><p><strong>with_f1_score</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>norm_axes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>rotate_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>class_names_fontsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>fig</strong></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>matplotlib figure</p>
+</dd>
+</dl>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r4988b7a7f52e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and
+visualization.
+Human Brain Mapping , Aug. 2017. Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
+</div>
+</div>
+</dd></dl>
+
+</section>
+<section id="module-braindecode.visualization.gradients">
+<span id="braindecode-visualization-gradients-module"></span><h2>braindecode.visualization.gradients module<a class="headerlink" href="#module-braindecode.visualization.gradients" title="Link to this heading">#</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.gradients.compute_amplitude_gradients">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.gradients.</span></span><span class="sig-name descname"><span class="pre">compute_amplitude_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/gradients.py#L10-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.gradients.compute_amplitude_gradients" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="braindecode.visualization.gradients.compute_amplitude_gradients_for_X">
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.gradients.</span></span><span class="sig-name descname"><span class="pre">compute_amplitude_gradients_for_X</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/gradients.py#L21-L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.gradients.compute_amplitude_gradients_for_X" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.visualization.plot_confusion_matrix"><code class="docutils literal notranslate"><span class="pre">plot_confusion_matrix()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.visualization.confusion_matrices">braindecode.visualization.confusion_matrices module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.visualization.confusion_matrices.plot_confusion_matrix"><code class="docutils literal notranslate"><span class="pre">plot_confusion_matrix()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-braindecode.visualization.gradients">braindecode.visualization.gradients module</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.visualization.gradients.compute_amplitude_gradients"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.visualization.gradients.compute_amplitude_gradients_for_X"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients_for_X()</span></code></a></li>
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+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
+  <label class="sidebar-toggle primary-toggle" for="__primary">
+    <span class="fa-solid fa-bars"></span>
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+  <div class="navbar-header-items__start">
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+      <div class="navbar-item">
+  
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+<a class="navbar-brand logo" href="../index.html">
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+    <img src="../_static/braindecode_symbol.png" class="logo__image only-light" alt="Braindecode Logo"/>
+    <script>document.write(`<img src="../_static/braindecode_symbol.png" class="logo__image only-dark" alt="Braindecode Logo"/>`);</script>
+  
+  
+</a></div>
+    
+  </div>
+  
+  
+  <div class="col-lg-9 navbar-header-items">
+    
+    <div class="me-auto navbar-header-items__center">
+      
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
+                        Cite
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
+                        Tutorial and Examples
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../api.html">
+                        API Reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
+                        Get help
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
+                        What’s new
+                      </a>
+                    </li>
+                
+  </ul>
+</nav></div>
+      
+    </div>
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+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
+        </div>
+      
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span>
+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-bs-toggle="dropdown">
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+      <span class="caret"></span>
+    </button>
+    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
+    <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+`);
+</script></div>
+      
+    </div>
+    
+  </div>
+  
+  
+    <div class="navbar-persistent--mobile">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+  </button>
+`);
+</script>
+    </div>
+  
+
+  
+</div>
+
+    </nav>
+  
+  <div class="bd-container">
+    <div class="bd-container__inner bd-page-width">
+      
+      <div class="bd-sidebar-primary bd-sidebar hide-on-wide">
+        
+
+  
+  <div class="sidebar-header-items sidebar-primary__section">
+    
+    
+      <div class="sidebar-header-items__center">
+        
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
+                        Install
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
+                        Cite
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
+                        Tutorial and Examples
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../api.html">
+                        API Reference
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
+                        Get help
+                      </a>
+                    </li>
+                
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
+                        What’s new
+                      </a>
+                    </li>
+                
+  </ul>
+</nav></div>
+        
+      </div>
+    
+    
+    
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></span>
+    <span class="theme-switch" data-mode="dark"><i class="fa-solid fa-moon"></i></span>
+    <span class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></span>
+  </button>
+`);
+</script></div>
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-bs-toggle="dropdown">
+      0.8  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
+    </button>
+    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
+    <!-- dropdown will be populated by javascript on page load -->
+    </div>
+  </div>
+`);
+</script></div>
+        
+      </div>
+    
+  </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
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+        <div class="header-article-item">
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+
+
+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
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+    </li>
+    <li class="breadcrumb-item active" aria-current="page">braindecode</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="braindecode">
+<h1>braindecode<a class="headerlink" href="#braindecode" title="Link to this heading">#</a></h1>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.html">braindecode package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.doc"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.doc</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.doc"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.doc</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#subpackages">Subpackages</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.augmentation.html">braindecode.augmentation package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.AugmentedDataLoader"><code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.FrequencyShift"><code class="docutils literal notranslate"><span class="pre">FrequencyShift</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsXRotation"><code class="docutils literal notranslate"><span class="pre">SensorsXRotation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsYRotation"><code class="docutils literal notranslate"><span class="pre">SensorsYRotation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SensorsZRotation"><code class="docutils literal notranslate"><span class="pre">SensorsZRotation</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SignFlip"><code class="docutils literal notranslate"><span class="pre">SignFlip</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#braindecode.augmentation.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.base">braindecode.augmentation.base module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.functional">braindecode.augmentation.functional module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.augmentation.html#module-braindecode.augmentation.transforms">braindecode.augmentation.transforms module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datasets.html">braindecode.datasets package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BNCI2014001"><code class="docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.HGD"><code class="docutils literal notranslate"><span class="pre">HGD</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.MOABBDataset"><code class="docutils literal notranslate"><span class="pre">MOABBDataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.SleepPhysionet"><code class="docutils literal notranslate"><span class="pre">SleepPhysionet</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUH"><code class="docutils literal notranslate"><span class="pre">TUH</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_X_y"><code class="docutils literal notranslate"><span class="pre">create_from_X_y()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#braindecode.datasets.create_from_mne_raw"><code class="docutils literal notranslate"><span class="pre">create_from_mne_raw()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.base">braindecode.datasets.base module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.bbci">braindecode.datasets.bbci module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.bcicomp">braindecode.datasets.bcicomp module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.mne">braindecode.datasets.mne module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.moabb">braindecode.datasets.moabb module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.sleep_physionet">braindecode.datasets.sleep_physionet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.tuh">braindecode.datasets.tuh module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datasets.html#module-braindecode.datasets.xy">braindecode.datasets.xy module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.datautil.html">braindecode.datautil package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.mne">braindecode.datautil.mne module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.preprocess">braindecode.datautil.preprocess module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.serialization">braindecode.datautil.serialization module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.util">braindecode.datautil.util module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.windowers">braindecode.datautil.windowers module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.datautil.html#module-braindecode.datautil.xy">braindecode.datautil.xy module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.models.html">braindecode.models package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.atcnet">braindecode.models.atcnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.base">braindecode.models.base module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.deep4">braindecode.models.deep4 module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.deepsleepnet">braindecode.models.deepsleepnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegconformer">braindecode.models.eegconformer module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception">braindecode.models.eeginception module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception_erp">braindecode.models.eeginception_erp module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eeginception_mi">braindecode.models.eeginception_mi module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegitnet">braindecode.models.eegitnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegnet">braindecode.models.eegnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.eegresnet">braindecode.models.eegresnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.functions">braindecode.models.functions module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.hybrid">braindecode.models.hybrid module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.modules">braindecode.models.modules module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.shallow_fbcsp">braindecode.models.shallow_fbcsp module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_blanco_2020">braindecode.models.sleep_stager_blanco_2020 module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_chambon_2018">braindecode.models.sleep_stager_chambon_2018 module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.sleep_stager_eldele_2021">braindecode.models.sleep_stager_eldele_2021 module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.tcn">braindecode.models.tcn module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.tidnet">braindecode.models.tidnet module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.usleep">braindecode.models.usleep module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.models.html#module-braindecode.models.util">braindecode.models.util module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.preprocessing.html">braindecode.preprocessing package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Crop"><code class="docutils literal notranslate"><span class="pre">Crop</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.DropChannels"><code class="docutils literal notranslate"><span class="pre">DropChannels</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Filter"><code class="docutils literal notranslate"><span class="pre">Filter</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Pick"><code class="docutils literal notranslate"><span class="pre">Pick</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Preprocessor"><code class="docutils literal notranslate"><span class="pre">Preprocessor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.Resample"><code class="docutils literal notranslate"><span class="pre">Resample</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference"><code class="docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows"><code class="docutils literal notranslate"><span class="pre">create_fixed_length_windows()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events"><code class="docutils literal notranslate"><span class="pre">create_windows_from_events()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_target_channels"><code class="docutils literal notranslate"><span class="pre">create_windows_from_target_channels()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_demean"><code class="docutils literal notranslate"><span class="pre">exponential_moving_demean()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.exponential_moving_standardize"><code class="docutils literal notranslate"><span class="pre">exponential_moving_standardize()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.filterbank"><code class="docutils literal notranslate"><span class="pre">filterbank()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#braindecode.preprocessing.preprocess"><code class="docutils literal notranslate"><span class="pre">preprocess()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.mne_preprocess">braindecode.preprocessing.mne_preprocess module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess">braindecode.preprocessing.preprocess module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.preprocessing.html#module-braindecode.preprocessing.windowers">braindecode.preprocessing.windowers module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.samplers.html">braindecode.samplers package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.RelativePositioningSampler"><code class="docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#braindecode.samplers.SequenceSampler"><code class="docutils literal notranslate"><span class="pre">SequenceSampler</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#module-braindecode.samplers.base">braindecode.samplers.base module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.samplers.html#module-braindecode.samplers.ssl">braindecode.samplers.ssl module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.training.html">braindecode.training package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedLoss"><code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTimeSeriesEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTimeSeriesEpochScoring</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.TimeSeriesLoss"><code class="docutils literal notranslate"><span class="pre">TimeSeriesLoss</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.mixup_criterion"><code class="docutils literal notranslate"><span class="pre">mixup_criterion()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.predict_trials"><code class="docutils literal notranslate"><span class="pre">predict_trials()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#braindecode.training.trial_preds_from_window_preds"><code class="docutils literal notranslate"><span class="pre">trial_preds_from_window_preds()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.callbacks">braindecode.training.callbacks module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.losses">braindecode.training.losses module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.training.html#module-braindecode.training.scoring">braindecode.training.scoring module</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.visualization.html">braindecode.visualization package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.compute_amplitude_gradients"><code class="docutils literal notranslate"><span class="pre">compute_amplitude_gradients()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.visualization.html#braindecode.visualization.plot_confusion_matrix"><code class="docutils literal notranslate"><span class="pre">plot_confusion_matrix()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.visualization.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.visualization.html#module-braindecode.visualization.confusion_matrices">braindecode.visualization.confusion_matrices module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.visualization.html#module-braindecode.visualization.gradients">braindecode.visualization.gradients module</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#module-braindecode.classifier">braindecode.classifier module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.doc"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.doc</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.classifier.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#module-braindecode.eegneuralnet">braindecode.eegneuralnet module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#module-braindecode.regressor">braindecode.regressor module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.doc"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.doc</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="braindecode.html#braindecode.regressor.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#module-braindecode.util">braindecode.util module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.ThrowAwayIndexLoader"><code class="docutils literal notranslate"><span class="pre">ThrowAwayIndexLoader</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.corr"><code class="docutils literal notranslate"><span class="pre">corr()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.cov"><code class="docutils literal notranslate"><span class="pre">cov()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.create_mne_dummy_raw"><code class="docutils literal notranslate"><span class="pre">create_mne_dummy_raw()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.get_balanced_batches"><code class="docutils literal notranslate"><span class="pre">get_balanced_batches()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.np_to_th"><code class="docutils literal notranslate"><span class="pre">np_to_th()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.np_to_var"><code class="docutils literal notranslate"><span class="pre">np_to_var()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.read_all_file_names"><code class="docutils literal notranslate"><span class="pre">read_all_file_names()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.set_random_seeds"><code class="docutils literal notranslate"><span class="pre">set_random_seeds()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.th_to_np"><code class="docutils literal notranslate"><span class="pre">th_to_np()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.update_estimator_docstring"><code class="docutils literal notranslate"><span class="pre">update_estimator_docstring()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.var_to_np"><code class="docutils literal notranslate"><span class="pre">var_to_np()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="braindecode.html#braindecode.util.wrap_reshape_apply_fn"><code class="docutils literal notranslate"><span class="pre">wrap_reshape_apply_fn()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="braindecode.html#module-braindecode.version">braindecode.version module</a></li>
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diff --git a/0.8/whats_new.html b/0.8/whats_new.html
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,749 @@
+
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+  <script data-cfasync="false">
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+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
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+
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+<nav aria-label="Breadcrumbs">
+  <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb">
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+      <a href="index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
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+  </ul>
+</nav>
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+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="what-s-new">
+<span id="whats-new"></span><h1>What’s new<a class="headerlink" href="#what-s-new" title="Link to this heading">#</a></h1>
+<p>Please follow the examples below like <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a>, so the
+whats_new page will have a link to the function/class documentation.</p>
+<ul class="simple">
+<li><p>“Enhancements” for new features</p></li>
+<li><p>“Bugs” for bug fixes</p></li>
+<li><p>“API changes” for backward-incompatible changes</p></li>
+</ul>
+<section id="current-0-9-dev0">
+<span id="current"></span><h2>Current 0.9 (dev0)<a class="headerlink" href="#current-0-9-dev0" title="Link to this heading">#</a></h2>
+<section id="enhancements">
+<h3>Enhancements<a class="headerlink" href="#enhancements" title="Link to this heading">#</a></h3>
+</section>
+<section id="bugs">
+<h3>Bugs<a class="headerlink" href="#bugs" title="Link to this heading">#</a></h3>
+</section>
+<section id="api-changes">
+<h3>API changes<a class="headerlink" href="#api-changes" title="Link to this heading">#</a></h3>
+</section>
+</section>
+<section id="current-0-8-11-2022">
+<span id="changes-0-8-0"></span><h2>Current 0.8 (11-2022)<a class="headerlink" href="#current-0-8-11-2022" title="Link to this heading">#</a></h2>
+<section id="id1">
+<h3>Enhancements<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI" title="braindecode.models.EEGInceptionMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInceptionMI</span></code></a> network for motor imagery (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/428">#428</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet" title="braindecode.models.ATCNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.ATCNet</span></code></a> network for motor imagery (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/429">#429</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding to <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUH" title="braindecode.datasets.tuh.TUH"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.tuh.TUH</span></code></a> compatibility with version 3.0 of TUH dataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/431">#431</a> by <a class="reference external" href="https://github.com/MohammadJavadD">Mohammad Javad D</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>, <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>, <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>, <a class="reference external" href="https://github.com/dengemann">Denis A. Engemann</a> and <a class="reference external" href="https://github.com/ostormer">Oskar Størmer</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet" title="braindecode.models.DeepSleepNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.DeepSleepNet</span></code></a> network for sleep staging (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/417">#417</a> by <a class="reference external" href="https://github.com/Tgnassou">Theo Gnassounou</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer" title="braindecode.models.EEGConformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGConformer</span></code></a> network (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/454">#454</a> by <a class="reference external" href="https://github.com/eeyhsong">Yonghao Song</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding einops in the requirements (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/466">#466</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Have moabb as an extra dependency (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/467">#467</a> by <a class="reference external" href="https://github.com/ZamboniMarco99">Marco Zamboni</a>)</p></li>
+<li><p>Replacing the replacing Pytorch layers to Rearrange from einops #468  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/468">#468</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Refactoring the documentation and creating a sub-structure for the examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/470">#470</a> by <a class="reference external" href="https://github.com/dengemann">Denis A. Engemann</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Solving issues with slow conda and splitting the doc and test .yml to speed the CI. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/479">#479</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving the GitHub Actions CI and solving the skorch compatibility in the examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/472">#472</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Changing the documentation order (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/489">#489</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improve the documentation for the Temple University Hospital (TUH) EEG Corpus with discrete targets (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/485">#485</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving documentation for MOABB dataset, Trialwise Decoding &amp; Cropped Decoding (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/490">#490</a> by <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>)</p></li>
+<li><p>Improving the documentation for the sleep stage examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/487">#487</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving the tutorial Hyperparameter tuning with scikit-learn (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/473">#473</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add <a class="reference internal" href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> base class for all braindecode models (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Normalize all models common parameters and leaving the old ones as deprecated (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Improving the tutorial with a Data Augmentation Search (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/495">#495</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Improving documentation for “Split Dataset” and “Process a big data EEG resource” examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/494">#494</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a>)</p></li>
+<li><p>Improving documentation for the Convolutional neural network regression model on fake data (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/491">#491</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Enforcing the eval mode in the function predict trial. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/497">#497</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding extra requirements for pip install, update doc, removing conda env file (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/505">#505</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add models user-friendly representation with torchinfo tables to <a class="reference internal" href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Merged temporal and spatial convolutions for Deep4 and ShallowFBCSP (by <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a> and <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Enabling data augmentation of single inputs (with no batch dimension). (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/503">#503</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <cite>randomize</cite> parameter to <a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/504">#504</a> by <a href="#id17"><span class="problematic" id="id18">`Théo Gnassounou`_</span></a>.)</p></li>
+<li><p>Creating new preprocessor objects based on mne’s raw/Epochs methods <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Resample</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.DropChannels</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.SetEEGReference</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Filter</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Pick</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Crop</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/500">#500</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Moving <a href="#id2"><span class="problematic" id="id3">:function:`braindecode.models.util.get_output_shape`</span></a> and <a href="#id4"><span class="problematic" id="id5">:function:`braindecode.models.util.to_dense_prediction_model`</span></a> to <a class="reference internal" href="source/braindecode.models.html#braindecode.models.base.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/514">#514</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Automatically populate signal-related parameters in <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> and <a class="reference internal" href="source/braindecode.html#braindecode.EEGRegressor" title="braindecode.EEGRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/517">#517</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Adding a pure PyTorch tutorial (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/523">#523</a> by <a class="reference external" href="https://github.com/remidbs">Remi Delbouys</a>  and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">models_dict</span></code> to <a class="reference internal" href="source/braindecode.models.html#module-braindecode.models.util" title="braindecode.models.util"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models.util</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/524">#524</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Keep using <cite>mne.Raw</cite> after windowing to speed up windowing, do not create <cite>mne.Epochs</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/515">#515</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Changing <a class="reference internal" href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.Deep4Net</span></code></a> <cite>final_conv_length</cite> default value to ‘auto’ (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/535">#535</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Add support for <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a> in <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> and <a class="reference internal" href="source/braindecode.html#braindecode.EEGRegressor" title="braindecode.EEGRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/529">#529</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Allow passing only the name of a braindecode model to <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> and <a class="reference internal" href="source/braindecode.html#braindecode.EEGRegressor" title="braindecode.EEGRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/528">#528</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Standardizing models’ last layer names (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/520">#520</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a> and <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add basic training example with MNE epochs (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/539">#539</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Log validation accuracy in <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/541">#541</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Better type hints in <a class="reference internal" href="source/braindecode.augmentation.html#module-braindecode.augmentation.base" title="braindecode.augmentation.base"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.augmentation.base</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/551">#551</a> by <a class="reference external" href="https://github.com/tgy">Valentin Iovene</a>)</p></li>
+<li><p>Support for MOABB 1.0.0 and switch to pyproject.toml (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/553">#553</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding pre-commit hooks (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/556">#556</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+</ul>
+</section>
+<section id="id6">
+<h3>Bugs<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Fixing conda env in the CI (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/461">#461</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing E231 missing whitespace after ‘,’ untraceable error in old flake8 (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/460">#460</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing deprecation warning due to torch transposition in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.augmentation.functional._frequency_shift()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/446">#446</a> by <a class="reference external" href="https://github.com/matthieutrs">Matthieu Terris</a>)</p></li>
+<li><p>Fix padding’s device in <a class="reference internal" href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet" title="braindecode.models.EEGResNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGResNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/451">#451</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix skorch version issue (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/465">#465</a> by <a class="reference external" href="https://github.com/ZamboniMarco99">Marco Zamboni</a>)</p></li>
+<li><p>Fix wrong <cite>kernel_size</cite> dtype when running torchinfo in <a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/538">#538</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Fix bug when using GPU and channel shuffle transform (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/546">#546</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+</ul>
+</section>
+<section id="id7">
+<h3>API changes<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Renaming the <a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInception</span></code></a> network as <a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP" title="braindecode.models.EEGInceptionERP"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInceptionERP</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/428">#428</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Removing support for Python 3.7 (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/397">#397</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing the LogSoftmax layer from the models and adding deprecated warnings and temporary flags (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/513">#513</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="current-0-7-10-2022">
+<span id="changes-0-7-0"></span><h2>Current 0.7 (10-2022)<a class="headerlink" href="#current-0-7-10-2022" title="Link to this heading">#</a></h2>
+<section id="id8">
+<h3>Enhancements<a class="headerlink" href="#id8" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding EEG-Inception Network <a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInception</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/390">#390</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding EEG-ITNet Network <a class="reference internal" href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet" title="braindecode.models.EEGITNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGITNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/400">#400</a> by <a class="reference external" href="https://github.com/GhBlg">Ghaith Bouallegue</a>)</p></li>
+<li><p>Allowing target_names as list for BaseDataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/371">#371</a> by <a class="reference external" href="https://github.com/MohammadJavadD">Mohammad Javad D</a> and <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Adding tutorial with GridSearchCV for data augmentation on the BCIC IV 2a with module <cite>braindecode.augmentation</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/389">#389</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding tutorial with GridSearchCV to exemplify how to tune hyperparameters, for instance with the learning rate (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/349">#349</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a> and by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding tutorial with a Unified Validation scheme (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/378">#378</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a>)</p></li>
+<li><p>Adding <cite>verbose</cite> parameter to <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events()</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels()</span></code></a>, and <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/391">#391</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Enable augmentation on GPU within <code class="xref py py-class docutils literal notranslate"><span class="pre">AugmentedDataloader</span></code> via a new <cite>device</cite> parameter (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/406">#406</a> by <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <cite>randomize</cite> parameter to <a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/504">#504</a> by <a href="#id19"><span class="problematic" id="id20">`Théo Gnassounou`_</span></a>.)</p></li>
+</ul>
+</section>
+<section id="id9">
+<h3>Bugs<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Fixing parameter <cite>subject_ids</cite> to <cite>recoding_ids</cite> in TUHAbnormal example (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/402">#402</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Bug fix <a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.functional.ft_surrogate" title="braindecode.augmentation.functional.ft_surrogate"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.augmentation.functional.ft_surrogate()</span></code></a> and add option to sample independently per-channel (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/409">#409</a> by <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+</ul>
+</section>
+<section id="id10">
+<h3>API changes<a class="headerlink" href="#id10" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Renaming the method <cite>get_params</cite> to <cite>get_augmentation_params</cite> in augmentation classes. This makes the Transform module compatible with scikit-learn cloning mechanism (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/388">#388</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
+<li><p>Delaying the deprecation of the preprocessing scale function <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code> and updates tutorials where the function were used. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/413">#413</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing deprecated functions and classes <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.zscore()</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.MNEPreproc</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.NumpyPreproc</span></code>  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/415">#415</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Setting <cite>iterator_train__drop_last=True</cite> by default for <a class="reference internal" href="source/braindecode.html#braindecode.EEGClassifier" title="braindecode.EEGClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code></a> and <a class="reference internal" href="source/braindecode.html#braindecode.EEGRegressor" title="braindecode.EEGRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/411">#411</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-6-2021-12-06">
+<span id="changes-0-6-0"></span><h2>Version 0.6 (2021-12-06)<a class="headerlink" href="#version-0-6-2021-12-06" title="Link to this heading">#</a></h2>
+<section id="id11">
+<h3>Enhancements<a class="headerlink" href="#id11" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding <a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> along with support for returning sequences of windows in <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> and an updated sleep staging example to show how to train on sequences of windows (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/263">#263</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding Thinker Invariance Network <a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.TIDNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/170">#170</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Adding a confusion matrix plot generator <a class="reference internal" href="source/braindecode.visualization.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/274">#274</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a href="#id21"><span class="problematic" id="id22">`Dan Wilson`_</span></a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Adding data <a class="reference internal" href="api.html#augmentation-api"><span class="std std-ref">Augmentation</span></a> module (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/254">#254</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>, <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a> and <a class="reference external" href="https://tommoral.github.io">Thomas Moreau</a>)</p></li>
+<li><p>Adding Mixup augmentation <a class="reference internal" href="source/braindecode.augmentation.html#braindecode.augmentation.Mixup" title="braindecode.augmentation.Mixup"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.augmentation.Mixup</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/254">#254</a> by <a class="reference external" href="https://github.com/sbbrandt">Simon Brandt</a>)</p></li>
+<li><p>Adding saving of preprocessing and windowing choices in <a class="reference internal" href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a>, <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events()</span></code></a> and <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows()</span></code></a> to datasets to facilitate reproducibility (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/287">#287</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="source/braindecode.models.html#braindecode.models.util.aggregate_probas" title="braindecode.models.util.aggregate_probas"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.models.util.aggregate_probas()</span></code></a> to perform self-ensembling of predictions with sequence-to-sequence models (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/294">#294</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="source/braindecode.training.html#braindecode.training.scoring.predict_trials" title="braindecode.training.scoring.predict_trials"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.training.scoring.predict_trials()</span></code></a> to generate trialwise predictions after cropped training (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/312">#312</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Preprocessing and windowing choices are now saved on the level of individual datasets (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/288">#288</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Serialization now happens entirely on dataset level creating subsets for individual datasets that contain ‘fif’ and ‘json’ files (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/288">#288</a> <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Instantiation of TUH <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUH" title="braindecode.datasets.tuh.TUH"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.tuh.TUH</span></code></a> and TUHAbnormal <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.tuh.TUHAbnormal" title="braindecode.datasets.tuh.TUHAbnormal"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.tuh.TUHAbnormal</span></code></a>, as well as loading <a class="reference internal" href="source/braindecode.datautil.html#braindecode.datautil.serialization.load_concat_dataset" title="braindecode.datautil.serialization.load_concat_dataset"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.serialization.load_concat_dataset()</span></code></a> of stored datasets now support multiple workers (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/288">#288</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding balanced sampling of sequences of windows with <a class="reference internal" href="source/braindecode.samplers.html#braindecode.samplers.BalancedSequenceSampler" title="braindecode.samplers.BalancedSequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.BalancedSequenceSampler</span></code></a>  as proposed in U-Sleep paper (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/295">#295</a> by <a class="reference external" href="https://github.com/Tgnassou">Theo Gnassounou</a> and <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p><a class="reference internal" href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> can now work in parallel and serialize datasets to enable lazy-loading (i.e. <cite>preload=False</cite>) (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/277">#277</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.TimeDistributed</span></code></a> to apply a module on a sequence (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/318">#318</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding time series targets decoding together with <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BCICompetitionIVDataset4</span></code></a> and fingers flexion decoding from ECoG examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/261">#261</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a> and <a class="reference external" href="https://github.com/MFattouh">Mohammed Fattouh</a>)</p></li>
+<li><p>Make EEGClassifier and EEGRegressor cloneable for scikit-learn (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/347">#347</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>, <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>, <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a> and <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
+<li><p>Allow to raise a warning when a few trials are shorter than the windows length, instead of raising an error and stopping all computation. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/353">#353</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Setting <cite>torch.backends.cudnn.benchmark</cite> in <a class="reference internal" href="source/braindecode.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.util.set_random_seeds()</span></code></a>, adding warning and more info to the docstring to improve reproducibility (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/333">#333</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Adding option to pass arguments through <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.MOABBDataset</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/365">#365</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Adding a possibility to use a dict to split a BaseConcatDataset in <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset.split()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/367">#367</a> by <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
+<li><p>Adding <code class="docutils literal notranslate"><span class="pre">crop</span></code> parameter to <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> dataset to speed up examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/367">#367</a> by <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
+</ul>
+</section>
+<section id="id12">
+<h3>Bugs<a class="headerlink" href="#id12" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Correctly computing recording length in <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_fixed_length_windows" title="braindecode.preprocessing.windowers.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_fixed_length_windows()</span></code></a> in case recording was cropped (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/304">#304</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Fixing <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> to allow serialization and avoid mismatch in channel names attributes (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/327">#327</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Propagating <cite>target_transform</cite> to all datasets when using <code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset.subset()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/261">#261</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+</ul>
+</section>
+<section id="id13">
+<h3>API changes<a class="headerlink" href="#id13" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Removing the default sampling frequency sfreq value in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datasets.create_windows_from_events()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/256">#256</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Made windowing arguments optional in <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_fixed_length_windows" title="braindecode.preprocessing.windowers.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_fixed_length_windows()</span></code></a> &amp; <a class="reference internal" href="source/braindecode.preprocessing.html#braindecode.preprocessing.windowers.create_windows_from_events" title="braindecode.preprocessing.windowers.create_windows_from_events"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_windows_from_events()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/269">#269</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a href="#id23"><span class="problematic" id="id24">`Dan Wilson`_</span></a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Deprecating preprocessing functions <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.zscore()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code> in favour of sklearn’s implementation (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/292">#292</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p><a class="reference internal" href="source/braindecode.preprocessing.html#module-braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> now returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.dataset.BaseConcatDataset</span></code> object (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/277">#277</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="version-0-5-1-2021-07-14">
+<span id="changes-0-5-1"></span><h2>Version 0.5.1 (2021-07-14)<a class="headerlink" href="#version-0-5-1-2021-07-14" title="Link to this heading">#</a></h2>
+<section id="id14">
+<h3>Enhancements<a class="headerlink" href="#id14" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding <cite>n_jobs</cite> parameter to windowers <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_windows_from_events()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_fixed_length_windows()</span></code> to allow for parallelization of the windowing process (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/199">#199</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding support for on-the-fly transforms (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/198">#198</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Unifying preprocessors under the <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.Preprocessor</span></code> class (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/197">#197</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding self-supervised learning example on the Sleep Physionet dataset along with new sampler module <cite>braindecode.samplers</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/178">#178</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding sleep staging example on the Sleep Physionet dataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/161">#161</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding new parameters to windowers <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_windows_from_events()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_fixed_length_windows()</span></code> for finer control over epoching (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/152">#152</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Adding Temporal Convolutional Network <a class="reference internal" href="generated/braindecode.models.TCN.html#braindecode.models.TCN" title="braindecode.models.TCN"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.TCN</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/138">#138</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding option to use BaseConcatDataset as input to BaseConcatDataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/142">#142</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding a simplified API for splitting of BaseConcatDataset: parameters <cite>property</cite> and <cite>split_ids</cite> in <a class="reference internal" href="source/braindecode.datasets.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset.split()</span></code></a> are replaced by <cite>by</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/147">#147</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding a preprocessor that realizes a filterbank: <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.filterbank()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/158">#158</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Removing code duplicate in BaseDataset and WindowsDataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/159">#159</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Only load data if needed during preprocessing (e.g., allow timecrop without loading) (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/164">#164</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Adding option to sort filtered channels by frequency band for the filterbank in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.filterbank()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/185">#185</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding the USleep model <a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/282">#282</a> by <a class="reference external" href="https://github.com/Tgnassou">Theo Gnassounou</a> and <a class="reference external" href="https://github.com/l-omar-chehab">Omar Chehab</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code></a> and <a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020" title="braindecode.models.SleepStagerBlanco2020"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerBlanco2020</span></code></a> models for sleep staging  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/341">#341</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
+</ul>
+</section>
+<section id="id15">
+<h3>Bugs<a class="headerlink" href="#id15" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Amplitude gradients are correctly computed for layers with multiple filters
+(before, they were accidentally summed over all previous filters in the layer) (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/167">#167</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p><a class="reference internal" href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.models.get_output_shape()</span></code></a> and <a class="reference internal" href="source/braindecode.visualization.html#braindecode.visualization.compute_amplitude_gradients" title="braindecode.visualization.compute_amplitude_gradients"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.visualization.compute_amplitude_gradients()</span></code></a> assume 3d, not 4d inputs (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/166">#166</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Fixing windower functions when the continuous data has been cropped (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/152">#152</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Fixing incorrect usage of recording ids in TUHAbnormal (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/146">#146</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Adding check for correct input dimensions (4d) in TCN (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/169">#169</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Fixing <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_windows_from_events()</span></code> when <cite>window_size</cite> is not given but there is a <code class="code docutils literal notranslate"><span class="pre">trial_stop_offset_samples</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/148">#148</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
+<li><p>Fixing <a class="reference internal" href="source/braindecode.html#braindecode.classifier.EEGClassifier.predict_proba" title="braindecode.classifier.EEGClassifier.predict_proba"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.classifier.EEGClassifier.predict_proba()</span></code></a> and <code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.regressor.EEGRegressor.predict()</span></code> behavior in the cropped mode (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/171">#171</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Freeze torch random generator for scoring functions for reproducibility (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/155">#155</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Make EEGResNet work for <code class="code docutils literal notranslate"><span class="pre">final_pool_length='auto'</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/223">#223</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a> and <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+</ul>
+</section>
+<section id="id16">
+<h3>API changes<a class="headerlink" href="#id16" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Preprocessor classes <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.MNEPreproc</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.NumpyPreproc</span></code> are deprecated in favor of <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.Preprocessor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/197">#197</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Parameter <cite>stop_offset_samples</cite> of <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_fixed_length_windows()</span></code> must now be set to <cite>None</cite> instead of 0 to indicate the end of the recording (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/152">#152</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+</ul>
+</section>
+<section id="authors">
+<h3>Authors<a class="headerlink" href="#authors" title="Link to this heading">#</a></h3>
+</section>
+</section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#enhancements">Enhancements</a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#api-changes">API changes</a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id7">API changes</a></li>
+</ul>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id8">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id9">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id10">API changes</a></li>
+</ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-6-2021-12-06">Version 0.6 (2021-12-06)</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id11">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id12">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id13">API changes</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#version-0-5-1-2021-07-14">Version 0.5.1 (2021-07-14)</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id14">Enhancements</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id15">Bugs</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id16">API changes</a></li>
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diff --git a/stable/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb b/stable/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb
new file mode 100644
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+++ b/stable/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb
@@ -0,0 +1,291 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Sleep staging on the Sleep Physionet dataset using Chambon2018 network\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the time distributed approach of [1]_ to learn on\nsequences of EEG windows using the openly accessible Sleep Physionet dataset\n[2]_ [3]_.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading\n\nFirst, we load the data using the\n:class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load\ntwo recordings from two different individuals: we will use the first one to\ntrain our network and the second one to evaluate performance (as in the `MNE`_\nsleep staging example).\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from numbers import Integral\nfrom braindecode.datasets import SleepPhysionet\n\nsubject_ids = [0, 1]\ndataset = SleepPhysionet(\n    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nNext, we preprocess the raw data. We convert the data to microvolts and apply\na lowpass filter. We omit the downsampling step of [1]_ as the Sleep\nPhysionet data is already sampled at a lower 100 Hz.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extract windows\n\nWe extract 30-s windows to be used in the classification task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Window preprocessing\n\nWe also preprocess the windows by applying channel-wise z-score normalization\nin each window.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import scale as standard_scale\n\npreprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\nWe split the dataset into training and validation set taking\nevery other subject as train or valid.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])\nsplits = windows_dataset.split(split_ids)\ntrain_set, valid_set = splits[\"train\"], splits[\"valid\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create sequence samplers\n\nFollowing the time distributed approach of [1]_, we need to provide our\nneural network with sequences of windows, such that the embeddings of\nmultiple consecutive windows can be concatenated and provided to a final\nclassifier. We can achieve this by defining Sampler objects that return\nsequences of window indices.\nTo simplify the example, we train the whole model end-to-end on sequences,\nrather than using the two-step approach of [1]_ (i.e. training the feature\nextractor on single windows, then freezing its weights and training the\nclassifier).\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also implement a transform to extract the label of the center window of a\nsequence to use it as target.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Use label of center window in the sequence\ndef get_center_label(x):\n    if isinstance(x, Integral):\n        return x\n    return x[np.ceil(len(x) / 2).astype(int)] if len(x) > 1 else x\n\n\ntrain_set.target_transform = get_center_label\nvalid_set.target_transform = get_center_label"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, since some sleep stages appear a lot more often than others (e.g.\nmost of the night is spent in the N2 stage), the classes are imbalanced. To\navoid overfitting on the more frequent classes, we compute weights that we\nwill provide to the loss function when training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.utils import compute_class_weight\n\ny_train = [train_set[idx][1] for idx in train_sampler]\nclass_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\nWe can now create the deep learning model. In this tutorial, we use the sleep\nstaging architecture introduced in [1]_, which is a four-layer convolutional\nneural network. We use the time distributed version of the model, where the\nfeature vectors of a sequence of windows are concatenated and passed to a\nlinear layer for classification.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_outputs=n_classes,\n    n_times=input_size_samples,\n    return_feats=True\n)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\nWe can now train our network. :class:`braindecode.EEGClassifier` is a\nbraindecode object that is responsible for managing the training of neural\nnetworks. It inherits from :class:`skorch.NeuralNetClassifier`, so the\ntraining logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>We use different hyperparameters from [1]_, as these hyperparameters were\n   optimized on a different dataset (MASS SS3) and with a different number of\n   recordings. Generally speaking, it is recommended to perform\n   hyperparameter optimization if reusing this code on a different dataset or\n   with more recordings.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 10\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=np.unique(y_train),\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot results\n\nWe use the history stored by Skorch during training to plot the performance of\nthe model throughout training. Specifically, we plot the loss and the balanced\nbalanced accuracy for the training and validation sets.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\ndf.index.name = \"Epoch\"\nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\ndf[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)\ndf[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)\nax1.set_ylabel('Loss')\nax2.set_ylabel('Balanced accuracy')\nax1.legend(['Train', 'Valid'])\nax2.legend(['Train', 'Valid'])\nfig.tight_layout()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we also display the confusion matrix and classification report:\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, classification_report\nfrom braindecode.visualization import plot_confusion_matrix\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\nplot_confusion_matrix(confusion_mat=confusion_mat,\n                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])\n\nprint(classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true, color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Our model was able to learn despite the low amount of data that was available\n(only two recordings in this example) and reached a balanced accuracy of\nabout 36% in a 5-class classification task (chance-level = 20%) on held-out\ndata.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To further improve performance, more recordings should be included in the\n   training set, and hyperparameters should be selected accordingly.\n   Increasing the sequence length was also shown in [1]_ to help improve\n   performance, especially when few EEG channels are available.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py b/stable/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py
new file mode 100644
index 00000000..d5ad7952
--- /dev/null
+++ b/stable/_downloads/0a8b8bc2f1b933515b7b4101626dd179/plot_bcic_iv_2a_moabb_trial.py
@@ -0,0 +1,351 @@
+"""
+Basic Brain Decoding on EEG Data
+========================================
+
+This tutorial shows you how to train and test deep learning models with
+Braindecode in a classical EEG setting: you have trials of data with
+labels (e.g., Right Hand, Left Hand, etc.).
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Loading and preparing the data
+# -------------------------------------
+#
+
+
+######################################################################
+# Loading the dataset
+# ~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# First, we load the data. In this tutorial, we load the BCI Competition
+# IV 2a data [1]_ using braindecode's wrapper to load via
+# `MOABB library <https://github.com/NeuroTechX/moabb>`__ [2]_.
+#
+# .. note::
+#    To load your own datasets either via mne or from
+#    preprocessed X/y numpy arrays, see `MNE Dataset
+#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
+#    Tutorial <./plot_custom_dataset_example.html>`__.
+#
+
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
+
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Now we apply preprocessing like bandpass filtering to our dataset. You
+# can either apply functions provided by
+# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
+# `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
+# or apply your own functions, either to the MNE object or the underlying
+# numpy array.
+#
+# .. note::
+#    Generally, braindecode prepocessing is directly applied to the loaded
+#    data, and not applied on-the-fly as transformations, such as in
+#    PyTorch-libraries like
+#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
+#
+
+from numpy import multiply
+
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+# Transform the data
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+
+######################################################################
+# Extracting Compute Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Now we extract compute windows from the signals, these will be the inputs
+# to the deep networks during training. In the case of trialwise
+# decoding, we just have to decide if we want to include some part
+# before and/or after the trial. For our work with this dataset,
+# it was often beneficial to also include the 500 ms before the trial.
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+
+######################################################################
+# Splitting the dataset into training and validation sets
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case ``session`` column. We select
+# ``T`` for training and ``test`` for validation.
+#
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
+
+
+######################################################################
+# Creating a model
+# ------------
+#
+
+
+######################################################################
+# Now we create the deep learning model! Braindecode comes with some
+# predefined convolutional neural network architectures for raw
+# time-domain EEG. Here, we use the shallow ConvNet model from [3]_. These models are
+# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
+# to use your own model, it just has to be a normal PyTorch
+# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
+#
+
+import torch
+
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans and time steps from dataset
+n_chans = train_set[0][0].shape[0]
+input_window_samples = train_set[0][0].shape[1]
+
+model = ShallowFBCSPNet(
+    n_chans,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length='auto',
+)
+
+# Display torchinfo table describing the model
+print(model)
+
+# Send model to GPU
+if cuda:
+    model = model.cuda()
+
+
+######################################################################
+# Model Training
+# --------------
+#
+
+
+######################################################################
+# Now we will train the network! ``EEGClassifier`` is a Braindecode object
+# responsible for managing the training of neural networks. It inherits
+# from skorch `NeuralNetClassifier <https://skorch.readthedocs.io/en/stable/classifier.html#>`__,
+# so the training logic is the same as in `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+
+
+######################################################################
+# .. note::
+#    In this tutorial, we use some default parameters that we
+#    have found to work well for motor decoding, however we strongly
+#    encourage you to perform your own hyperparameter optimization using
+#    cross validation on your training data.
+#
+
+from skorch.callbacks import LRScheduler
+from skorch.helper import predefined_split
+
+from braindecode import EEGClassifier
+
+# We found these values to be good for the shallow network:
+lr = 0.0625 * 0.01
+weight_decay = 0
+
+# For deep4 they should be:
+# lr = 1 * 0.01
+# weight_decay = 0.5 * 0.001
+
+batch_size = 64
+n_epochs = 4
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(valid_set),  # using valid_set for validation
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy", ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+)
+# Model training for the specified number of epochs. `y` is None as it is
+# already supplied in the dataset.
+_ = clf.fit(train_set, y=None, epochs=n_epochs)
+
+
+######################################################################
+# Plotting Results
+# ------------
+#
+
+
+######################################################################
+# Now we use the history stored by Skorch throughout training to plot
+# accuracy and loss curves.
+#
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.lines import Line2D
+
+# Extract loss and accuracy values for plotting from history object
+results_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']
+df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
+                  index=clf.history[:, 'epoch'])
+
+# get percent of misclass for better visual comparison to loss
+df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
+               valid_misclass=100 - 100 * df.valid_accuracy)
+
+fig, ax1 = plt.subplots(figsize=(8, 3))
+df.loc[:, ['train_loss', 'valid_loss']].plot(
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
+
+ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
+ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
+
+ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+df.loc[:, ['train_misclass', 'valid_misclass']].plot(
+    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
+ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
+ax2.set_ylabel("Misclassification Rate [%]", color='tab:red', fontsize=14)
+ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend
+ax1.set_xlabel("Epoch", fontsize=14)
+
+# where some data has already been plotted to ax
+handles = []
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
+plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
+plt.tight_layout()
+
+
+######################################################################
+# Plotting a  Confusion Matrix
+# ----------------------------
+#
+
+
+#######################################################################
+# Here we generate a confusion matrix as in [3]_.
+#
+
+
+from sklearn.metrics import confusion_matrix
+
+from braindecode.visualization import plot_confusion_matrix
+
+# generate confusion matrices
+# get the targets
+y_true = valid_set.get_metadata().target
+y_pred = clf.predict(valid_set)
+
+# generating confusion matrix
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+# add class labels
+# label_dict is class_name : str -> i_class : int
+label_dict = windows_dataset.datasets[0].window_kwargs[0][1]['mapping']
+# sort the labels by values (values are integer class labels)
+labels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]
+
+# plot the basic conf. matrix
+plot_confusion_matrix(confusion_mat, class_names=labels)
+
+#############################################################
+#
+#
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb b/stable/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb
new file mode 100644
index 00000000..77f4bd58
--- /dev/null
+++ b/stable/_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb
@@ -0,0 +1,183 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Benchmarking eager and lazy loading\n\nIn this example, we compare the execution time and memory requirements of 1)\neager loading, i.e., preloading the entire data into memory and 2) lazy loading,\ni.e., only loading examples from disk when they are required. We also include\nsome other experiment parameters in the comparison for the sake of completeness\n(e.g., `num_workers`, `cuda`, `batch_size`, etc.).\n\nWhile eager loading might be required for some preprocessing steps that require\ncontinuous data (e.g., temporal filtering, resampling), it also allows\nfast access to the data during training. However, this might come at the expense\nof large memory usage, and can ultimately become impossible if the dataset does\nnot fit into memory (e.g., the TUH EEG dataset's >1,5 TB of recordings will\nnot fit in the memory of most machines).\n\nLazy loading avoids this potential memory issue by loading examples from disk\nwhen they are required. This means large datasets can be used for training,\nhowever this introduces some file-reading overhead every time an example must\nbe extracted. Some preprocessing steps that require continuous data also have to\nbe implemented differently to accommodate the nature of windowed data. Overall\nthough, we can reduce the impact of lazy loading by using the `num_workers`\nparameter of pytorch's `Dataloader` class, which dispatches the data loading to\nmultiple processes.\n\nTo enable lazy loading in braindecode, data files must be saved in an\nMNE-compatible format (e.g., 'fif', 'edf', etc.), and the `Dataset` object must\nhave been instantiated with parameter `preload=False`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom itertools import product\nimport time\n\nimport torch\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader\n\nimport mne\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nfrom braindecode.datasets import TUHAbnormal\nfrom braindecode.preprocessing import create_fixed_length_windows\nfrom braindecode.models import ShallowFBCSPNet, Deep4Net\n\n\nmne.set_log_level('WARNING')  # avoid messages every time a window is extracted"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We start by setting two pytorch internal parameters that can affect the\ncomparison::\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "N_JOBS = 8\ntorch.backends.cudnn.benchmark = True  # Enables automatic algorithm optimizations\ntorch.set_num_threads(N_JOBS)  # Sets the available number of threads"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we define a few functions to automate the benchmarking.\nFor the purpose of this example, we load some recordings from the TUH Abnormal\ncorpus, extract sliding windows, and bundle them in a braindecode Dataset.\nWe then train a neural network for a few epochs.\n\nEach one of these steps will be timed, so we can report the total time taken\nto prepare the data and train the model.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def load_example_data(preload, window_len_s, n_recordings=10):\n    \"\"\"Create windowed dataset from subjects of the TUH Abnormal dataset.\n\n    Parameters\n    ----------\n    preload: bool\n        If True, use eager loading, otherwise use lazy loading.\n    window_len_s: int\n        Window length in seconds.\n    n_recordings: list of int\n        Number of recordings to load.\n\n    Returns\n    -------\n    windows_ds: BaseConcatDataset\n        Windowed data.\n\n    .. warning::\n        The recordings from the TUH Abnormal corpus do not all share the same\n        sampling rate. The following assumes that the files have already been\n        resampled to a common sampling rate.\n    \"\"\"\n\n    recording_ids = list(range(n_recordings))\n\n    ds = TUHAbnormal(\n        TUH_PATH, recording_ids=recording_ids,\n        target_name='pathological',\n        preload=preload)\n\n    fs = ds.datasets[0].raw.info['sfreq']\n    window_len_samples = int(fs * window_len_s)\n    window_stride_samples = int(fs * 4)\n    # window_stride_samples = int(fs * window_len_s)\n    windows_ds = create_fixed_length_windows(\n        ds, start_offset_samples=0, stop_offset_samples=None,\n        window_size_samples=window_len_samples,\n        window_stride_samples=window_stride_samples, drop_last_window=True,\n        preload=preload)\n\n    # Drop bad epochs\n    # XXX: This could be parallelized.\n    # XXX: Also, this could be implemented in the Dataset object itself.\n    for ds in windows_ds.datasets:\n        ds.windows.drop_bad()\n        assert ds.windows.preload == preload\n\n    return windows_ds\n\n\ndef create_example_model(n_channels, n_classes, window_len_samples,\n                         kind='shallow', cuda=False):\n    \"\"\"Create model, loss and optimizer.\n\n    Parameters\n    ----------\n    n_channels : int\n        Number of channels in the input\n    n_times : int\n        Window length in the input\n    n_classes : int\n        Number of classes in the output\n    kind : str\n        'shallow' or 'deep'\n    cuda : bool\n        If True, move the model to a CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Model to train.\n    loss :\n        Loss function\n    optimizer :\n        Optimizer\n    \"\"\"\n    if kind == 'shallow':\n        model = ShallowFBCSPNet(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            n_filters_time=40, filter_time_length=25, n_filters_spat=40,\n            pool_time_length=75, pool_time_stride=15, final_conv_length='auto',\n            split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,\n            drop_prob=0.5)\n    elif kind == 'deep':\n        model = Deep4Net(\n            n_channels, n_classes, input_window_samples=window_len_samples,\n            final_conv_length='auto', n_filters_time=25, n_filters_spat=25,\n            filter_time_length=10, pool_time_length=3, pool_time_stride=3,\n            n_filters_2=50, filter_length_2=10, n_filters_3=100,\n            filter_length_3=10, n_filters_4=200, filter_length_4=10,\n            first_pool_mode=\"max\", later_pool_mode=\"max\", drop_prob=0.5,\n            double_time_convs=False, split_first_layer=True, batch_norm=True,\n            batch_norm_alpha=0.1, stride_before_pool=False)\n    else:\n        raise ValueError\n\n    if cuda:\n        model.cuda()\n\n    optimizer = optim.Adam(model.parameters())\n    loss = nn.NLLLoss()\n\n    return model, loss, optimizer\n\n\ndef run_training(model, dataloader, loss, optimizer, n_epochs=1, cuda=False):\n    \"\"\"Run training loop.\n\n    Parameters\n    ----------\n    model : torch.nn.Module\n        Model to train.\n    dataloader : torch.utils.data.Dataloader\n        Data loader which will serve examples to the model during training.\n    loss :\n        Loss function.\n    optimizer :\n        Optimizer.\n    n_epochs : int\n        Number of epochs to train the model for.\n    cuda : bool\n        If True, move X and y to CUDA device.\n\n    Returns\n    -------\n    model : torch.nn.Module\n        Trained model.\n    \"\"\"\n    for i in range(n_epochs):\n        loss_vals = list()\n        for X, y, _ in dataloader:\n            model.train()\n            model.zero_grad()\n\n            y = y.long()\n            if cuda:\n                X, y = X.cuda(), y.cuda()\n\n            loss_val = loss(model(X), y)\n            loss_vals.append(loss_val.item())\n\n            loss_val.backward()\n            optimizer.step()\n\n        print(f'Epoch {i + 1} - mean training loss: {np.mean(loss_vals)}')\n\n    return model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we define the different hyperparameters that we want to compare:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "PRELOAD = [True, False]  # True -> eager loading; False -> lazy loading\nN_RECORDINGS = [10]  # Number of recordings to load from the TUH Abnormal corpus\nWINDOW_LEN_S = [2, 4, 15]  # Window length, in seconds\nN_EPOCHS = [2]  # Number of epochs to train the model for\nBATCH_SIZE = [64, 256]  # Training minibatch size\nMODEL = ['shallow', 'deep']\n\nNUM_WORKERS = [8, 0]  # number of processes used by pytorch's Dataloader\nPIN_MEMORY = [False]  # whether to use pinned memory\nCUDA = [True, False] if torch.cuda.is_available() else [False]  # whether to use a CUDA device\n\nN_REPETITIONS = 3  # Number of times to repeat the experiment (to get better time estimates)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following path needs to be changed to your local folder containing the\nTUH Abnormal corpus:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "TUH_PATH = ('/storage/store/data/tuh_eeg/www.isip.piconepress.com/projects/'\n            'tuh_eeg/downloads/tuh_eeg_abnormal/v2.0.0/edf/')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can finally cycle through all the different combinations of the parameters\nwe set above to evaluate their execution time:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "all_results = list()\nfor (i, preload, n_recordings, win_len_s, n_epochs, batch_size, model_kind,\n        num_workers, pin_memory, cuda) in product(\n            range(N_REPETITIONS), PRELOAD, N_RECORDINGS, WINDOW_LEN_S, N_EPOCHS,\n            BATCH_SIZE, MODEL, NUM_WORKERS, PIN_MEMORY, CUDA):\n\n    results = {\n        'repetition': i,\n        'preload': preload,\n        'n_recordings': n_recordings,\n        'win_len_s': win_len_s,\n        'n_epochs': n_epochs,\n        'batch_size': batch_size,\n        'model_kind': model_kind,\n        'num_workers': num_workers,\n        'pin_memory': pin_memory,\n        'cuda': cuda\n    }\n    print(f'\\nRepetition {i + 1}/{N_REPETITIONS}:\\n{results}')\n\n    # Load the dataset\n    data_loading_start = time.time()\n    dataset = load_example_data(preload, win_len_s, n_recordings=n_recordings)\n    data_loading_end = time.time()\n\n    # Create the data loader\n    training_setup_start = time.time()\n    dataloader = DataLoader(\n        dataset, batch_size=batch_size, shuffle=False, pin_memory=pin_memory,\n        num_workers=num_workers, worker_init_fn=None)\n\n    # Instantiate model and optimizer\n    n_channels = len(dataset.datasets[0].windows.ch_names)\n    n_times = len(dataset.datasets[0].windows.times)\n    n_classes = 2\n    model, loss, optimizer = create_example_model(\n        n_channels, n_classes, n_times, kind=model_kind, cuda=cuda)\n    training_setup_end = time.time()\n\n    # Start training loop\n    model_training_start = time.time()\n    trained_model = run_training(\n        model, dataloader, loss, optimizer, n_epochs=n_epochs, cuda=cuda)\n    model_training_end = time.time()\n\n    del dataset, model, loss, optimizer, trained_model\n\n    # Record timing results\n    results['data_preparation'] = data_loading_end - data_loading_start\n    results['training_setup'] = training_setup_end - training_setup_start\n    results['model_training'] = model_training_end - model_training_start\n    all_results.append(results)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The results are formatted into a pandas DataFrame and saved locally as a CSV\nfile.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results_df = pd.DataFrame(all_results)\nfname = 'lazy_vs_eager_loading_results.csv'\nresults_df.to_csv(fname)\nprint(f'Results saved under {fname}.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can finally summarize this information into the following plot:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "sns.catplot(\n    data=results_df, row='cuda', x='model_kind', y='model_training',\n    hue='num_workers', col='preload', kind='strip')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>The results of this comparison will change depending on the hyperparameters\n  that were set above, and on the actual hardware that is being used.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Generally speaking, we expect lazy loading to be slower than eager loading\nduring model training, but to potentially be pretty competitive if multiple\nworkers were enabled (i.e.., `num_workers > 0`). Training on a CUDA device\nshould also yield substantial speedups.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py b/stable/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py
new file mode 100644
index 00000000..6fe716a8
--- /dev/null
+++ b/stable/_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py
@@ -0,0 +1,344 @@
+"""
+Searching the best data augmentation on BCIC IV 2a Dataset
+====================================================================================
+
+This tutorial shows how to search data augmentations using braindecode.
+Indeed, it is known that the best augmentation to use often dependent on the task
+or phenomenon studied. Here we follow the methodology proposed in [1]_ on the
+openly available BCI IV 2a Dataset.
+
+
+.. topic:: Data Augmentation
+
+    Data augmentation could be a step in training deep learning models.
+    For decoding brain signals, recent studies have shown that artificially
+    generating samples may increase the final performance of a deep learning model [1]_.
+    Other studies have shown that data augmentation can be used to cast
+    a self-supervised paradigm, presenting a more diverse
+    view of the data, both with pretext tasks and contrastive learning [2]_.
+
+
+Data augmentation and self-supervised learning approaches demand an intense comparison
+to find the best fit with the data. This view is demonstrated in [1]_ and shows the
+importance of selecting the right transformation and strength for different type of
+task considered. Here, we use the augmentation module present in braindecode in
+the context of trialwise decoding with the BCI IV 2a dataset.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Bruno Aristimunha <a.bruno@ufabc.edu.br>
+#          Cédric Rommel <cedric.rommel@inria.fr>
+# License: BSD (3-clause)
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading
+# ~~~~~~~
+#
+# First, we load the data. In this tutorial, we use the functionality of braindecode
+# to load BCI IV competition dataset 1. The dataset is available on the BNCI website.
+# There is 9 subjects recorded with 22 electrodes while doing a motor imagery task,
+# with 144 trials per class. We will load this dataset through the MOABB library.
+
+from skorch.callbacks import LRScheduler
+
+from braindecode import EEGClassifier
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# We apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related
+# brain activity
+
+from braindecode.preprocessing import (
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+######################################################################
+# In time series targets setup, targets variables are stored in mne.Raw object as channels
+# of type `misc`. Thus those channels have to be selected for further processing. However,
+# many mne functions ignore `misc` channels and perform operations only on data channels
+# (see https://mne.tools/stable/glossary.html#term-data-channels).
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Extracting windows
+# ~~~~~~~~~~~~~~~~~~
+#
+# Now we cut out compute windows, the inputs for the deep networks during
+# training. We use the braindecode function for this, provinding parameters
+# to define how trials should be used.
+
+
+from braindecode.preprocessing import create_windows_from_events
+from skorch.helper import SliceDataset
+from numpy import array
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Following the split defined in the BCI competition
+
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Defining a list of transforms
+# ------------------------------
+#
+# In this tutorial, we will use three categories of augmentations.
+# This categorization has been proposed by [1]_ to explain and aggregate
+# the several possibilities of augmentations in EEG, being them:
+#
+# a) Frequency domain augmentations,
+# b) Time domain augmentations,
+# c) Spatial domain augmentations.
+#
+# From this same paper, we selected the best augmentations in each type: ``FTSurrogate``,
+# ``SmoothTimeMask``, ``ChannelsDropout``, respectively.
+#
+# For each augmentation, we adjustable two values from a range for one parameter
+# inside the transformation.
+#
+# It is important to remember that you can increase the range.
+# For that, we need to define three lists of transformations and range for the parameter
+# ∆φmax in FTSurrogate where ∆φmax ∈ [0, 2π); for ∆t in SmoothTimeMask is ∆t ∈ [0, 2];
+# For the method ChannelsDropout, we analyse the parameter p_drop ∈ [0, 1].
+
+from numpy import linspace
+from braindecode.augmentation import FTSurrogate, SmoothTimeMask, ChannelsDropout
+
+seed = 20200220
+
+transforms_freq = [FTSurrogate(probability=0.5, phase_noise_magnitude=phase_freq,
+                               random_state=seed) for phase_freq in linspace(0, 1, 2)]
+
+transforms_time = [SmoothTimeMask(probability=0.5, mask_len_samples=int(sfreq * second),
+                                  random_state=seed) for second in linspace(0.1, 2, 2)]
+
+transforms_spatial = [ChannelsDropout(probability=0.5, p_drop=prob,
+                                      random_state=seed) for prob in linspace(0, 1, 2)]
+
+######################################################################
+# Training a model with data augmentation
+# ---------------------------------------
+#
+# Now that we know how to instantiate three list of ``Transforms``, it is time to learn how
+# to use them to train a model and try to search the best for the dataset.
+# Let's first create a model for search a parameter.
+#
+# Create model
+# ~~~~~~~~~~~~
+#
+# The model to be trained is defined as usual.
+import torch
+
+from braindecode.util import set_random_seeds
+from braindecode.models import ShallowFBCSPNet
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+
+
+######################################################################
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set ``cudnn_benchmark=False`` in ``set_random_seeds``
+# or remove ``torch.backends.cudnn.benchmark = True``
+
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans and time steps from dataset
+n_channels = train_set[0][0].shape[0]
+input_window_samples = train_set[0][0].shape[1]
+
+model = ShallowFBCSPNet(
+    n_channels,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length='auto',
+)
+
+######################################################################
+# Create an EEGClassifier with the desired augmentation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# In order to train with data augmentation, a custom data loader can be
+# for the training. Multiple transforms can be passed to it and will be applied
+# sequentially to the batched data within the ``AugmentedDataLoader`` object.
+
+from braindecode.augmentation import AugmentedDataLoader
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+##########################################################################
+# The model is now trained as in the trial-wise example. The
+# ``AugmentedDataLoader`` is used as the train iterator and the list of
+# transforms are passed as arguments.
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+
+batch_size = 64
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader
+    iterator_train__transforms=[],  # This sets is handled by GridSearchCV
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=None,  # GridSearchCV will control the split and train/validation over the dataset
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        'accuracy',
+        ('lr_scheduler', LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes
+)
+
+#####################################################################
+# To use the skorch framework, it is necessary to transform the windows
+# dataset using the module SliceData. Also, it is mandatory to eval the
+# generator of the training.
+
+train_X = SliceDataset(train_set, idx=0)
+train_y = array(list(SliceDataset(train_set, idx=1)))
+
+#######################################################################
+#   Given the trialwise approach, here we use the KFold approach and
+#   GridSearchCV.
+
+from sklearn.model_selection import KFold, GridSearchCV
+
+cv = KFold(n_splits=2, shuffle=True, random_state=seed)
+fit_params = {'epochs': n_epochs}
+
+transforms = transforms_freq + transforms_time + transforms_spatial
+
+param_grid = {
+    'iterator_train__transforms': transforms,
+}
+
+clf.verbose = 0
+
+search = GridSearchCV(
+    estimator=clf,
+    param_grid=param_grid,
+    cv=cv,
+    return_train_score=True,
+    scoring='accuracy',
+    refit=True,
+    verbose=1,
+    error_score='raise')
+
+search.fit(train_X, train_y, **fit_params)
+
+######################################################################
+# Analysing the best fit
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Next, just perform an analysis of the best fit, and the parameters,
+# remembering the order that was adjusted.
+
+import pandas as pd
+import numpy as np
+
+search_results = pd.DataFrame(search.cv_results_)
+
+best_run = search_results[search_results['rank_test_score'] == 1].squeeze()
+best_aug = best_run['params']
+validation_score = np.around(best_run['mean_test_score'] * 100, 2).mean()
+training_score = np.around(best_run['mean_train_score'] * 100, 2).mean()
+
+report_message = 'Best augmentation is saved in best_aug which gave a mean validation accuracy' + \
+                 'of {}% (train accuracy of {}%).'.format(validation_score, training_score)
+
+print(report_message)
+
+eval_X = SliceDataset(eval_set, idx=0)
+eval_y = SliceDataset(eval_set, idx=1)
+score = search.score(eval_X, eval_y)
+print(f'Eval accuracy is {score * 100:.2f}%.')
+
+######################################################################
+# Plot results
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+search_results.plot.bar(
+    x="param_iterator_train__transforms", y="mean_train_score", yerr="std_train_score",
+    rot=45, color=["C0", "C0", "C1", "C1", "C2", "C2"], legend=None, ax=ax)
+ax.set_xlabel("Data augmentation strategy")
+ax.set_ylim(0.2, 0.32)
+plt.tight_layout()
+
+######################################################################
+# References
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+#
+# .. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)
+#        Data augmentation for learning predictive models on EEG:
+#        a systematic comparison. https://arxiv.org/abs/2206.14483
+# .. [2] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A. (2021).
+#        Uncovering the structure of clinical EEG signals with self-supervised learning.
+#        Journal of Neural Engineering, 18(4), 046020.
diff --git a/stable/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb b/stable/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb
new file mode 100644
index 00000000..4c1b1c91
--- /dev/null
+++ b/stable/_downloads/10fc813a8f78253f4bf301264950f5c1/plot_bcic_iv_4_ecog_cropped.ipynb
@@ -0,0 +1,338 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset\n\nThis tutorial shows you how to train and test deep learning models with\nBraindecode on ECoG BCI IV competition dataset 4 using cropped mode. For this\ndataset we will predict 5 regression targets corresponding to flexion of each finger.\nThe targets were recorded as a time series (each 25 Hz), so this tutorial is\nan example of time series target prediction.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Maciej Sliwowski <maciek.sliwowski@gmail.com>\n#          Mohammed Fattouh <mo.fattouh@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preparing the dataset\n\n### Loading\n\nFirst, we load the data. In this tutorial, we use the functionality of braindecode\nto load [BCI IV competition dataset 4](http://www.bbci.de/competition/iv/#dataset4)_.\nThe dataset is available as a part of ECoG library:\nhttps://searchworks.stanford.edu/view/zk881ps0522\n\nThe dataset contains ECoG signal and time series of 5 targets corresponding\nto each finger flexion. This is different than standard decoding setup for EEG with\nmultiple trials and usually one target per trial. Here, fingers flexions change in time\nand are recorded with sampling frequency equals to 25 Hz.\n\nIf this dataset is used please cite [1].\n\n[1] Miller, Kai J. \"A library of human electrocorticographic data and analyses.\n\"Nature human behaviour 3, no. 11 (2019): 1225-1235. https://doi.org/10.1038/s41562-019-0678-3\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import copy\n\nimport numpy as np\nimport sklearn\nfrom mne import set_log_level\n\nfrom braindecode.datasets import BCICompetitionIVDataset4\n\nsubject_id = 1\ndataset = BCICompetitionIVDataset4(subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and test\n\nWe can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select `train` dataset\nfor training and validation and `test` for final evaluation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = dataset.split('session')\ntrain_set = dataset['train']\ntest_set = dataset['test']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nNow we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Preprocessing steps are taken from a standard EEG processing pipeline.\n   The only change is the cutoff frequency of the filter. For a proper ECoG\n   decoding other preprocessing steps may be needed.</p></div>\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We select only first 30 seconds from the training dataset to limit time and memory\nto run this example. We split training dataset into train and validation (only 6 seconds).\nTo obtain full results whole datasets should be used.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "valid_set = preprocess(copy.deepcopy(train_set),\n                       [Preprocessor('crop', tmin=24, tmax=30)], n_jobs=-1)\npreprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)\npreprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In time series targets setup, targets variables are stored in mne.Raw object as channels\nof type `misc`. Thus those channels have to be selected for further processing. However,\nmany mne functions ignore `misc` channels and perform operations only on data channels\n(see https://mne.tools/stable/glossary.html#term-data-channels).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocessors = [\n    # TODO: ensure that misc is not removed\n    Preprocessor('pick_types', ecog=True, misc=True),\n    Preprocessor(lambda x: x / 1e6, picks='ecog'),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size, picks='ecog')\n]\n# Transform the data\npreprocess(train_set, preprocessors)\npreprocess(valid_set, preprocessors)\npreprocess(test_set, preprocessors)\n\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = train_set.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in train_set.datasets])\n# Extract target sampling frequency\ntarget_sfreq = train_set.datasets[0].raw.info['temp']['target_sfreq']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\nIn contrast to trialwise decoding, we first have to create the model\nbefore we can cut the dataset into windows. This is because we need to\nknow the receptive field of the network to know how large the window\nstride should be.\n\nWe first choose the compute/input window size that will be fed to the\nnetwork during training This has to be larger than the networks\nreceptive field size and can otherwise be chosen for computational\nefficiency (see explanations in the beginning of this tutorial). Here we\nchoose 1000 samples, which is 1 second for the 1000 Hz sampling rate.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "input_window_samples = 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 1\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0] - 5\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    final_conv_length=2,\n    add_log_softmax=False,\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()\n\nfrom braindecode.models import get_output_shape, to_dense_prediction_model\n\nto_dense_prediction_model(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To know the models\u2019 receptive field, we calculate the shape of model\noutput for a dummy input.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_fixed_length_windows\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\n\ntrain_set = create_fixed_length_windows(\n    train_set,\n    start_offset_samples=0,\n    stop_offset_samples=None,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    targets_from='channels',\n    last_target_only=False,\n    preload=False\n)\n\nvalid_set = create_fixed_length_windows(\n    valid_set,\n    start_offset_samples=0,\n    stop_offset_samples=None,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    targets_from='channels',\n    last_target_only=False,\n    preload=False\n)\n\ntest_set = create_fixed_length_windows(\n    test_set,\n    start_offset_samples=0,\n    stop_offset_samples=None,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    targets_from='channels',\n    last_target_only=False,\n    preload=False\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We select only the thumb's finger flexion to create one model per finger.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Methods to predict all 5 fingers flexion with the same model may be considered as well.\n   We encourage you to find your own way to use braindecode models to predict fingers flexions.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "train_set.target_transform = lambda x: x[0: 1]\nvalid_set.target_transform = lambda x: x[0: 1]\ntest_set.target_transform = lambda x: x[0: 1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\nIn difference to trialwise decoding, we now should supply\n``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the\ncriterion, as well as ``criterion__loss_function`` as the loss function\napplied to the meaned predictions.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n   have found to work well for EEG motor decoding, however we strongly\n   encourage you to perform your own hyperparameter optimization using\n   cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGRegressor\nfrom braindecode.training import CroppedTimeSeriesEpochScoring, TimeSeriesLoss\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 27  # only 27 examples in train set, otherwise set to 64\nn_epochs = 8\n\nregressor = EEGRegressor(\n    model,\n    cropped=True,\n    aggregate_predictions=False,\n    criterion=TimeSeriesLoss,\n    criterion__loss_function=torch.nn.functional.mse_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n        ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=True,\n                                                   name='r2_train')\n         ),\n        ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,\n                                                   lower_is_better=False,\n                                                   on_train=False,\n                                                   name='r2_valid')\n         )\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Model training for a specified number of epochs. ``y`` is None as it is already supplied\nin the dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "regressor.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Obtaining predictions and targets for the test, train, and validation dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def pad_and_select_predictions(preds, y):\n    preds = np.pad(preds,\n                   ((0, 0), (0, 0), (y.shape[2] - preds.shape[2], 0)),\n                   'constant',\n                   constant_values=0)\n\n    mask = ~np.isnan(y[0, 0, :])\n    preds = np.squeeze(preds[..., mask], 0)\n    y = np.squeeze(y[..., mask], 0)\n    return y.T, preds.T\n\n\npreds_train, y_train = regressor.predict_trials(train_set, return_targets=True)\npreds_train, y_train = pad_and_select_predictions(preds_train, y_train)\n\npreds_valid, y_valid = regressor.predict_trials(valid_set, return_targets=True)\npreds_valid, y_valid = pad_and_select_predictions(preds_valid, y_valid)\n\npreds_test, y_test = regressor.predict_trials(test_set, return_targets=True)\npreds_test, y_test = pad_and_select_predictions(preds_test, y_test)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We plot target and predicted finger flexion on training, validation, and test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time needed to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can compute correlation coefficients for each finger\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "corr_coeffs = []\nfor dim in range(y_test.shape[1]):\n    corr_coeffs.append(\n        np.corrcoef(preds_test[:, dim], y_test[:, dim])[0, 1]\n    )\nprint('Correlation coefficient for each dimension: ', np.round(corr_coeffs, 2))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we use the history stored by Skorch throughout training to plot\naccuracy and loss curves.\nExtract loss and accuracy values for plotting from history object\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results_columns = ['train_loss', 'valid_loss', 'r2_train', 'r2_valid']\ndf = pd.DataFrame(regressor.history[:, results_columns], columns=results_columns,\n                  index=regressor.history[:, 'epoch'])\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['r2_train', 'r2_valid']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"R2 score\", color='tab:red', fontsize=14)\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-',\n                      label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':',\n                      label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14, loc='center right')\nplt.tight_layout()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/1224801107d6c3b138edf8ee33de0561/plot_bcic_iv_2a_moabb_cropped.ipynb b/stable/_downloads/1224801107d6c3b138edf8ee33de0561/plot_bcic_iv_2a_moabb_cropped.ipynb
deleted file mode 100644
index e1efcc49..00000000
--- a/stable/_downloads/1224801107d6c3b138edf8ee33de0561/plot_bcic_iv_2a_moabb_cropped.ipynb
+++ /dev/null
@@ -1,234 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Cropped Decoding on BCIC IV 2a Dataset\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Building on the [Trialwise decoding\ntutorial](./plot_bcic_iv_2a_moabb_trial.html)_, we now do more\ndata-efficient cropped decoding!\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# In Braindecode, there are two supported configurations created for\n# training models: trialwise decoding and cropped decoding. We will\n# explain this visually by comparing trialwise to cropped decoding.\n#\n# .. image:: ../_static/trialwise_explanation.png\n# .. image:: ../_static/cropped_explanation.png\n#\n# On the left, you see trialwise decoding:\n#\n# 1. A complete trial is pushed through the network.\n# 2. The network produces a prediction.\n# 3. The prediction is compared to the target (label) for that trial to\n#    compute the loss.\n#\n# On the right, you see cropped decoding:\n#\n# 1. Instead of a complete trial, crops are pushed through the network.\n# 2. For computational efficiency, multiple neighbouring crops are pushed\n#    through the network simultaneously (these neighbouring crops are\n#    called compute windows)\n# 3. Therefore, the network produces multiple predictions (one per crop in\n#    the window)\n# 4. The individual crop predictions are averaged before computing the\n#    loss function\n#\n# .. note::\n#\n#     -  The network architecture implicitly defines the crop size (it is the\n#        receptive field size, i.e., the number of timesteps the network uses\n#        to make a single prediction)\n#     -  The window size is a user-defined hyperparameter, called\n#        ``input_window_samples`` in Braindecode. It mostly affects runtime\n#        (larger window sizes should be faster). As a rule of thumb, you can\n#        set it to two times the crop size.\n#     -  Crop size and window size together define how many predictions the\n#        network makes per window: ``#window \u2212 #crop + 1 = #predictions``\n#"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# .. note::\n#\n#     For cropped decoding, the above training setup is mathematically\n#     identical to sampling crops in your dataset, pushing them through the\n#     network and training directly on the individual crops. At the same time,\n#     the above training setup is much faster as it avoids redundant\n#     computations by using dilated convolutions, see our paper\n#     `Deep learning with convolutional neural networks for EEG decoding and visualization <https://arxiv.org/abs/1703.05051>`_. # noqa: E501\n#     However, the two setups are only mathematically identical in case (1)\n#     your network does not use any padding or only left padding and\n#     (2) your loss function leads\n#     to the same gradients when using the averaged output. The first is true\n#     for our shallow and deep ConvNet models and the second is true for the\n#     log-softmax outputs and negative log likelihood loss that is typically\n#     used for classification in PyTorch.\n#\n\n# Loading and preprocessing the dataset\n# -------------------------------------\n#\n# Loading and preprocessing stays the same as in the `Trialwise decoding\n# tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__.\n\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create model and compute windowing parameters\n\nIn contrast to trialwise decoding, we first have to create the model\nbefore we can cut the dataset into windows. This is because we need to\nknow the receptive field of the network to know how large the window\nstride should be.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# We first choose the compute/input window size that will be fed to the\n# network during training This has to be larger than the networks\n# receptive field size and can otherwise be chosen for computational\n# efficiency (see explanations in the beginning of this tutorial). Here we\n# choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.\n#\n\ninput_window_samples = 1000"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we create the model. To enable it to be used in cropped decoding\nefficiently, we manually set the length of the final convolution layer\nto some length that makes the receptive field of the ConvNet smaller\nthan ``input_window_samples`` (see ``final_conv_length=30`` in the model\ndefinition).\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans from dataset\nn_chans = dataset[0][0].shape[0]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=30,\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "And now we transform model with strides to a model that outputs dense\nprediction, so we can use it to obtain predictions for all\ncrops.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.models import to_dense_prediction_model, get_output_shape\n\nto_dense_prediction_model(model)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To know the models\u2019 receptive field, we calculate the shape of model\noutput for a dummy input.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Cut the data into windows\nIn contrast to trialwise decoding, we have to supply an explicit\nwindow size and window stride to the ``create_windows_from_events``\nfunction.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    preload=True\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Split the dataset\n\nThis code is the same as in trialwise decoding.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "splitted = windows_dataset.split('session')\ntrain_set = splitted['session_T']\nvalid_set = splitted['session_E']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Training\n --------\n In difference to trialwise decoding, we now should supply\n ``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the\n criterion, as well as ``criterion__loss_function`` as the loss function\n applied to the meaned predictions.\n\n#####################################################################\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# .. note::\n#\n#     In this tutorial, we use some default parameters that we\n#     have found to work well for motor decoding, however we strongly\n#     encourage you to perform your own hyperparameter optimization using\n#     cross validation on your training data.\n#\n\nfrom skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\nfrom braindecode.training import CroppedLoss\n\n# These values we found good for shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    cropped=True,\n    criterion=CroppedLoss,\n    criterion__loss_function=torch.nn.functional.nll_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\", (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Plot Results\nThis is again the same code as in trialwise decoding.\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n<div class=\"alert alert-info\"><h4>Note</h4><p>Note that we drop further in the classification error and\n    loss as in the trialwise decoding tutorial.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport pandas as pd\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nplt.style.use('seaborn')\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Plot Confusion Matrix\n\nGenerate a confusion matrix as in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.metrics import confusion_matrix\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = valid_set.datasets[0].windows.event_id.items()\n# sort the labels by values (values are integer class labels)\nlabels = list(dict(sorted(list(label_dict), key=lambda kv: kv[1])).keys())\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/176a7695926ccfe061692c4af12b673d/plot_dataset_example.ipynb b/stable/_downloads/176a7695926ccfe061692c4af12b673d/plot_dataset_example.ipynb
deleted file mode 100644
index 1ef3255e..00000000
--- a/stable/_downloads/176a7695926ccfe061692c4af12b673d/plot_dataset_example.ipynb
+++ /dev/null
@@ -1,252 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# MOABB Dataset Example\n\nIn this example, we show how to fetch and prepare a MOABB dataset for usage\nwith Braindecode.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#          Hubert Banville <hubert.jbanville@gmail.com>\n#          Simon Brandt <simonbrandt@protonmail.com>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nfrom braindecode.datasets import MOABBDataset\nfrom braindecode.preprocessing import \\\n    create_windows_from_events, create_fixed_length_windows\nfrom braindecode.preprocessing import preprocess, Preprocessor"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, we create a dataset based on BCIC IV 2a fetched with MOABB,\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "dataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[1])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "ds has a pandas DataFrame with additional description of its internal datasets\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "dataset.description"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can iterate through ds which yields one time point of a continuous signal x,\nand a target y (which can be None if targets are not defined for the entire\ncontinuous signal).\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "for x, y in dataset:\n    print(x.shape, y)\n    break"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can apply preprocessing transforms that are defined in mne and work\nin-place, such as resampling, bandpass filtering, or electrode selection.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=True),\n    Preprocessor('resample', sfreq=100)\n]\nprint(dataset.datasets[0].raw.info[\"sfreq\"])\npreprocess(dataset, preprocessors)\nprint(dataset.datasets[0].raw.info[\"sfreq\"])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can easily split ds based on a criteria applied to the description\nDataFrame:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "subsets = dataset.split(\"session\")\nprint({subset_name: len(subset) for subset_name, subset in subsets.items()})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we use a windower to extract events from the dataset based on events:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "windows_dataset = create_windows_from_events(\n    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=100,\n    window_size_samples=400, window_stride_samples=100,\n    drop_last_window=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can iterate through the windows_ds which yields a window x,\na target y, and window_ind (which itself contains ``i_window_in_trial``,\n``i_start_in_trial``, and ``i_stop_in_trial``, which are required for\ncombining window predictions in the scorer).\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "for x, y, window_ind in windows_dataset:\n    print(x.shape, y, window_ind)\n    break"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We visually inspect the windows:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "max_i = 2\nfig, ax_arr = plt.subplots(1, max_i + 1, figsize=(3.5 * (max_i + 1), 3.5),\n                           sharex=True, sharey=True)\nfor i, (x, y, window_ind) in enumerate(windows_dataset):\n    ax_arr[i].plot(x.T)\n    ax_arr[i].set_ylim(-4e-5, 4e-5)\n    ax_arr[i].set_title(f\"label={y}\")\n    if i == max_i:\n        break\n\nfig.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, we can create evenly spaced (\"sliding\") windows using a\ndifferent windower.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "sliding_windows_dataset = create_fixed_length_windows(\n    dataset, start_offset_samples=0, stop_offset_samples=0,\n    window_size_samples=1200, window_stride_samples=1000,\n    drop_last_window=False)\n\nprint(len(sliding_windows_dataset))\nfor x, y, window_ind in sliding_windows_dataset:\n    print(x.shape, y, window_ind)\n    break\n\nsliding_windows_dataset.description"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Transforms can also be applied on windows in the same way as shown\nabove on continuous data:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def crop_windows(windows, start_offset_samples, stop_offset_samples):\n    fs = windows.info[\"sfreq\"]\n    windows.crop(tmin=start_offset_samples / fs, tmax=stop_offset_samples / fs,\n                 include_tmax=False)\n\n\nepochs_preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),\n    Preprocessor(crop_windows, apply_on_array=False, start_offset_samples=100,\n                 stop_offset_samples=900)\n]\n\nprint(windows_dataset.datasets[0].windows.info[\"ch_names\"],\n      len(windows_dataset.datasets[0].windows.times))\npreprocess(windows_dataset, epochs_preprocessors)\nprint(windows_dataset.datasets[0].windows.info[\"ch_names\"],\n      len(windows_dataset.datasets[0].windows.times))\n\n\nmax_i = 2\nfig, ax_arr = plt.subplots(1, max_i + 1, figsize=(3.5 * (max_i + 1), 3.5),\n                           sharex=True, sharey=True)\nfor i, (x, y, window_ind) in enumerate(windows_dataset):\n    ax_arr[i].plot(x.T)\n    ax_arr[i].set_ylim(-4e-5, 4e-5)\n    ax_arr[i].set_title(f\"label={y}\")\n    if i == max_i:\n        break\n\nfig.tight_layout()\nplt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Again, we can easily split windows_ds based on some criteria in the\ndescription DataFrame:\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "subsets = windows_dataset.split(\"session\")\nprint({subset_name: len(subset) for subset_name, subset in subsets.items()})"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py b/stable/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py
new file mode 100644
index 00000000..5101398f
--- /dev/null
+++ b/stable/_downloads/193f3437bf871ff79a1b5b3f01396c26/plot_regression.py
@@ -0,0 +1,257 @@
+"""
+Convolutional neural network regression model on fake data.
+===========================================================
+
+This example shows how to create a CNN regressor from a CNN classifier by removing `softmax`
+function from the classifier's output layer and how to train it on a fake regression dataset.
+
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#          Sara Sedlar <sara.sedlar@gmail.com>
+# License: BSD-3
+
+###################################################################################################
+# Fake regression data
+# --------------------
+# Function for generation of the fake regression dataset generates `n_fake_recs` recordings,
+# each containing sinusoidal signals with Gaussian noise. Each fake recording signal has
+# `n_fake_chs` channels, it lasts `fake_duration` [s] and it is sampled with `fake_sfreq` [Hz].
+# The recordings are split into train, validation and testing sessions.
+
+import numpy as np
+import pandas as pd
+
+from braindecode.datasets import BaseConcatDataset, BaseDataset
+from braindecode.util import create_mne_dummy_raw
+
+
+###################################################################################################
+# Function for generating fake regression data
+def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq,
+                            fake_duration, n_fake_targets,
+                            fake_data_split=[0.6, 0.2, 0.2]):
+    """Generate a fake regression dataset.
+
+    Parameters
+    ----------
+    n_fake_recs : int
+        Number of fake recordings.
+    n_fake_chs : int
+        Number of fake EEG channels.
+    fake_sfreq : float
+        Fake sampling frequency in Hz.
+    fake_duration : float
+        Fake recording duration in seconds.
+    n_fake_targets : int
+        Number of targets.
+    fake_data_split : list
+        List of train/valid/test subset fractions.
+
+    Returns
+    -------
+    dataset : BaseConcatDataset object
+        The generated dataset object.
+    """
+
+    datasets = []
+    for i in range(n_fake_recs):
+        if i < int(fake_data_split[0] * n_fake_recs):
+            target_subset = "train"
+        elif i < int((1 - fake_data_split[2]) * n_fake_recs):
+            target_subset = "valid"
+        else:
+            target_subset = "test"
+        raw, _ = create_mne_dummy_raw(n_channels=n_fake_chs,
+                                      n_times=fake_duration * fake_sfreq,
+                                      sfreq=fake_sfreq)
+
+        target = np.random.randint(0, 10, n_fake_targets)
+        for j in range(n_fake_targets):
+            x = np.sin(2 * np.pi * target[j] * raw.times)
+            raw._data += np.expand_dims(x, axis=0)
+
+        if n_fake_targets == 1:
+            target = target[0]
+        fake_description = pd.Series(data=[target, target_subset],
+                                     index=["target", "session"])
+        datasets.append(
+            BaseDataset(raw, fake_description, target_name="target"))
+
+    return BaseConcatDataset(datasets)
+
+
+####################################################################################################
+# Generating fake regression dataset
+# -----------------------------------
+#
+#
+n_fake_rec = 20
+n_fake_chans = 21
+fake_sfreq = 100
+fake_duration = 30
+n_fake_targets = 1
+dataset = fake_regression_dataset(n_fake_recs=n_fake_rec,
+                                  n_fake_chs=n_fake_chans,
+                                  fake_sfreq=fake_sfreq,
+                                  fake_duration=fake_duration,
+                                  n_fake_targets=n_fake_targets)
+
+###################################################################################################
+# Defining a CNN regression model
+# -------------------------------
+#
+# Choosing and defining a CNN classifier, `ShallowFBCSPNet` or `Deep4Net`, introduced in [1]_.
+# To convert a classifier to a regressor, `softmax` function is removed from its output layer.
+from braindecode.util import set_random_seeds
+from braindecode.models import Deep4Net
+from braindecode.models import ShallowFBCSPNet
+import torch
+
+# Choosing a CNN model
+model_name = "shallow"  # 'shallow' or 'deep'
+
+# Defining a CNN model
+if model_name in ["shallow", "Shallow", "ShallowConvNet"]:
+    model = ShallowFBCSPNet(in_chans=n_fake_chans,
+                            n_classes=n_fake_targets,
+                            input_window_samples=fake_sfreq * fake_duration,
+                            n_filters_time=40, n_filters_spat=40,
+                            final_conv_length=35,
+                            add_log_softmax=False,)
+elif model_name in ["deep", "Deep", "DeepConvNet"]:
+    model = Deep4Net(in_chans=n_fake_chans, n_classes=n_fake_targets,
+                     input_window_samples=fake_sfreq * fake_duration,
+                     n_filters_time=25, n_filters_spat=25,
+                     stride_before_pool=True,
+                     n_filters_2=n_fake_chans * 2,
+                     n_filters_3=n_fake_chans * 4,
+                     n_filters_4=n_fake_chans * 8,
+                     final_conv_length=1,
+                     add_log_softmax=False, )
+else:
+    raise ValueError(f'{model_name} unknown')
+
+###################################################################################################
+# Choosing between GPU and CPU processors
+# ---------------------------------------
+# By default, model's training and evaluation take place at GPU if it exists, otherwise on CPU.
+cuda = torch.cuda.is_available()
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+
+# Setting a random seed
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+if cuda:
+    model.cuda()
+
+###################################################################################################
+# Data windowing
+# ----------------
+# Windowing data with a sliding window into the epochs of the size `window_size_samples`.
+from braindecode.models.util import to_dense_prediction_model, get_output_shape
+from braindecode.preprocessing import create_fixed_length_windows
+
+window_size_samples = fake_sfreq * fake_duration // 3
+to_dense_prediction_model(model)
+n_preds_per_input = get_output_shape(model, n_fake_chans, window_size_samples)[
+    2]
+windows_dataset = create_fixed_length_windows(dataset,
+                                              start_offset_samples=0,
+                                              stop_offset_samples=0,
+                                              window_size_samples=window_size_samples,
+                                              window_stride_samples=n_preds_per_input,
+                                              drop_last_window=False,
+                                              preload=True)
+
+# Splitting windowed data into train, valid and test subsets.
+splits = windows_dataset.split("session")
+train_set = splits["train"]
+valid_set = splits["valid"]
+test_set = splits["test"]
+
+###################################################################################################
+# Model training
+# -----------------
+# Model is trained by minimizing MSE loss between ground truth and estimated value averaged over
+# a period of time using AdamW optimizer [2]_, [3]_. Learning rate is managed by CosineAnnealingLR
+# learning rate scheduler.
+from braindecode import EEGRegressor
+from braindecode.training.losses import CroppedLoss
+from skorch.callbacks import LRScheduler
+from skorch.helper import predefined_split
+
+batch_size = 4
+n_epochs = 3
+optimizer_lr = 0.001
+optimizer_weight_decay = 0.0
+regressor = EEGRegressor(model, cropped=True,
+                         criterion=CroppedLoss,
+                         criterion__loss_function=torch.nn.functional.mse_loss,
+                         optimizer=torch.optim.AdamW,
+                         optimizer__lr=optimizer_lr,
+                         optimizer__weight_decay=optimizer_weight_decay,
+                         train_split=predefined_split(valid_set),
+                         iterator_train__shuffle=True,
+                         batch_size=batch_size,
+                         callbacks=["neg_root_mean_squared_error",
+                                    ("lr_scheduler",
+                                     LRScheduler('CosineAnnealingLR',
+                                                 T_max=n_epochs - 1))],
+                         device=device, )
+regressor.fit(train_set, y=None, epochs=n_epochs)
+
+###################################################################################################
+# Model evaluation
+# -----------------
+# Plotting training and validation losses and negative root mean square error
+import matplotlib.pyplot as plt
+
+
+fig, axes = plt.subplots(1, 2, figsize=(10, 5))
+axes[0].set_title("Train and valid losses")
+axes[0].plot(regressor.history[:, "train_loss"])
+axes[0].plot(regressor.history[:, "valid_loss"])
+axes[0].set_xlabel("Epochs")
+axes[0].set_ylabel("Cropped MSE loss")
+axes[0].legend(["Train", "Valid"])
+
+axes[1].set_title("Train and valid errors")
+axes[1].plot(regressor.history[:, "train_neg_root_mean_squared_error"])
+axes[1].plot(regressor.history[:, "valid_neg_root_mean_squared_error"])
+axes[1].set_xlabel("Epochs")
+axes[1].set_ylabel("Negative RMSE")
+axes[1].legend(["Train", "Valid"])
+
+###################################################################################################
+# Model testing
+# -----------------
+# Plotting a scatter plot of estimated versus target values and corresponding trend line.
+fig, axes = plt.subplots(1, 1, figsize=(5, 5))
+y_estim = np.ravel(regressor.predict(test_set))
+y_gt = test_set.get_metadata()["target"].to_numpy()
+
+_ = axes.scatter(y_gt, y_estim)
+_ = axes.set_ylabel("Estimated targets.")
+_ = axes.set_xlabel("Ground truth targets.")
+
+z = np.polyfit(y_gt, y_estim, 1)
+p = np.poly1d(z)
+plt.plot(y_gt, p(y_gt), "r--")
+plt.show()
+###################################################################################################
+# References
+# ----------
+#
+# .. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Human brain mapping, 38(11), 5391-5420.
+#
+# .. [2] Kingma, Diederik P., and Jimmy Ba.
+#        "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
+#
+# .. [3] Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.
+#        "On the convergence of adam and beyond." arXiv preprint arXiv:1904.09237 (2019).
diff --git a/stable/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb b/stable/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb
new file mode 100644
index 00000000..10dc1da7
--- /dev/null
+++ b/stable/_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb
@@ -0,0 +1,104 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Benchmarking preprocessing with parallelization and serialization\n\nIn this example, we compare the execution time and memory requirements of\npreprocessing data with the parallelization and serialization functionalities\navailable in :func:`braindecode.preprocessing.preprocess`.\n\nWe compare 4 cases:\n\n1. Sequential, no serialization\n2. Sequential, with serialization\n3. Parallel, no serialization\n4. Parallel, with serialization\n\nCase 1 is the simplest approach, in which all recordings in a\n:class:`braindecode.datasets.BaseConcatDataset` are preprocessed one after the\nother. In this scenario, :func:`braindecode.preprocessing.preprocess` acts\ninplace, which means memory usage will likely stay stable (depending on the\npreprocessing operations) if recordings have been preloaded. However, two\npotential issues arise when working with large datasets: (1) if recordings have\nnot been preloaded before preprocessing, `preprocess()` will need to load them\nand keep them in memory, in which case memory can become a bottleneck, and (2)\nsequential preprocessing can take a considerable amount of time to run when\nworking with many recordings.\n\nA solution to the first issue (memory usage) is to save the preprocessed data\nto a file so it can be cleared from memory before moving on to the next\nrecording (case 2). The recordings can then be reloaded with `preload=False`\nonce they have all been saved to disk. This enables using the lazy loading\ncapabilities of :class:`braindecode.datasets.BaseConcatDataset` and avoids\npotential memory bottlenecks. The downside is that the writing to disk can take\nsome time and of course requires disk space.\n\nA solution to the second issue (slow preprocessing) is to parallelize the\npreprocessing over multiple cores whenever possible (case 3). This can speed up\npreprocessing significantly. However, this approach will increase memory usage\nbecause of the way parallelization is implemented internally (with\n`joblib`, copies of (part of) the data must be made when sending arguments to\nparallel processes).\n\nFinally, case 4 (combining parallelization and serialization) is likely to be\nboth fast and memory efficient. As shown in this example, this remains a\ntradeoff though, and the selected configuration should depend on the size of\nthe dataset and the specific operations applied to the recordings.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\n\nimport time\nimport tempfile\nfrom itertools import product\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import scale\nfrom memory_profiler import memory_usage\n\nfrom braindecode.datasets import SleepPhysionet\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We create a function that goes through the usual three steps of data\npreparation: (1) data loading, (2) continuous data preprocessing,\n(3) windowing and (4) windowed data preprocessing. We use the\n:class:`braindecode.datasets.SleepPhysionet` dataset for testing purposes.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def prepare_data(n_recs, save, preload, n_jobs):\n    if save:\n        tmp_dir = tempfile.TemporaryDirectory()\n        save_dir = tmp_dir.name\n    else:\n        save_dir = None\n\n    # (1) Load the data\n    concat_ds = SleepPhysionet(\n        subject_ids=range(n_recs), recording_ids=[1], crop_wake_mins=30,\n        preload=preload)\n    sfreq = concat_ds.datasets[0].raw.info['sfreq']\n\n    # (2) Preprocess the continuous data\n    preprocessors = [\n        Preprocessor('crop', tmin=10),\n        Preprocessor('filter', l_freq=None, h_freq=30)\n    ]\n    preprocess(concat_ds, preprocessors, save_dir=save_dir, overwrite=True,\n               n_jobs=n_jobs)\n\n    # (3) Window the data\n    windows_ds = create_fixed_length_windows(\n        concat_ds, 0, None, int(30 * sfreq), int(30 * sfreq), True,\n        preload=preload, n_jobs=n_jobs)\n\n    # Preprocess the windowed data\n    preprocessors = [Preprocessor(scale, channel_wise=True)]\n    preprocess(windows_ds, preprocessors, save_dir=save_dir, overwrite=True,\n               n_jobs=n_jobs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we can run our function and measure its run time and peak memory usage\nfor each one of our 4 cases above. We call the function multiple times with\neach configuration to get better estimates.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To better characterize the run time vs. memory usage tradeoff for your\n  specific configuration (as this will differ based on available hardware,\n  data size and preprocessing operations), we recommend adapting this example\n  to your use case and running it on your machine.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_repets = 2  # Number of repetitions\nall_n_recs = 2  # Number of recordings to load and preprocess\nall_n_jobs = [1, 2]  # Number of parallel processes\n\nresults = list()\nfor _, n_recs, save, n_jobs in product(\n        range(n_repets), [all_n_recs], [True, False], all_n_jobs):\n\n    start = time.time()\n    mem = max(memory_usage(\n        proc=(prepare_data, [n_recs, save, False, n_jobs], {})))\n    time_taken = time.time() - start\n\n    results.append({\n        'n_recs': n_recs,\n        'max_mem': mem,\n        'save': save,\n        'n_jobs': n_jobs,\n        'time': time_taken\n    })"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can plot the results:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.DataFrame(results)\n\nfig, ax = plt.subplots(figsize=(6, 4))\ncolors = {True: 'tab:orange', False: 'tab:blue'}\nmarkers = {n: m for n, m in zip(all_n_jobs, ['o', 'x', '.'])}\nfor (save, n_jobs), sub_df in df.groupby(['save', 'n_jobs']):\n    ax.scatter(x=sub_df['time'], y=sub_df['max_mem'], color=colors[save],\n               marker=markers[n_jobs], label=f'save={save}, n_jobs={n_jobs}')\nax.legend()\nax.set_xlabel('Execution time (s)')\nax.set_ylabel('Memory usage (MiB)')\nax.set_title(f'Loading and preprocessing {all_n_recs} recordings from Sleep '\n             'Physionet')\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We see that parallel preprocessing without serialization (blue crosses) is\nfaster than simple sequential processing (blue circles), however it uses more\nmemory.\n\nCombining parallel preprocessing and serialization (orange crosses) reduces\nmemory usage significantly, however it increases run time by a few seconds.\nDepending on available resources (e.g. in limited memory settings), it might\ntherefore be more advantageous to use both parallelization and serialization\ntogether.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb b/stable/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb
new file mode 100644
index 00000000..123ba2ab
--- /dev/null
+++ b/stable/_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb
@@ -0,0 +1,259 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Data Augmentation on BCIC IV 2a Dataset\n\nThis tutorial shows how to train EEG deep models with data augmentation. It\nfollows the trial-wise decoding example and also illustrates the effect of a\ntransform on the input signals.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Simon Brandt <simonbrandt@protonmail.com>\n#          C\u00e9dric Rommel <cedric.rommel@inria.fr>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extracting windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to\n# define how trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Defining a Transform\n\nData can be manipulated by transforms, which are callable objects. A\ntransform is usually handled by a custom data loader, but can also be called\ndirectly on input data, as demonstrated below for illutrative purposes.\n\nFirst, we need to define a Transform. Here we chose the FrequencyShift, which\nrandomly translates all frequencies within a given range.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.augmentation import FrequencyShift\n\ntransform = FrequencyShift(\n    probability=1.,  # defines the probability of actually modifying the input\n    sfreq=sfreq,\n    max_delta_freq=2.  # the frequency shifts are sampled now between -2 and 2 Hz\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Manipulating one session and visualizing the transformed data\n\n\nNext, let us augment one session to show the resulting frequency shift. The\ndata of an mne Epoch is used here to make usage of mne functions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport numpy as np\n\nX = np.stack([X for X, y, i in train_set.datasets[0]])\n# This allows to apply the transform with a fixed shift (10 Hz) for\n# visualization instead of sampling the shift randomly between -2 and 2 Hz\nX_tr, _ = transform.operation(torch.as_tensor(X).float(), None, 10., sfreq)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The psd of the transformed session has now been shifted by 10 Hz, as one can\nsee on the psd plot.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import mne\nimport matplotlib.pyplot as plt\n\n\ndef plot_psd(data, axis, label, color):\n    psds, freqs = mne.time_frequency.psd_array_multitaper(data, sfreq=sfreq,\n                                                          fmin=0.1, fmax=100)\n    psds = 10. * np.log10(psds)\n    psds_mean = psds.mean(0).mean(0)\n    axis.plot(freqs, psds_mean, color=color, label=label)\n\n\n_, ax = plt.subplots()\nplot_psd(X, ax, 'original', 'k')\nplot_psd(X_tr.numpy(), ax, 'shifted', 'r')\n\nax.set(title='Multitaper PSD (gradiometers)', xlabel='Frequency (Hz)',\n       ylabel='Power Spectral Density (dB)')\nax.legend()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training a model with data augmentation\n\nNow that we know how to instantiate ``Transforms``, it is time to learn how\nto use them to train a model and try to improve its generalization power.\nLet's first create a model.\n\n### Create model\n\nThe model to be trained is defined as usual.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an EEGClassifier with the desired augmentation\n\nIn order to train with data augmentation, a custom data loader can be\nfor the training. Multiple transforms can be passed to it and will be applied\nsequentially to the batched data within the ``AugmentedDataLoader`` object.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.augmentation import AugmentedDataLoader, SignFlip\n\nfreq_shift = FrequencyShift(\n    probability=.5,\n    sfreq=sfreq,\n    max_delta_freq=2.  # the frequency shifts are sampled now between -2 and 2 Hz\n)\n\nsign_flip = SignFlip(probability=.1)\n\ntransforms = [\n    freq_shift,\n    sign_flip\n]\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model is now trained as in the trial-wise example. The\n``AugmentedDataLoader`` is used as the train iterator and the list of\ntransforms are passed as arguments.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=transforms,  # This sets the augmentations to use\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Manually composing Transforms\n\nIt would be equivalent (although more verbose) to pass to ``EEGClassifier`` a\ncomposition of the same transforms:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.augmentation import Compose\n\ncomposed_transforms = Compose(transforms=transforms)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Setting the data augmentation at the Dataset level\n\nAlso note that it is also possible for most of the transforms to pass them\ndirectly to the WindowsDataset object through the `transform` argument, as\nmost commonly done in other libraries. However, it is advised to use the\n``AugmentedDataLoader`` as above, as it is compatible with all transforms and\ncan be more efficient.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "train_set.transform = composed_transforms"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py b/stable/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py
new file mode 100644
index 00000000..800e73d9
--- /dev/null
+++ b/stable/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py
@@ -0,0 +1,362 @@
+"""
+Sleep staging on the Sleep Physionet dataset using Eldele2021
+=============================================================
+
+This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We use the attention-based model from [1]_ with the time distributed approach of [2]_
+to learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset [3]_ [4]_.
+
+"""
+# Authors: Divyesh Narayanan <divyesh.narayanan@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading
+# ~~~~~~~
+#
+# First, we load the data using the
+# :class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load
+# two recordings from two different individuals: we will use the first one to
+# train our network and the second one to evaluate performance (as in the `MNE`_
+# sleep staging example).
+#
+# .. _MNE: https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html
+#
+
+from numbers import Integral
+from braindecode.datasets import SleepPhysionet
+
+subject_ids = [0, 1]
+crop = (0, 30 * 400)  # we only keep 400 windows of 30s to speed example
+dataset = SleepPhysionet(
+    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30,
+    crop=crop)
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# Next, we preprocess the raw data. We convert the data to microvolts and apply
+# a lowpass filter.
+
+from braindecode.preprocessing import preprocess, Preprocessor
+from numpy import multiply
+
+high_cut_hz = 30
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
+    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
+]
+
+# Transform the data
+preprocess(dataset, preprocessors)
+
+######################################################################
+# Extract windows
+# ~~~~~~~~~~~~~~~
+#
+# We extract 30-s windows to be used in the classification task.
+# The Eldele2021 model takes a single channel as input. Here, the Fpz-Cz channel is used as it
+# was found to give better performance than using the Pz-Oz channel
+
+from braindecode.preprocessing import create_windows_from_events
+
+mapping = {  # We merge stages 3 and 4 following AASM standards.
+    'Sleep stage W': 0,
+    'Sleep stage 1': 1,
+    'Sleep stage 2': 2,
+    'Sleep stage 3': 3,
+    'Sleep stage 4': 3,
+    'Sleep stage R': 4
+}
+
+window_size_s = 30
+sfreq = 100
+window_size_samples = window_size_s * sfreq
+
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+    window_size_samples=window_size_samples,
+    window_stride_samples=window_size_samples,
+    picks="Fpz-Cz",  # the other option is Pz-Oz,
+    preload=True,
+    mapping=mapping
+)
+
+######################################################################
+# Window preprocessing
+# ~~~~~~~~~~~~~~~~~~~~
+#
+# We also preprocess the windows by applying channel-wise z-score normalization
+# in each window.
+
+from sklearn.preprocessing import scale as standard_scale
+
+preprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We split the dataset into training and validation set taking
+# every other subject as train or valid.
+
+split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])
+splits = windows_dataset.split(split_ids)
+train_set, valid_set = splits["train"], splits["valid"]
+
+######################################################################
+# Create sequence samplers
+# ------------------------
+#
+# Following the time distributed approach of [2]_, we need to provide our
+# neural network with sequences of windows, such that the embeddings of
+# multiple consecutive windows can be concatenated and provided to a final
+# classifier. We can achieve this by defining Sampler objects that return
+# sequences of window indices.
+# To simplify the example, we train the whole model end-to-end on sequences,
+# rather than using the two-step approach of [2]_ (i.e. training the feature
+# extractor on single windows, then freezing its weights and training the
+# classifier).
+#
+
+from braindecode.samplers import SequenceSampler
+
+n_windows = 3  # Sequences of 3 consecutive windows
+n_windows_stride = 3  # Maximally overlapping sequences
+
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
+valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
+
+# Print number of examples per class
+print('Training examples: ', len(train_sampler))
+print('Validation examples: ', len(valid_sampler))
+
+######################################################################
+# We also implement a transform to extract the label of the center window of a
+# sequence to use it as target.
+
+import numpy as np
+
+
+# Use label of center window in the sequence
+def get_center_label(x):
+    if isinstance(x, Integral):
+        return x
+    return x[np.ceil(len(x) / 2).astype(int)] if len(x) > 1 else x
+
+
+train_set.target_transform = get_center_label
+valid_set.target_transform = get_center_label
+
+######################################################################
+# Finally, since some sleep stages appear a lot more often than others (e.g.
+# most of the night is spent in the N2 stage), the classes are imbalanced. To
+# avoid overfitting on the more frequent classes, we compute weights that we
+# will provide to the loss function when training.
+
+from sklearn.utils import compute_class_weight
+
+y_train = [train_set[idx][1] for idx in train_sampler]
+class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
+
+######################################################################
+# Create model
+# ------------
+#
+# We can now create the deep learning model. In this tutorial, we use the sleep
+# staging architecture introduced in [1]_, which is an attention-based
+# neural network. We use the time distributed version of the model, where the
+# feature vectors of a sequence of windows are concatenated and passed to a
+# linear layer for classification.
+#
+
+import torch
+from torch import nn
+from braindecode.util import set_random_seeds
+from braindecode.models import SleepStagerEldele2021, TimeDistributed
+
+cuda = torch.cuda.is_available()  # check if GPU is available
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to reproduce results
+set_random_seeds(seed=31, cuda=cuda)
+
+n_classes = 5
+# Extract number of channels and time steps from dataset
+n_channels, input_size_samples = train_set[0][0].shape
+
+feat_extractor = SleepStagerEldele2021(
+    sfreq,
+    n_outputs=n_classes,
+    n_times=input_size_samples,
+    return_feats=True,
+)
+
+model = nn.Sequential(
+    TimeDistributed(feat_extractor),  # apply model on each 30-s window
+    nn.Sequential(  # apply linear layer on concatenated feature vectors
+        nn.Flatten(start_dim=1),
+        nn.Dropout(0.5),
+        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)
+    )
+)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# Training
+# --------
+#
+# We can now train our network. :class:`braindecode.EEGClassifier` is a
+# braindecode object that is responsible for managing the training of neural
+# networks. It inherits from :class:`skorch.NeuralNetClassifier`, so the
+# training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+
+from skorch.helper import predefined_split
+from skorch.callbacks import EpochScoring
+from braindecode import EEGClassifier
+
+lr = 1e-3
+batch_size = 32
+n_epochs = 3  # we use few epochs for speed and but more than one for plotting
+
+train_bal_acc = EpochScoring(
+    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',
+    lower_is_better=False)
+valid_bal_acc = EpochScoring(
+    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',
+    lower_is_better=False)
+callbacks = [
+    ('train_bal_acc', train_bal_acc),
+    ('valid_bal_acc', valid_bal_acc)
+]
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.CrossEntropyLoss,
+    criterion__weight=torch.Tensor(class_weights).to(device),
+    optimizer=torch.optim.Adam,
+    iterator_train__shuffle=False,
+    iterator_train__sampler=train_sampler,
+    iterator_valid__sampler=valid_sampler,
+    train_split=predefined_split(valid_set),  # using valid_set for validation
+    optimizer__lr=lr,
+    batch_size=batch_size,
+    callbacks=callbacks,
+    device=device,
+    classes=np.unique(y_train)
+)
+# Model training for a specified number of epochs. `y` is None as it is already
+# supplied in the dataset.
+clf.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Plot results
+# ------------
+#
+# We use the history stored by Skorch during training to plot the performance of
+# the model throughout training. Specifically, we plot the loss and the balanced
+# balanced accuracy for the training and validation sets.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Extract loss and balanced accuracy values for plotting from history object
+df = pd.DataFrame(clf.history.to_list())
+df.index.name = "Epoch"
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)
+df[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)
+df[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)
+ax1.set_ylabel('Loss')
+ax2.set_ylabel('Balanced accuracy')
+ax1.legend(['Train', 'Valid'])
+ax2.legend(['Train', 'Valid'])
+fig.tight_layout()
+plt.show()
+
+######################################################################
+# Finally, we also display the confusion matrix and classification report:
+#
+
+from sklearn.metrics import confusion_matrix, classification_report
+from braindecode.visualization import plot_confusion_matrix
+
+y_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]
+y_pred = clf.predict(valid_set)
+
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+plot_confusion_matrix(confusion_mat=confusion_mat)
+#                      class_names=['Wake', 'N1', 'N2', 'N3', 'N4', 'REM'])
+
+print(classification_report(y_true, y_pred))
+
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true, color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
+
+######################################################################
+# The model was able to learn despite the low amount of data that was available
+# (only two recordings in this example) and reached a balanced accuracy of
+# about 43% in a 5-class classification task (chance-level = 20%) on held-out
+# data over 10 epochs.
+#
+# .. note::
+#    To further improve performance, the number of epochs should be increased.
+#    It has been reduced here for faster run-time in document generation. In
+#    testing, 10 epochs provided reasonable performance with around 89% balanced
+#    accuracy on training data and around 43% on held out validation data.
+#    Increasing the number of training recordings and optimizing the hyperparameters
+#    will also help increase performance
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage
+#         Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and
+#         Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
+#
+# .. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
+#       Classification Using Multivariate and Multimodal Time Series.
+#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+#       (758-769)
+#
+# .. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb b/stable/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb
new file mode 100644
index 00000000..0f5d006d
--- /dev/null
+++ b/stable/_downloads/2466f8ec5c733d0bd65e187b45d875cc/plot_data_augmentation_search.ipynb
@@ -0,0 +1,302 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Searching the best data augmentation on BCIC IV 2a Dataset\n\nThis tutorial shows how to search data augmentations using braindecode.\nIndeed, it is known that the best augmentation to use often dependent on the task\nor phenomenon studied. Here we follow the methodology proposed in [1]_ on the\nopenly available BCI IV 2a Dataset.\n\n\n.. topic:: Data Augmentation\n\n    Data augmentation could be a step in training deep learning models.\n    For decoding brain signals, recent studies have shown that artificially\n    generating samples may increase the final performance of a deep learning model [1]_.\n    Other studies have shown that data augmentation can be used to cast\n    a self-supervised paradigm, presenting a more diverse\n    view of the data, both with pretext tasks and contrastive learning [2]_.\n\n\nData augmentation and self-supervised learning approaches demand an intense comparison\nto find the best fit with the data. This view is demonstrated in [1]_ and shows the\nimportance of selecting the right transformation and strength for different type of\ntask considered. Here, we use the augmentation module present in braindecode in\nthe context of trialwise decoding with the BCI IV 2a dataset.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Bruno Aristimunha <a.bruno@ufabc.edu.br>\n#          C\u00e9dric Rommel <cedric.rommel@inria.fr>\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading\n\nFirst, we load the data. In this tutorial, we use the functionality of braindecode\nto load BCI IV competition dataset 1. The dataset is available on the BNCI website.\nThere is 9 subjects recorded with 22 electrodes while doing a motor imagery task,\nwith 144 trials per class. We will load this dataset through the MOABB library.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nfrom braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nWe apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related\nbrain activity\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In time series targets setup, targets variables are stored in mne.Raw object as channels\nof type `misc`. Thus those channels have to be selected for further processing. However,\nmany mne functions ignore `misc` channels and perform operations only on data channels\n(see https://mne.tools/stable/glossary.html#term-data-channels).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extracting windows\n\nNow we cut out compute windows, the inputs for the deep networks during\ntraining. We use the braindecode function for this, provinding parameters\nto define how trials should be used.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\nfrom skorch.helper import SliceDataset\nfrom numpy import array\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\nFollowing the split defined in the BCI competition\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\neval_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Defining a list of transforms\n\nIn this tutorial, we will use three categories of augmentations.\nThis categorization has been proposed by [1]_ to explain and aggregate\nthe several possibilities of augmentations in EEG, being them:\n\na) Frequency domain augmentations,\nb) Time domain augmentations,\nc) Spatial domain augmentations.\n\nFrom this same paper, we selected the best augmentations in each type: ``FTSurrogate``,\n``SmoothTimeMask``, ``ChannelsDropout``, respectively.\n\nFor each augmentation, we adjustable two values from a range for one parameter\ninside the transformation.\n\nIt is important to remember that you can increase the range.\nFor that, we need to define three lists of transformations and range for the parameter\n\u2206\u03c6max in FTSurrogate where \u2206\u03c6max \u2208 [0, 2\u03c0); for \u2206t in SmoothTimeMask is \u2206t \u2208 [0, 2];\nFor the method ChannelsDropout, we analyse the parameter p_drop \u2208 [0, 1].\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from numpy import linspace\nfrom braindecode.augmentation import FTSurrogate, SmoothTimeMask, ChannelsDropout\n\nseed = 20200220\n\ntransforms_freq = [FTSurrogate(probability=0.5, phase_noise_magnitude=phase_freq,\n                               random_state=seed) for phase_freq in linspace(0, 1, 2)]\n\ntransforms_time = [SmoothTimeMask(probability=0.5, mask_len_samples=int(sfreq * second),\n                                  random_state=seed) for second in linspace(0.1, 2, 2)]\n\ntransforms_spatial = [ChannelsDropout(probability=0.5, p_drop=prob,\n                                      random_state=seed) for prob in linspace(0, 1, 2)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training a model with data augmentation\n\nNow that we know how to instantiate three list of ``Transforms``, it is time to learn how\nto use them to train a model and try to search the best for the dataset.\nLet's first create a model for search a parameter.\n\n### Create model\n\nThe model to be trained is defined as usual.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Set random seed to be able to roughly reproduce results\nNote that with cudnn benchmark set to True, GPU indeterminism\nmay still make results substantially different between runs.\nTo obtain more consistent results at the cost of increased computation time,\nyou can set ``cudnn_benchmark=False`` in ``set_random_seeds``\nor remove ``torch.backends.cudnn.benchmark = True``\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "seed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create an EEGClassifier with the desired augmentation\n\nIn order to train with data augmentation, a custom data loader can be\nfor the training. Multiple transforms can be passed to it and will be applied\nsequentially to the batched data within the ``AugmentedDataLoader`` object.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.augmentation import AugmentedDataLoader\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model is now trained as in the trial-wise example. The\n``AugmentedDataLoader`` is used as the train iterator and the list of\ntransforms are passed as arguments.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "lr = 0.0625 * 0.01\nweight_decay = 0\n\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader\n    iterator_train__transforms=[],  # This sets is handled by GridSearchCV\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,  # GridSearchCV will control the split and train/validation over the dataset\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'accuracy',\n        ('lr_scheduler', LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To use the skorch framework, it is necessary to transform the windows\ndataset using the module SliceData. Also, it is mandatory to eval the\ngenerator of the training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "train_X = SliceDataset(train_set, idx=0)\ntrain_y = array(list(SliceDataset(train_set, idx=1)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Given the trialwise approach, here we use the KFold approach and\nGridSearchCV.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import KFold, GridSearchCV\n\ncv = KFold(n_splits=2, shuffle=True, random_state=seed)\nfit_params = {'epochs': n_epochs}\n\ntransforms = transforms_freq + transforms_time + transforms_spatial\n\nparam_grid = {\n    'iterator_train__transforms': transforms,\n}\n\nclf.verbose = 0\n\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=cv,\n    return_train_score=True,\n    scoring='accuracy',\n    refit=True,\n    verbose=1,\n    error_score='raise')\n\nsearch.fit(train_X, train_y, **fit_params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Analysing the best fit\n\nNext, just perform an analysis of the best fit, and the parameters,\nremembering the order that was adjusted.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\nimport numpy as np\n\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results['rank_test_score'] == 1].squeeze()\nbest_aug = best_run['params']\nvalidation_score = np.around(best_run['mean_test_score'] * 100, 2).mean()\ntraining_score = np.around(best_run['mean_train_score'] * 100, 2).mean()\n\nreport_message = 'Best augmentation is saved in best_aug which gave a mean validation accuracy' + \\\n                 'of {}% (train accuracy of {}%).'.format(validation_score, training_score)\n\nprint(report_message)\n\neval_X = SliceDataset(eval_set, idx=0)\neval_y = SliceDataset(eval_set, idx=1)\nscore = search.score(eval_X, eval_y)\nprint(f'Eval accuracy is {score * 100:.2f}%.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Plot results\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots()\nsearch_results.plot.bar(\n    x=\"param_iterator_train__transforms\", y=\"mean_train_score\", yerr=\"std_train_score\",\n    rot=45, color=[\"C0\", \"C0\", \"C1\", \"C1\", \"C2\", \"C2\"], legend=None, ax=ax)\nax.set_xlabel(\"Data augmentation strategy\")\nax.set_ylim(0.2, 0.32)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### References\n\n\n.. [1] Rommel, C., Paillard, J., Moreau, T., & Gramfort, A. (2022)\n       Data augmentation for learning predictive models on EEG:\n       a systematic comparison. https://arxiv.org/abs/2206.14483\n.. [2] Banville, H., Chehab, O., Hyv\u00e4rinen, A., Engemann, D. A., & Gramfort, A. (2021).\n       Uncovering the structure of clinical EEG signals with self-supervised learning.\n       Journal of Neural Engineering, 18(4), 046020.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb b/stable/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb
new file mode 100644
index 00000000..4b233948
--- /dev/null
+++ b/stable/_downloads/263464a28477cf8decb861ae6e2e9be7/plot_how_train_test_and_tune.ipynb
@@ -0,0 +1,375 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# How to train, test and tune your model?\n\nThis tutorial shows you how to properly train, tune and test your deep learning\nmodels with Braindecode. We will use the BCIC IV 2a dataset [1]_ as a showcase example.\n\nThe methods shown can be applied to any standard supervised trial-based decoding setting.\nThis tutorial will include additional parts of code like loading and preprocessing of data,\ndefining a model, and other details which are not exclusive to this page (see\n[Cropped Decoding Tutorial](./plot_bcic_iv_2a_moabb_cropped.html)_). Therefore we\nwill not further elaborate on these parts and you can feel free to skip them.\n\nIn general, we distinguish between \"usual\" training and evaluation and hyperparameter search.\nThe tutorial is therefore split into two parts, one for the three different training schemes\nand one for the two different hyperparameter tuning methods.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Why should I care about model evaluation?\nShort answer: To produce reliable results!\n\nIn machine learning, we usually follow the scheme of splitting the\ndata into two parts, training and testing sets. It sounds like a\nsimple division, right? But the story does not end here.\n\n- What are model's parameters?\n\nModel's parameters are learnable weights which are used in the\nextraction of the relevant features and in performing the final inference.\nIn the context of deep learning, these are usually fully connected weights,\nconvolutional kernels, biases, etc.\n\n- What are model's hyperparameters?\n\nModel's hyperparameters are used to set the capacity (size) of the model\nand to guide the parameter learning process.\nIn the context of deep learning, examples of the hyperparameters are the\nnumber of convolutional layers and the number of convolutional kernels in\neach of them, the number and size of the fully connected weights,\nchoice of the optimizer and its learning rate, the number of training epochs,\nchoice of the nonlinearities, etc.\n\n\nWhile developing a ML model you usually have to adjust and tune\nhyperparameters of your model or pipeline (e.g., number of layers,\nlearning rate, number of epochs). Deep learning models usually have\nmany free parameters; they could be considered as complex models with\nmany degrees of freedom. If you kept using the test dataset to\nevaluate your adjustment, you would run into data leakage.\n\nThis means that if you use the test set to adjust the hyperparameters\nof your model, the model implicitly learns or memorizes the test set.\nTherefore, the trained model is no longer independent of the test set\n(even though it was never used for training explicitly!).\nIf you perform any hyperparameter tuning, you need a third split,\nthe so-called validation set.\n\nThis tutorial shows the three basic schemes for training and evaluating\nthe model as well as two methods to tune your hyperparameters.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>You might recognize that the accuracy gets better throughout\n   the experiments of this tutorial. The reason behind that is that\n   we always use the same model with the same parameters in every\n   segment to keep the tutorial short and readable. If you do your\n   own experiments you always have to reinitialize the model before\n   training.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing of data, defining a model, etc.\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading data\n\nIn this example, we load the BCI Competition IV 2a data [1]_, for one\nsubject (subject id 3), using braindecode's wrapper to load via\n[MOABB library](https://github.com/NeuroTechX/moabb)_ [2]_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing data\n\nIn this example, preprocessing includes signal rescaling, the bandpass filtering\n(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using\nthe exponential moving mean and variance.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize,\n    preprocess,\n    Preprocessor,\n)\n\nlow_cut_hz = 4.0  # low cut frequency for filtering\nhigh_cut_hz = 38.0  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor(\"pick_types\", eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(\n        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV\n        factor=1e6,\n    ),\n    Preprocessor(\"filter\", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(\n        exponential_moving_standardize,  # Exponential moving standardization\n        factor_new=factor_new,\n        init_block_size=init_block_size,\n    ),\n]\n\n# Preprocess the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Windows\n\nExtraction of the trials (windows) from the time series is based on the\nevents inside the dataset. One event is the demarcation of the stimulus or\nthe beginning of the trial. In this example, we want to analyse 0.5 [s] long\nbefore the corresponding event and the duration of the event itself.\n#Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the\n``trial_stop_offset_seconds`` to 0 [s].\n\nWe extract from the dataset the sampling frequency, which is the same for\nall datasets in this case, and we tested it.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are\n   defined in seconds and need to be converted into samples (multiplication\n   with the sampling frequency), relative to the event.\n   This variable is dataset dependent.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info[\"sfreq\"]\nassert all([ds.raw.info[\"sfreq\"] == sfreq for ds in dataset.datasets])\n# Calculate the window start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to\n# define how windows should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and test\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset BCIC IV 2a dataset using additional\ninfo stored in the description attribute, in this case the ``session``\ncolumn. We select ``0train`` for training and ``0test`` for testing.\nFor other datasets, you might have to choose another column and/or column.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>No matter which of the three schemes you use, this initial\n   two-fold split into train_set and test_set always remains the same.\n   Remember that you are not allowed to use the test_set during any\n   stage of training or tuning.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split(\"session\")\ntrain_set = splitted['0train']  # Session train\ntest_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create model\n\nIn this tutorial, ShallowFBCSPNet classifier [3]_ is explored. The model\ntraining is performed on GPU if it exists, otherwise on CPU.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = \"cuda\" if cuda else \"cpu\"\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = windows_dataset[0][0].shape[0]\ninput_window_samples = windows_dataset[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=\"auto\",\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to train and evaluate your model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Simple Train-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This is the easiest training scheme to use as the dataset is only\nsplit into two distinct sets (``train_set`` and ``test_set``).\nThis scheme uses no separate validation split and should only be\nused for the final evaluation of the (previously!) found\nhyperparameters configuration.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>If you make any use of the ``test_set`` during training\n   (e.g. by using EarlyStopping) there will be data leakage\n   which will make the reported generalization capability/decoding\n   performance of your model less credible.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\nclf.fit(train_set, y=None)\n\n# evaluated the model after training\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the first option with a util function.\nThe following figure illustrates split of entire dataset into the\ntraining and testing subsets.\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\n\nsns.set(font_scale=1.5)\n\n\ndef plot_simple_train_test(ax, all_dataset, train_set, test_set):\n    \"\"\"Create a sample plot for training-testing split.\"\"\"\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#DDF2FF\", \"#2196F3\"]\n\n    ax.barh(\"Original\\ndataset\", len(all_dataset), left=0,\n            height=0.5, color=bd_cmap[0])\n    ax.barh(\"Train-Test\\nsplit\", len(train_set), left=0,\n            height=0.5, color=bd_cmap[1])\n    ax.barh(\"Train-Test\\nsplit\", len(test_set), left=len(train_set),\n            height=0.5, color=bd_cmap[2])\n\n    ax.invert_yaxis()\n    ax.set(xlabel=\"Number of samples.\", title=\"Train-Test split\")\n    ax.legend([\"Original set\", \"Training set\", \"Testing set\"], loc='lower center',\n              ncols=4, bbox_to_anchor=(0.5, 0.5))\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 8))\nplot_simple_train_test(ax=ax, all_dataset=windows_dataset,\n                       train_set=train_set, test_set=test_set)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: Train-Val-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When evaluating different settings hyperparameters for your model,\nthere is still a risk of overfitting on the test set because the\nparameters can be tweaked until the estimator performs optimally.\nFor more information visit [sklearns Cross-Validation Guide](https://scikit-learn.org/stable/modules/cross_validation.html)_.\nThis second option splits the original ``train_set`` into two distinct\nsets, the training set and the validation set to avoid overfitting\nthe hyperparameters to the test set.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>If your dataset is really small, the validation split can become\n   quite small. This may lead to unreliable tuning results. To\n   avoid this, either use Option 3 or adjust the split ratio.</p></div>\n\nTo split the ``train_set`` we will make use of the\n``train_split`` argument of ``EEGClassifier``. If you leave this empty\n(not None!), skorch will make an 80-20 train-validation split.\nIf you want to control the split manually you can do that by using\n``Subset`` from torch and ``predefined_split`` from skorch.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import Subset\nfrom sklearn.model_selection import train_test_split\nfrom skorch.helper import predefined_split, SliceDataset\n\nX_train = SliceDataset(train_set, idx=0)\ny_train = np.array([y for y in SliceDataset(train_set, idx=1)])\ntrain_indices, val_indices = train_test_split(\n    X_train.indices_, test_size=0.2, shuffle=False\n)\ntrain_subset = Subset(train_set, train_indices)\nval_subset = Subset(train_set, val_indices)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>The parameter ``shuffle`` is set to ``False``. For time-series\n   data this should always be the case as shuffling might take\n   advantage of correlated samples, which would make the validation\n   performance less meaningful.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "clf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(val_subset),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\nclf.fit(train_subset, y=None)\n\n# evaluate the model after training and validation\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the second option with a util function.\nThe following figure illustrates split of entire dataset into the\ntraining, validation and testing subsets.\n``Making more compact plot_train_valid_test function.``\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):\n    \"\"\"Create a sample plot for training, validation, testing.\"\"\"\n\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\", ]\n\n    n_train, n_val, n_test = len(train_subset), len(val_subset), len(test_set)\n    ax.barh(\"Original\\ndataset\", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_train, left=0, height=0.5, color=bd_cmap[1])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_val, left=n_train, height=0.5, color=bd_cmap[2])\n    ax.barh(\"Train-Test-Valid\\nsplit\", n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])\n\n    ax.invert_yaxis()\n    ax.set(xlabel=\"Number of samples.\", title=\"Train-Test-Valid split\")\n    ax.legend([\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n              loc=\"lower center\", ncols=2, bbox_to_anchor=(0.5, 0.4))\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 5))\nplot_train_valid_test(ax=ax, all_dataset=windows_dataset,\n                      train_subset=train_subset, val_subset=val_subset, test_set=test_set,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 3: k-Fold Cross Validation\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "As mentioned above, using only one validation split might not be\nsufficient, as there might be a shift in the data distribution.\nTo compensate for this, one can run a k-fold Cross Validation,\nwhere every sample of the training set is in the validation set once.\nAfter averaging over the k validation scores afterwards, you get a\nvery reliable estimate of how the model would perform on unseen\ndata (test set).\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This k-Fold Cross Validation can be used without a separate\n   (holdout) test set. If there is no test set available, e.g. in a\n   competition, this scheme is highly recommended to get a reliable\n   estimate of the generalization performance.</p></div>\n\nTo implement this, we will make use of sklearn function\n[cross_val_score](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.cross_val_score.html)_ and the [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_\nselection.KFold.html)_. CV splitter.\nThe ``train_split`` argument has to be set to ``None``, as sklearn\nwill take care of the splitting.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\n\nfrom sklearn.model_selection import KFold, cross_val_score\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n    max_epochs=n_epochs,\n)\n\ntrain_val_split = KFold(n_splits=5, shuffle=False)\n# By setting n_jobs=-1, cross-validation is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\ncv_results = cross_val_score(\n    clf, X_train, y_train, scoring=\"accuracy\", cv=train_val_split, n_jobs=1\n)\nprint(\n    f\"Validation accuracy: {np.mean(cv_results * 100):.2f}\"\n    f\"+-{np.std(cv_results * 100):.2f}%\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Let's visualize the third option with a util function.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plot_k_fold(ax, cv, all_dataset, X_train, y_train, test_set):\n    \"\"\"Create a sample plot for training, validation, testing.\"\"\"\n\n    bd_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\", ]\n\n    ax.barh(\"Original\\nDataset\", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])\n\n    # Generate the training/validation/testing data fraction visualizations for each CV split\n    for ii, (tr_idx, val_idx) in enumerate(cv.split(X=X_train, y=y_train)):\n        n_train, n_val, n_test = len(tr_idx), len(val_idx), len(test_set)\n        n_train2 = n_train + n_val - max(val_idx) - 1\n        ax.barh(\"cv\" + str(ii + 1), min(val_idx), left=0, height=0.5, color=bd_cmap[1])\n        ax.barh(\"cv\" + str(ii + 1), n_val, left=min(val_idx), height=0.5, color=bd_cmap[2])\n        ax.barh(\"cv\" + str(ii + 1), n_train2, left=max(val_idx) + 1, height=0.5, color=bd_cmap[1])\n        ax.barh(\"cv\" + str(ii + 1), n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])\n\n    ax.invert_yaxis()\n    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])\n    ax.set(xlabel=\"Number of samples.\", title=\"KFold Train-Test-Valid split\")\n    ax.legend([Patch(color=bd_cmap[i]) for i in range(4)],\n              [\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n              loc=\"lower center\", ncols=2)\n    ax.text(-0.07, 0.45, 'Train-Valid-Test split', rotation=90,\n            verticalalignment='center', horizontalalignment='left', transform=ax.transAxes)\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(15, 7))\nplot_k_fold(ax, cv=train_val_split, all_dataset=windows_dataset,\n            X_train=X_train, y_train=y_train, test_set=test_set,)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to tune your hyperparameters\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "One way to do hyperparameter tuning is to run each configuration\nmanually (via Option 2 or 3 from above) and compare the validation\nperformance afterwards. In the early stages of your development\nprocess this might be sufficient to get a rough understanding of\nhow your hyperparameter should look like for your model to converge.\nHowever, this manual tuning process quickly becomes messy as the\nnumber of hyperparameters you want to (jointly) tune increases.\nTherefore you should, automate this process. We will present two\ndifferent options, analogous to Option 2 and 3 from above.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Train-Val-Test Split\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We will again make use of the [sklearn](https://scikit-learn.org/stable/)_\nlibrary to do the hyperparameter search. [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html)_\nwill perform a Grid Search over the parameters specified in ``param_grid``.\nWe use grid search for the model selection as a simple example, but you can use other strategies\nas well.\n([List of the sklearn classes for model selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection)_.)\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\nfrom sklearn.model_selection import GridSearchCV\n\ntrain_val_split = [\n    tuple(train_test_split(X_train.indices_, test_size=0.2, shuffle=False))\n]\n\nparam_grid = {\n    \"optimizer__lr\": [0.00625, 0.000625],\n}\n\n# By setting n_jobs=-1, grid search is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=train_val_split,\n    return_train_score=True,\n    scoring=\"accuracy\",\n    refit=True,\n    verbose=1,\n    error_score=\"raise\",\n    n_jobs=1,\n)\n\nsearch.fit(X_train, y_train)\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results[\"rank_test_score\"] == 1].squeeze()\n\nbest_parameters = best_run[\"params\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: k-Fold Cross Validation\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To perform a full k-Fold CV just replace ``train_val_split`` from\nabove with the [KFold](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.KFold.html)_ cross-validator from sklearn.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "train_val_split = KFold(n_splits=5, shuffle=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
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@@ -1,379 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# How to train, test and tune your model\n\nThis tutorial shows you how to properly train, tune and test your deep learning\nmodels with Braindecode. We will use the BCIC IV 2a dataset as a showcase example.\n\nThe methods shown can be applied to any standard supervised trial-based decoding setting.\nThis tutorial will include additional parts of code like loading and preprocessing,\ndefining a model, and other details which are not exclusive to this page (compare\n[Cropped Decoding Tutorial](./plot_bcic_iv_2a_moabb_trial.html)_). Therefore we\nwill not further elaborate on these parts and you can feel free to skip them.\n\nIn general we distinguish between \"usual\" training and evaluation and hyperparameter search.\nThe tutorial is therefore split into two parts, one for the three different training schemes\nand one for the two different hyperparameter tuning methods.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Why should I care about model evaluation?\nShort answer: To produce reliable results!\n\nIn machine learning, we usually follow the scheme of splitting the\ndata into two parts, training and testing sets. It sounds like a\nsimple division, right? But the story does not end here.\n\nWhile developing a ML model you usually have to adjust and tune\nhyperparameters of your model or pipeline (e.g., number of layers,\nlearning rate, number of epochs). Deep learning models usually have\nmany free parameters; they could be considered complex models with\nmany degrees of freedom. If you kept using the test dataset to\nevaluate your adjustmentyou would run into data leakage.\n\nThis means that if you use the test set to adjust the hyperparameters\nof your model, the model implicitly learns or memorizes the test set.\nTherefore, the trained model is no longer independent of the test set\n(even though it was never used for training explicitly!).\nIf you perform any hyperparameter tuning, you need a third split,\nthe so-called validation set.\n\nThis tutorial shows the three basic schemes for training and evaluating\nthe model as well as two methods to tune your hyperparameters.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>You might recognize that the accuracy gets better throughout\n   the experiments of this tutorial. The reason behind that is that\n   we always use the same model with the same paramters in every\n   segment to keep the tutorial short and readable. If you do your\n   own experiments you always have to reinitialize the model before\n   training.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Loading, preprocessing, defining a model, etc.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Loading\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Preprocessing\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize,\n    preprocess,\n    Preprocessor,\n)\n\nlow_cut_hz = 4.0  # low cut frequency for filtering\nhigh_cut_hz = 38.0  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\npreprocessors = [\n    Preprocessor(\"pick_types\", eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(\n        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV\n        factor=1e6,\n    ),\n    Preprocessor(\"filter\", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(\n        exponential_moving_standardize,  # Exponential moving standardization\n        factor_new=factor_new,\n        init_block_size=init_block_size,\n    ),\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Cut Compute Windows\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info[\"sfreq\"]\nassert all([ds.raw.info[\"sfreq\"] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create model\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = \"cuda\" if cuda else \"cpu\"\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans and time steps from dataset\nn_channels = windows_dataset[0][0].shape[0]\ninput_window_samples = windows_dataset[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=\"auto\",\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## How to train and evaluate your model\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split dataset into train and test\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case the ``session`` column. We\nselect ``session_T`` for training and ``session_E`` for testing.\nFor other datasets, you might have to choose another column.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>No matter which of the three schemes you use, this initial\n   two-fold split into train_set and test_set always remains the same.\n   Remember that you are not allowed to use the test_set during any\n   stage of training or tuning.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "splitted = windows_dataset.split(\"session\")\ntrain_set = splitted[\"session_T\"]\ntest_set = splitted[\"session_E\"]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 1: Simple Train-Test Split\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This is the easiest training scheme to use as the dataset is only\nsplit into two distinct sets (``train_set`` and ``test_set``).\nThis scheme uses no separate validation split and should only be\nused for the final evaluation of the (previously!) found\nhyperparameters configuration.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>If you make any use of the ``test_set`` during training\n   (e.g. by using EarlyStopping) there will be data leakage\n   which will make the reported generalization capability/decoding\n   performance of your model less credible.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)\n\n# score the Model after training\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Let's visualize the first option with a util function.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.patches import Patch\n\n\ndef plot_simple_train_test(ax, windows_dataset, train_set, test_set):\n    \"\"\"Create a sample plot for training-testing split.\"\"\"\n    braindecode_cmap = [\"#3A6190\", \"#683E00\", \"#DDF2FF\", \"#2196F3\"]\n\n    ax.scatter(\n        range(len(windows_dataset)),\n        [3.5] * len(windows_dataset),\n        c=braindecode_cmap[0],\n        marker=\"_\",\n        lw=50,\n    )\n\n    ax.scatter(\n        range(len(train_set) + len(test_set)),\n        [0.5] * len(train_set) + [0.5] * len(test_set),\n        c=[braindecode_cmap[1]] * len(train_set)\n        + [braindecode_cmap[2]] * len(test_set),\n        marker=\"_\",\n        lw=50,\n    )\n\n    ax.set(\n        ylim=[-1, 5],\n        yticks=[0.5, 3.5],\n        yticklabels=[\"Train-Test\\nSplit\", \"Original\\nDataset\"],\n        xlabel=\"Number of samples.\",\n        title=\"Train-Test Split\",\n    )\n\n    ax.legend(\n        [\n            Patch(color=braindecode_cmap[0]),\n            Patch(color=braindecode_cmap[1]),\n            Patch(color=braindecode_cmap[2]),\n        ],\n        [\"Original set\", \"Training set\", \"Testing set\"],\n        loc=(1.02, 0.8),\n    )\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 5))\nplot_simple_train_test(\n    ax=ax, windows_dataset=windows_dataset, train_set=train_set, test_set=test_set\n)\nfig.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 2: Train-Val-Test Split\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When evaluating different settings hyperparameters for your model,\nthere is still a risk of overfitting on the test set because the\nparameters can be tweaked until the estimator performs optimally.\nFor more information visit [sklearns Cross-Validation Guide](https://scikit-learn.org/stable/modules/cross_validation.html)_.\nThis second option splits the original ``train_set`` into two distinct\nsets, the training set and the validation set to avoid overfitting\nthe hyperparameters to the test set.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>If your dataset is really small, the validation split can become\n   quite small. This may lead to unreliable tuning results. To\n   avoid this, either use Option 3 or adjust the split ratio.</p></div>\n\nTo split the ``train_set`` we will make use of the\n``train_split`` argument of ``EEGClassifier``. If you leave this empty\n(not None!), skorch will make an 80-20 train-validation split.\nIf you want to control the split manually you can do that by using\n``Subset`` from torch and ``predefined_split`` from skorch.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from torch.utils.data import Subset\nfrom sklearn.model_selection import train_test_split\nfrom skorch.helper import predefined_split, SliceDataset\n\nX_train = SliceDataset(train_set, idx=0)\ny_train = np.array([y for y in SliceDataset(train_set, idx=1)])\ntrain_indices, val_indices = train_test_split(\n    X_train.indices_, test_size=0.2, shuffle=False\n)\ntrain_subset = Subset(train_set, train_indices)\nval_subset = Subset(train_set, val_indices)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<div class=\"alert alert-info\"><h4>Note</h4><p>The parameter ``shuffle`` is set to ``False``. For time-series\n   data this should always be the case as shuffling might take\n   advantage of correlated samples, which would make the validation\n   performance less meaningful.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "clf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(val_subset),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\nclf.fit(train_subset, y=None, epochs=n_epochs)\n\n# score the Model after training (optional)\ny_test = test_set.get_metadata().target\ntest_acc = clf.score(test_set, y=y_test)\nprint(f\"Test acc: {(test_acc * 100):.2f}%\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Let's visualize the second option with a util function.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def plot_train_valid_test(ax, windows_dataset, train_subset, val_subset, test_set):\n    \"\"\"Create a sample plot for training, validation, testing.\"\"\"\n\n    braindecode_cmap = [\n        \"#3A6190\",\n        \"#683E00\",\n        \"#2196F3\",\n        \"#DDF2FF\",\n    ]\n    ax.scatter(\n        range(len(windows_dataset)),\n        [3.5] * len(windows_dataset),\n        c=braindecode_cmap[0],\n        marker=\"_\",\n        lw=50,\n    )\n\n    ax.scatter(\n        range(len(train_subset) + len(val_subset) + len(test_set)),\n        [0.5] * len(train_subset) + [0.5] * len(val_subset) + [0.5] * len(test_set),\n        c=[braindecode_cmap[1]] * len(train_subset)\n        + [braindecode_cmap[2]] * len(val_subset)\n        + [braindecode_cmap[3]] * len(test_set),\n        marker=\"_\",\n        lw=50,\n    )\n\n    ax.set(\n        ylim=[-1, 5],\n        yticks=[0.5, 3.5],\n        yticklabels=[\"Train-Test\\nSplit\", \"Original\\nDataset\"],\n        xlabel=\"Number of samples.\",\n        title=\"Train-Validation-Test Split\",\n    )\n\n    ax.legend(\n        [\n            Patch(color=braindecode_cmap[0]),\n            Patch(color=braindecode_cmap[1]),\n            Patch(color=braindecode_cmap[2]),\n            Patch(color=braindecode_cmap[3]),\n        ],\n        [\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n        loc=(1.02, 0.8),\n    )\n\n    return ax\n\n\nfig, ax = plt.subplots(figsize=(12, 5))\nplot_train_valid_test(\n    ax=ax,\n    windows_dataset=windows_dataset,\n    train_subset=train_subset,\n    val_subset=val_subset,\n    test_set=test_set,\n)\nfig.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 3: k-Fold Cross Validation\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "As mentioned above, using only one validation split might not be\nsufficient, as there might be a shift in the data distribution.\nTo compensate for this, one can run a k-fold Cross Validation,\nwhere every sample of the training set is in the validation set once.\nAfter averaging over the k validation scores afterwards, you get a\nvery reliable estimate of how the model would perform on unseen\ndata (test set).\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This k-Fold Cross Validation can be used without a separate\n   (holdout) test set. If there is no test set available, e.g. in a\n   competition, this scheme is highly recommended to get a reliable\n   estimate of the generalization performance.</p></div>\n\nTo implement this, we will make use of sklearn function\n[cross_val_score](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.cross_val_score.html)_ and the [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_\nselection.KFold.html)_. CV splitter.\nThe ``train_split`` argument has to be set to ``None``, as sklearn\nwill take care of the splitting.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import KFold, cross_val_score\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=None,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler(\"CosineAnnealingLR\", T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n\ntrain_val_split = KFold(n_splits=5, shuffle=False)\nfit_params = {\"epochs\": n_epochs}\ncv_results = cross_val_score(\n    clf, X_train, y_train, scoring=\"accuracy\", cv=train_val_split, fit_params=fit_params\n)\nprint(\n    f\"Validation accuracy: {np.mean(cv_results * 100):.2f}\"\n    f\"+-{np.std(cv_results * 100):.2f}%\"\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Let's visualize the third option with a util function.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "braindecode_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\"]\n\n\ndef encode_color(value, br_cmap=braindecode_cmap):\n    # Util to encoder color\n    if value == 0:\n        return br_cmap[1]\n    else:\n        return br_cmap[2]\n\n\ndef plot_k_fold(cv, windows_dataset, X_train, y_train, test_set):\n    braindecode_cmap = [\"#3A6190\", \"#683E00\", \"#2196F3\", \"#DDF2FF\"]\n\n    mosaic = \"\"\"\n      aa\n      BC\n      \"\"\"\n\n    axes = plt.figure(figsize=(15, 7), constrained_layout=True).subplot_mosaic(\n        mosaic,\n        gridspec_kw={\"height_ratios\": [1.5, 5], \"width_ratios\": [3.5, 3.5]},\n    )\n\n    # Generate the training/testing visualizations for each CV split\n    for ii, (tr, tt) in enumerate(cv.split(X=X_train, y=y_train)):\n        # Fill in indices with the training/test groups\n\n        axes[\"a\"].scatter(\n            range(len(windows_dataset)),\n            [3.5] * len(windows_dataset),\n            c=braindecode_cmap[0],\n            marker=\"_\",\n            lw=20,\n        )\n        indices = np.array([np.nan] * len(X_train))\n        indices[tt] = 1\n        indices[tr] = 0\n\n        color_indices = list(map(encode_color, indices))\n\n        # Visualize the results\n        axes[\"B\"].scatter(\n            range(len(indices)),\n            [ii + 0.5] * len(indices),\n            c=color_indices,\n            marker=\"_\",\n            lw=10,\n            vmin=-0.2,\n            vmax=1.2,\n        )\n\n        axes[\"C\"].scatter(\n            range(len(test_set)),\n            [ii + 0.5] * len(test_set),\n            c=braindecode_cmap[3],\n            marker=\"_\",\n            lw=10,\n        )\n\n    axes[\"a\"].set(\n        yticklabels=[\"\"],\n        xlim=[0, len(windows_dataset) + 1],\n        ylabel=\"Original\\nData\",\n        ylim=[3.4, 3.6],\n    )\n    axes[\"a\"].yaxis.get_label().set_fontsize(16)\n\n    axes[\"C\"].set(\n        yticks=np.arange(5) + 0.5, yticklabels=[\"\"] * 5, xlim=[0, 300], ylim=[5, -0.2],\n    )\n\n    # Formatting\n    yticklabels = list(range(5))\n\n    axes[\"B\"].set(\n        yticks=np.arange(5) + 0.5,\n        yticklabels=yticklabels,\n        ylabel=\"CV iteration\",\n        ylim=[5, -0.2],\n        xlim=[0, 300],\n    )\n\n    axes[\"B\"].yaxis.get_label().set_fontsize(16)\n\n    axes[\"a\"].set_title(\"Training, testing with k-Fold Cross Validation\", fontsize=15)\n\n    plt.legend(\n        [\n            Patch(color=braindecode_cmap[0]),\n            Patch(color=braindecode_cmap[1]),\n            Patch(color=braindecode_cmap[2]),\n            Patch(color=braindecode_cmap[3]),\n        ],\n        [\"Original set\", \"Training set\", \"Validation set\", \"Testing set\"],\n        loc=(1.02, 0),\n    )\n    plt.subplots_adjust(wspace=0.075)\n\n\nplot_k_fold(\n    cv=train_val_split,\n    windows_dataset=windows_dataset,\n    X_train=X_train,\n    y_train=y_train,\n    test_set=test_set,\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## How to tune your hyperparameters\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "One way to do hyperparameter tuning is to run each configuration\nmanually (via Option 2 or 3 from above) and compare the validation\nperformance afterwards. In the early stages of your developement\nprocess this might be sufficient to get a rough understanding of\nhow your hyperparameter should look like for your model to converge.\nHowever, this manual tuning process quickly becomes messy as the\nnumber of hyperparameters you want to (jointly) tune increases.\nTherefore you sould automate this process. We will present two\ndifferent options, analogous to Option 2 and 3 from above.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 1: Train-Val-Test Split\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We will again make use of the sklearn library to do the hyperparameter\nsearch. [GridSearchCV](https://scikit-learn.org/stable/modules/\ngenerated/sklearn.model_selection.GridSearchCV.html)_ will perform\na Grid Search over the parameters specified in ``param_grid``.\nWe use grid search as a simple example, but you can use [any strategy\nyou want](https://scikit-learn.org/stable/modules/classes.html#\nmodule-sklearn.model_selection)_).\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import pandas as pd\nfrom sklearn.model_selection import GridSearchCV\n\ntrain_val_split = [\n    tuple(train_test_split(X_train.indices_, test_size=0.2, shuffle=False))\n]\n\nparam_grid = {\n    \"optimizer__lr\": [0.00625, 0.000625],\n}\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=train_val_split,\n    return_train_score=True,\n    scoring=\"accuracy\",\n    refit=True,\n    verbose=1,\n    error_score=\"raise\",\n)\n\nsearch.fit(X_train, y_train, **fit_params)\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results[\"rank_test_score\"] == 1].squeeze()\nprint(\n    f\"Best hyperparameters were {best_run['params']} which gave a validation \"\n    f\"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training \"\n    f\"accuracy of {best_run['mean_train_score'] * 100:.2f}%).\"\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Option 2: k-Fold Cross Validation\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To perform a full k-Fold CV just replace ``train_val_split`` from\nabove with the ``KFold`` cross-validator from sklearn.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "train_val_split = KFold(n_splits=5, shuffle=False)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/3862cafd7f0d815e434319ffe525afc8/plot_bcic_iv_2a_moabb_cropped.ipynb b/stable/_downloads/3862cafd7f0d815e434319ffe525afc8/plot_bcic_iv_2a_moabb_cropped.ipynb
new file mode 100644
index 00000000..113e6bfd
--- /dev/null
+++ b/stable/_downloads/3862cafd7f0d815e434319ffe525afc8/plot_bcic_iv_2a_moabb_cropped.ipynb
@@ -0,0 +1,226 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Cropped Decoding on BCIC IV 2a Dataset\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Building on the [Trialwise decoding](./plot_bcic_iv_2a_moabb_trial.html)_,\nwe now do more data-efficient cropped decoding!\n\nIn Braindecode, there are two supported configurations created for\ntraining models: trialwise decoding and cropped decoding. We will\nexplain this visually by comparing trialwise to cropped decoding.\n\n<img src=\"https://braindecode.org/stable/_static/trialwise_explanation.png\" alt=\"Trialwise decoding\">\n<img src=\"https://braindecode.org/stable/_static/cropped_explanation.png\" alt=\"Cropped decoding\">\nOn the left, you see trialwise decoding:\n\n1. A complete trial is pushed through the network.\n2. The network produces a prediction.\n3. The prediction is compared to the target (label) for that trial to\n   compute the loss.\n\nOn the right, you see cropped decoding:\n\n1. Instead of a complete trial, crops are pushed through the network.\n2. For computational efficiency, multiple neighbouring crops are pushed\n   through the network simultaneously (these neighbouring crops are\n   called compute windows)\n3. Therefore, the network produces multiple predictions (one per crop in\n   the window)\n4. The individual crop predictions are *AVERAGED* before computing the\n   loss function\n\nThis averaging of predictions of small sub-windows is the key difference\nbetween trialwise and cropped decoding. It was introduced in [1]_ and it impact\non the parameters of the network.\n\nIt is important to note that the averaging of predictions is only done\nduring training. During testing, the network is still applied to crops\nand the predictions are averaged afterwards.\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>-  The network architecture implicitly defines the crop size (it is the\n       receptive field size, i.e., the number of timesteps the network uses\n       to make a single prediction)\n    -  The window size is a user-defined hyperparameter, called\n       ``input_window_samples`` in Braindecode. It mostly affects runtime\n       (larger window sizes should be faster). As a rule of thumb, you can\n       set it to two times the crop size.\n    -  Crop size and window size together define how many predictions the\n       network makes per window: ``#window \u2212 #crop + 1 = #predictions``</p></div>\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>For cropped decoding, the above training setup is mathematically\n    similar to sampling crops in your dataset, pushing them through the\n    network and training directly on the individual crops. However, the\n    if their position would be randomly selected, the crops would be less\n    correlated in contrast to the neighbourhood crops selected from a window.\n    At the same time, the above training setup is much faster as it avoids\n    redundant computations by using dilated convolutions, see [2]_.\n    However, the two setups are only mathematically related in case (1)\n    your network does not use any padding or only left padding and\n    (2) your loss function leads\n    to the same gradients when using the averaged output. The first is true\n    for our shallow and deep ConvNet models and the second is true for the\n    log-softmax outputs and negative log likelihood loss that is typically\n    used for classification in PyTorch.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\nLoading and preprocessing stays the same as in the [Trialwise decoding\ntutorial](./plot_bcic_iv_2a_moabb_trial.html)_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])\n\nfrom numpy import multiply\n\nfrom braindecode.preprocessing import (\n    Preprocessor,\n    exponential_moving_standardize,\n    preprocess,\n)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),\n    # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),\n    # Bandpass filter\n    Preprocessor(exponential_moving_standardize,\n                 # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model and compute windowing parameters\nIn contrast to trialwise decoding, we first have to create the model\nbefore we can cut the dataset into windows. This is because we need to\nknow the neural network parameters to know how large the sub-window\nstride should be.\n\nWe first choose the compute/input window size that will be fed to the\nnetwork during training. This has to be larger than the networks\nthe number of timesteps size and can otherwise be chosen for computational\nefficiency (see explanations in the beginning of this tutorial). Here we\nchoose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "input_window_samples = 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the model. To enable it to be used in cropped decoding\nefficiently, we manually set the length of the final convolution layer\nto some length that makes the number of timesteps of the ConvNet smaller\nthan ``input_window_samples`` (see ``final_conv_length=30`` in the model\ndefinition).\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans from dataset\nn_chans = dataset[0][0].shape[0]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=30,\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    _ = model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And now we transform model with strides to a model that outputs dense\nprediction, so we can use it to obtain predictions for all\ncrops.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model.to_dense_prediction_model()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To know the models\u2019 output shape without the last layer, we calculate the\nshape of model output for a dummy input.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_preds_per_input = model.get_output_shape()[2]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cut the data into windows\nIn contrast to trialwise decoding, we have to supply an explicit\nwindow size and window stride to the ``create_windows_from_events``\nfunction.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    preload=True\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Split the dataset\n\nThis code is the same as in trialwise decoding.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\nIn difference to trialwise decoding, we now should supply\n``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the\ncriterion, as well as ``criterion__loss_function`` as the loss function\napplied to the meaned predictions.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n    have found to work well for motor decoding, however we strongly\n    encourage you to perform your own hyperparameter optimization using\n    cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\nfrom braindecode.training import CroppedLoss\n\n# These values we found good for shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    cropped=True,\n    criterion=CroppedLoss,\n    criterion__loss_function=torch.nn.functional.nll_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\n_ = clf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n<div class=\"alert alert-info\"><h4>Note</h4><p>Note that we drop further in the classification error and\n    loss as in the trialwise decoding tutorial.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy',\n                   'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,\n    fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(\n    Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(\n    Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Confusion Matrix\n\nGenerate a confusion matrix as in [2]_\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = valid_set.datasets[0].window_kwargs[0][1]['mapping']\n# sort the labels by values (values are integer class labels)\nlabels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb b/stable/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb
new file mode 100644
index 00000000..85f1f7ee
--- /dev/null
+++ b/stable/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb
@@ -0,0 +1,187 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Process a big data EEG resource (TUH EEG Corpus)\n\nIn this example, we showcase usage of the Temple University Hospital EEG Corpus\n(https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)\nincluding simple preprocessing steps as well as cutting of compute windows.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows)\nfrom numpy import multiply\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating the dataset using TUHMock\n\nSince the data is not available at the time of the creation of this example,\nwe are required to mock some of the dataset functionality. Therefore, if you\nwant to try this code with the actual data, please disconsider this section.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Firstly, we start by creating a TUH mock dataset using braindecode's _TUHMock class.\nThe complete code can be found at :func:`braindecode.datasets.TUH`, but we will give\na small description of how it works.\nThis class is able to read the recordings from TUH_PATH and generate a description\nby parsing information from file paths, such as patient id and recording data.\nTHis description can later be accessed by the object's .description method.\nAfter that, the files are sorted chronologically by year, month, day,\npatient id, recording session and segment, and then use the description corresponding\nto the specified by recording ids.\nFInally, additional information regarding age and gender of the subjects are parsed\ndirectly to the description.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "TUH_PATH = 'please insert actual path to data here'\n# specify the number of jobs for loading and windowing\nN_JOBS = 2\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=None,\n    preload=False,\n    add_physician_reports=False,\n    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can visualize our data's statistics using the class' \"description\" method\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def plt_histogram(df_of_ages_genders, alpha=0.5, fs=24, ylim=1.5, show_title=True):\n    # Dafarame containing info about gender and age of subjects\n    df = df_of_ages_genders\n    male_df = df[df[\"gender\"] == 'M']\n    female_df = df[df[\"gender\"] == 'F']\n\n    plt.figure(figsize=(15, 18))\n    if show_title:\n        plt.suptitle(\"Age information\", y=0.95, fontsize=fs + 5)\n\n    # First plot: Male individuals\n    plt.subplot(121)\n    plt.hist(male_df[\"age\"], bins=np.linspace(0, 100, 101),\n             alpha=alpha, color=\"green\", orientation=\"horizontal\")\n    plt.axhline(np.mean(male_df[\"age\"]), color=\"black\",\n                label=f\"mean age {np.mean(male_df['age']):.1f} \"\n                      f\"(\u00b1{np.std(male_df['age']):.1f})\")\n    plt.barh(np.mean(male_df[\"age\"]), height=2 * np.std(male_df[\"age\"]),\n             width=ylim, color=\"black\", alpha=0.25)\n\n    # Legend\n    plt.xlim(0, ylim)\n    plt.legend(fontsize=fs, loc=\"upper left\")\n    plt.title(f\"male ({100 * len(male_df) / len(df):.1f}%)\",\n              fontsize=fs, loc=\"left\", y=1, x=0.05)\n    plt.yticks(color='w')\n    plt.gca().invert_xaxis()\n    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)\n    plt.tick_params(labelsize=fs - 5)\n\n    # First plot: Female individuals\n    plt.subplot(122)\n    plt.hist(female_df[\"age\"], bins=np.linspace(0, 100, 101),\n             alpha=alpha, color=\"orange\", orientation=\"horizontal\")\n    plt.axhline(np.mean(female_df[\"age\"]), color=\"black\", linestyle=\"--\",\n                label=f\"mean age {np.mean(female_df['age']):.1f} (\"\n                      f\"\u00b1{np.std(female_df['age']):.1f})\")\n    plt.barh(np.mean(female_df[\"age\"]), height=2 * np.std(female_df[\"age\"]),\n             width=ylim, color=\"black\", alpha=0.25)\n\n    # Label\n    plt.legend(fontsize=fs, loc=\"upper right\")\n    plt.xlim(0, ylim)\n    plt.title(f\"female ({100 * len(female_df) / len(df):.1f}%)\",\n              fontsize=fs, loc=\"right\", y=1, x=0.95)\n    plt.ylim(0, 100)\n    plt.subplots_adjust(wspace=0, hspace=0)\n    plt.ylabel(\"age [years]\", fontsize=fs)\n    plt.xlabel(\"count\", fontsize=fs, x=1, labelpad=20)\n    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)\n    plt.tick_params(labelsize=fs - 5)\n\n    plt.show()\n\n\ndf = tuh.description\nplt_histogram(df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preprocessing\n\n### Selecting recordings\n\nFirst, we will do some selection of available recordings based on the duration.\nWe will select those recordings that have at least five minutes duration.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def select_by_duration(ds, tmin=0, tmax=None):\n    if tmax is None:\n        tmax = np.inf\n    # determine length of the recordings and select based on tmin and tmax\n    split_ids = []\n    for d_i, d in enumerate(ds.datasets):\n        duration = d.raw.n_times / d.raw.info['sfreq']\n        # select the ones in the required duration range\n        if tmin <= duration <= tmax:\n            split_ids.append(d_i)\n    splits = ds.split(split_ids)\n    split = splits['0']\n    return split\n\n\ntmin = 5 * 60\ntmax = None\ntuh = select_by_duration(tuh, tmin, tmax)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we will discard all recordings that have an incomplete channel\nconfiguration on the channels that we are interested. The subdivisions of the\nrecordings into 'le' and 'ar' labels represents the channels for\nthe re-referencing of the signals.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "short_ch_names = sorted([\n    'A1', 'A2',\n    'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',\n    'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])\n\n# TUH data is subdivided into 'le' and 'ar' recordings references\nar_ch_names = sorted([\n    'EEG A1-REF', 'EEG A2-REF',\n    'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',\n    'EEG C4-REF', 'EEG P3-REF', 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF',\n    'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', 'EEG T5-REF',\n    'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF'])\nle_ch_names = sorted([\n    'EEG A1-LE', 'EEG A2-LE',\n    'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE',\n    'EEG C4-LE', 'EEG P3-LE', 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE',\n    'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', 'EEG T5-LE',\n    'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE'])\nassert len(short_ch_names) == len(ar_ch_names) == len(le_ch_names)\nar_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    ar_ch_names, short_ch_names)}\nle_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    le_ch_names, short_ch_names)}\nch_mapping = {'ar': ar_ch_mapping, 'le': le_ch_mapping}\n\n\ndef select_by_channels(ds, ch_mapping):\n    split_ids = []\n    for i, d in enumerate(ds.datasets):\n        ref = 'ar' if d.raw.ch_names[0].endswith('-REF') else 'le'\n        # these are the channels we are looking for\n        seta = set(ch_mapping[ref].keys())\n        # these are the channels of the recoding\n        setb = set(d.raw.ch_names)\n        # if recording contains all channels we are looking for, include it\n        if seta.issubset(setb):\n            split_ids.append(i)\n    return ds.split(split_ids)['0']\n\n\ntuh = select_by_channels(tuh, ch_mapping)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Combining preprocessing steps\n\nNext, we use braindecode's preprocess to combine and execute several preprocessing\nsteps that are executed through 'mne':\n\n- Crop the recordings to a region of interest\n- Re-reference all recordings to 'ar' (requires load)\n- Rename channels to short channel names\n- Pick channels of interest\n- Scale signals to micro volts (requires load)\n- Clip outlier values to +/- 800 micro volts (requires load)\n- Resample recordings to a common frequency (requires load)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def custom_rename_channels(raw, mapping):\n    # rename channels which are dependent on referencing:\n    # le: EEG 01-LE, ar: EEG 01-REF\n    # mne fails if the mapping contains channels as keys that are not present\n    # in the raw\n    reference = raw.ch_names[0].split('-')[-1].lower()\n    assert reference in ['le', 'ref'], 'unexpected referencing'\n    reference = 'le' if reference == 'le' else 'ar'\n    raw.rename_channels(mapping[reference])\n\n\ndef custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):\n    # crop recordings to tmin \u2013 tmax. can be incomplete if recording\n    # has lower duration than tmax\n    # by default mne fails if tmax is bigger than duration\n    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)\n    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)\n\n\ntmin = 1 * 60\ntmax = 6 * 60\nsfreq = 100\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,\n                 apply_on_array=False),\n    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),\n    Preprocessor(custom_rename_channels, mapping=ch_mapping,\n                 apply_on_array=False),\n    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),\n    Preprocessor('resample', sfreq=sfreq),\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Next, we can apply the defined preprocessors on the selected recordings in parallel.\nWe additionally use the serialization functionality of\n:func:`braindecode.preprocessing.preprocess` to limit memory usage during\npreprocessing, as each file must be loaded into memory for some of the\npreprocessing steps to work.\nThis also makes it possible to use the lazy\nloading capabilities of :class:`braindecode.datasets.BaseConcatDataset`, as\nthe preprocessed data is automatically reloaded with ``preload=False``.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Here we use ``n_jobs=2`` as the machines the documentation is build on\n   only have two cores. This number should be modified based on the machine\n   that is available for preprocessing.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "OUT_PATH = tempfile.mkdtemp()  # please insert actual output directory here\ntuh_preproc = preprocess(\n    concat_ds=tuh,\n    preprocessors=preprocessors,\n    n_jobs=N_JOBS,\n    save_dir=OUT_PATH\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\nWe can finally generate compute windows. The resulting dataset is now ready\nto be used for model training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "window_size_samples = 1000\nwindow_stride_samples = 1000\n# Generate compute windows here and store them to disk\ntuh_windows = create_fixed_length_windows(\n    tuh_preproc,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_stride_samples,\n    drop_last_window=False,\n    n_jobs=N_JOBS,\n)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py b/stable/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py
new file mode 100644
index 00000000..9d58b381
--- /dev/null
+++ b/stable/_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py
@@ -0,0 +1,380 @@
+"""
+Hyperparameter tuning with scikit-learn
+=======================================
+
+The braindecode provides some compatibility with
+`scikit-learn <https://scikit-learn.org/stable/>`__. This allows us
+to use scikit-learn functionality to find the best hyperparameters for our
+model. This is especially useful to tune hyperparameters or
+parameters for one decoding task or a specific dataset.
+
+.. topic:: Why do you need to tune the neural networks model?
+
+    Deep learning models are often sensitive to the choice of hyperparameters
+    and parameters. Hyperparameters are the parameters set before
+    training the model. The hyeperparameters determine (1) the capacity of the model,
+    e.g. its depth (the number of layers) and its width (the number of
+    convolutional kernels, sizes of fully connected layers) and (2) the
+    learning process via the choice of optimizer and its learning rate,
+    the number of epochs, the batch size, the choice of non-linearities,
+    the strategies to initialize the learning weights, etc.
+    On the other hand, parameters are learned during training,
+    such as the neural network weights. The choice of these can have a
+    significant impact on the performance of the model.
+    Therefore, it is important to tune these to maximize the discriminative
+    power of the model, in the case of decoding tasks (classification,
+    regression, etc.), such as sleep staging, BCI, pathology detection, etc.
+    We recommend the Deep Learning Tuning Playbook by Google to learn more
+    about hyperparameters and parameters tuning [1]_.
+
+
+In this tutorial, we will use the standard decoding approach to show the impact
+of the learning rate and dropout probability on the model's performance.
+
+
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+
+
+######################################################################
+# Loading
+# ~~~~~~~
+#
+
+
+######################################################################
+# First, we load the data. In this tutorial, we use the functionality of
+# braindecode to load datasets via
+# `MOABB <https://github.com/NeuroTechX/moabb>`__ [2]_ to load the BCI
+# Competition IV 2a data [3]_.
+#
+# .. note::
+#    To load your own datasets either via mne or from
+#    preprocessed X/y numpy arrays, see `MNE Dataset
+#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
+#    Tutorial <./plot_custom_dataset_example.html>`__.
+#
+
+from braindecode.datasets.moabb import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# In this example, preprocessing includes signal rescaling, the bandpass filtering
+# (low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+# the exponential moving mean and variance.
+# You can either apply functions provided by
+# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
+# `mne.Epochs <https://mne.tools/stable/generated/mne.Epochs.html>`__
+# or apply your own functions, either to the MNE object or the underlying
+# numpy array.
+#
+# .. note::
+#    These prepocessings are now directly applied to the loaded
+#    data, and not on-the-fly applied as transformations in
+#    PyTorch-libraries like
+#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
+#
+
+from braindecode.preprocessing.preprocess import (
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
+    # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
+    # Bandpass filter
+    Preprocessor(exponential_moving_standardize,
+                 # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+# Preprocess the data
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Extraction of the Compute Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Extraction of the Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Extraction of the trials (windows) from the time series is based on the
+# events inside the dataset. One event is the demarcation of the stimulus or
+# the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+# before the corresponding event and the duration of the event itself.
+# #Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the
+# ``trial_stop_offset_seconds`` to 0 [s].
+#
+# We extract from the dataset the sampling frequency, which is the same for
+# all datasets in this case, and we tested it.
+#
+# .. note::
+#    The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are
+#    defined in seconds and need to be converted into samples (multiplication
+#    with the sampling frequency), relative to the event.
+#    This variable is dataset dependent.
+#
+
+from braindecode.preprocessing.windowers import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case ``session`` column. We select
+# ``0train`` for training and ``1test`` for evaluation.
+#
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+eval_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Create model
+# ------------
+#
+
+
+######################################################################
+# Now we create the deep learning model! Braindecode comes with some
+# predefined convolutional neural network architectures for raw
+# time-domain EEG. Here, we use the ShallowFBCSPNet model from `Deep
+# learning with convolutional neural networks for EEG decoding and
+# visualization <https://arxiv.org/abs/1703.05051>`__ [4]_. These models are
+# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
+# to use your own model, it just has to be a normal PyTorch
+# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
+#
+from functools import partial
+import torch
+from braindecode.util import set_random_seeds
+from braindecode.models import ShallowFBCSPNet
+
+# check if GPU is available, if True chooses to use it
+cuda = torch.cuda.is_available()
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+seed = 20200220  # random seed to make results reproducible
+# Set random seed to be able to reproduce results
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+# Extract number of chans and time steps from dataset
+n_chans = train_set[0][0].shape[0]
+input_window_samples = train_set[0][0].shape[1]
+
+# To analyze the impact of the different parameters inside the torch model, we
+# need to create partial initialisations. This is because the
+# GridSearchCV of scikit-learn will try to initialize the model with the
+# parameters we want to tune. If we do not do this, the GridSearchCV will
+# try to initialize the model with the parameters we want to tune but
+# without the parameters we do not want to tune. This will result in an
+# error.
+model = partial(ShallowFBCSPNet, n_chans, n_classes,
+                input_window_samples=input_window_samples,
+                final_conv_length='auto', )
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# Training
+# --------
+#
+
+
+######################################################################
+# Now we train the network! EEGClassifier is a Braindecode object
+# responsible for managing the training of neural networks. It inherits
+# from `skorch.NeuralNetClassifier <https://skorch.readthedocs.io/
+# en/latest/classifier.html>`__,
+# so the training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+
+from skorch.callbacks import LRScheduler
+from skorch.dataset import ValidSplit
+from braindecode import EEGClassifier
+
+batch_size = 16
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    optimizer__lr=[],  # This will be handled by GridSearchCV
+    batch_size=batch_size,
+    train_split=ValidSplit(0.2, random_state=seed),
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+)
+
+######################################################################
+# We use scikit-learn `GridSearchCV
+# <https://scikit-learn.org/stable/modules/generated/
+# sklearn.model_selection.GridSearchCV.html>`__ to tune hyperparameters.
+# To be able to do this, we slice the braindecode datasets that by default
+# return a 3-tuple to return X and y, respectively.
+#
+
+######################################################################
+# .. note::
+#    The KFold object splits the datasets based on their
+#    length which corresponds to the number of compute windows. In
+#    this (trialwise) example this is fine to do. In a cropped setting
+#    this is not advisable since this might split compute windows
+#    of a single trial into both train and valid set.
+#
+
+from sklearn.model_selection import GridSearchCV, KFold
+from skorch.helper import SliceDataset
+from numpy import array
+import pandas as pd
+
+train_X = SliceDataset(train_set, idx=0)
+train_y = array([y for y in SliceDataset(train_set, idx=1)])
+cv = KFold(n_splits=2, shuffle=True, random_state=42)
+
+learning_rates = [0.00625, 0.0000625]
+drop_probs = [0.2, 0.5, 0.8]
+
+fit_params = {'epochs': n_epochs}
+param_grid = {
+    'optimizer__lr': learning_rates,
+    'module__drop_prob': drop_probs
+}
+
+# By setting n_jobs=-1, grid search is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
+search = GridSearchCV(
+    estimator=clf,
+    param_grid=param_grid,
+    cv=cv,
+    return_train_score=True,
+    scoring='accuracy',
+    refit=True,
+    verbose=1,
+    error_score='raise',
+    n_jobs=1,
+)
+
+search.fit(train_X, train_y, **fit_params)
+
+# Extract the results into a DataFrame
+search_results = pd.DataFrame(search.cv_results_)
+
+######################################################################
+# Plotting the results
+# --------------------
+#
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# Create a pivot table for the heatmap
+pivot_table = search_results.pivot(index='param_optimizer__lr',
+                                   columns='param_module__drop_prob',
+                                   values='mean_test_score')
+# Create the heatmap
+fig, ax = plt.subplots()
+sns.heatmap(pivot_table, annot=True, fmt=".3f",
+            cmap="YlGnBu", cbar=True)
+plt.title('Grid Search Mean Test Scores')
+plt.ylabel('Learning Rate')
+plt.xlabel('Dropout Probability')
+plt.tight_layout()
+plt.show()
+
+###########################################################################
+# Get the best hyperparameters
+# ----------------------------
+#
+best_run = search_results[search_results['rank_test_score'] == 1].squeeze()
+print(
+    f"Best hyperparameters were {best_run['params']} which gave a validation "
+    f"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training "
+    f"accuracy of {best_run['mean_train_score'] * 100:.2f}%).")
+
+eval_X = SliceDataset(eval_set, idx=0)
+eval_y = SliceDataset(eval_set, idx=1)
+score = search.score(eval_X, eval_y)
+print(f"Eval accuracy is {score * 100:.2f}%.")
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,
+#       Zachary Nado (2022). Deep Learning Tuning Playbook.
+#       Github https://github.com/google-research/tuning_playbook
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [4] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py b/stable/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py
new file mode 100644
index 00000000..9cd08ee9
--- /dev/null
+++ b/stable/_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py
@@ -0,0 +1,167 @@
+"""
+Benchmarking preprocessing with parallelization and serialization
+=================================================================
+
+In this example, we compare the execution time and memory requirements of
+preprocessing data with the parallelization and serialization functionalities
+available in :func:`braindecode.preprocessing.preprocess`.
+
+We compare 4 cases:
+
+1. Sequential, no serialization
+2. Sequential, with serialization
+3. Parallel, no serialization
+4. Parallel, with serialization
+
+Case 1 is the simplest approach, in which all recordings in a
+:class:`braindecode.datasets.BaseConcatDataset` are preprocessed one after the
+other. In this scenario, :func:`braindecode.preprocessing.preprocess` acts
+inplace, which means memory usage will likely stay stable (depending on the
+preprocessing operations) if recordings have been preloaded. However, two
+potential issues arise when working with large datasets: (1) if recordings have
+not been preloaded before preprocessing, `preprocess()` will need to load them
+and keep them in memory, in which case memory can become a bottleneck, and (2)
+sequential preprocessing can take a considerable amount of time to run when
+working with many recordings.
+
+A solution to the first issue (memory usage) is to save the preprocessed data
+to a file so it can be cleared from memory before moving on to the next
+recording (case 2). The recordings can then be reloaded with `preload=False`
+once they have all been saved to disk. This enables using the lazy loading
+capabilities of :class:`braindecode.datasets.BaseConcatDataset` and avoids
+potential memory bottlenecks. The downside is that the writing to disk can take
+some time and of course requires disk space.
+
+A solution to the second issue (slow preprocessing) is to parallelize the
+preprocessing over multiple cores whenever possible (case 3). This can speed up
+preprocessing significantly. However, this approach will increase memory usage
+because of the way parallelization is implemented internally (with
+`joblib`, copies of (part of) the data must be made when sending arguments to
+parallel processes).
+
+Finally, case 4 (combining parallelization and serialization) is likely to be
+both fast and memory efficient. As shown in this example, this remains a
+tradeoff though, and the selected configuration should depend on the size of
+the dataset and the specific operations applied to the recordings.
+"""
+
+# Authors: Hubert Banville <hubert.jbanville@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+import time
+import tempfile
+from itertools import product
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import scale
+from memory_profiler import memory_usage
+
+from braindecode.datasets import SleepPhysionet
+from braindecode.preprocessing import (
+    preprocess, Preprocessor, create_fixed_length_windows)
+
+
+###############################################################################
+# We create a function that goes through the usual three steps of data
+# preparation: (1) data loading, (2) continuous data preprocessing,
+# (3) windowing and (4) windowed data preprocessing. We use the
+# :class:`braindecode.datasets.SleepPhysionet` dataset for testing purposes.
+
+
+def prepare_data(n_recs, save, preload, n_jobs):
+    if save:
+        tmp_dir = tempfile.TemporaryDirectory()
+        save_dir = tmp_dir.name
+    else:
+        save_dir = None
+
+    # (1) Load the data
+    concat_ds = SleepPhysionet(
+        subject_ids=range(n_recs), recording_ids=[1], crop_wake_mins=30,
+        preload=preload)
+    sfreq = concat_ds.datasets[0].raw.info['sfreq']
+
+    # (2) Preprocess the continuous data
+    preprocessors = [
+        Preprocessor('crop', tmin=10),
+        Preprocessor('filter', l_freq=None, h_freq=30)
+    ]
+    preprocess(concat_ds, preprocessors, save_dir=save_dir, overwrite=True,
+               n_jobs=n_jobs)
+
+    # (3) Window the data
+    windows_ds = create_fixed_length_windows(
+        concat_ds, 0, None, int(30 * sfreq), int(30 * sfreq), True,
+        preload=preload, n_jobs=n_jobs)
+
+    # Preprocess the windowed data
+    preprocessors = [Preprocessor(scale, channel_wise=True)]
+    preprocess(windows_ds, preprocessors, save_dir=save_dir, overwrite=True,
+               n_jobs=n_jobs)
+
+
+###############################################################################
+# Next, we can run our function and measure its run time and peak memory usage
+# for each one of our 4 cases above. We call the function multiple times with
+# each configuration to get better estimates.
+#
+# .. note::
+#   To better characterize the run time vs. memory usage tradeoff for your
+#   specific configuration (as this will differ based on available hardware,
+#   data size and preprocessing operations), we recommend adapting this example
+#   to your use case and running it on your machine.
+
+n_repets = 2  # Number of repetitions
+all_n_recs = 2  # Number of recordings to load and preprocess
+all_n_jobs = [1, 2]  # Number of parallel processes
+
+results = list()
+for _, n_recs, save, n_jobs in product(
+        range(n_repets), [all_n_recs], [True, False], all_n_jobs):
+
+    start = time.time()
+    mem = max(memory_usage(
+        proc=(prepare_data, [n_recs, save, False, n_jobs], {})))
+    time_taken = time.time() - start
+
+    results.append({
+        'n_recs': n_recs,
+        'max_mem': mem,
+        'save': save,
+        'n_jobs': n_jobs,
+        'time': time_taken
+    })
+
+
+###############################################################################
+# Finally, we can plot the results:
+
+df = pd.DataFrame(results)
+
+fig, ax = plt.subplots(figsize=(6, 4))
+colors = {True: 'tab:orange', False: 'tab:blue'}
+markers = {n: m for n, m in zip(all_n_jobs, ['o', 'x', '.'])}
+for (save, n_jobs), sub_df in df.groupby(['save', 'n_jobs']):
+    ax.scatter(x=sub_df['time'], y=sub_df['max_mem'], color=colors[save],
+               marker=markers[n_jobs], label=f'save={save}, n_jobs={n_jobs}')
+ax.legend()
+ax.set_xlabel('Execution time (s)')
+ax.set_ylabel('Memory usage (MiB)')
+ax.set_title(f'Loading and preprocessing {all_n_recs} recordings from Sleep '
+             'Physionet')
+plt.show()
+
+
+###############################################################################
+# We see that parallel preprocessing without serialization (blue crosses) is
+# faster than simple sequential processing (blue circles), however it uses more
+# memory.
+#
+# Combining parallel preprocessing and serialization (orange crosses) reduces
+# memory usage significantly, however it increases run time by a few seconds.
+# Depending on available resources (e.g. in limited memory settings), it might
+# therefore be more advantageous to use both parallelization and serialization
+# together.
diff --git a/stable/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py b/stable/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py
new file mode 100644
index 00000000..e8d3c186
--- /dev/null
+++ b/stable/_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py
@@ -0,0 +1,409 @@
+"""
+Training a Braindecode model in PyTorch
+=======================================
+
+This tutorial shows you how to train a Braindecode model with PyTorch. The data
+preparation and model instantiation steps are identical to that of the tutorial
+`How to train, test and tune your model <./plot_how_train_test_and_tune.html>`__
+
+We will use the BCIC IV 2a dataset as a showcase example.
+
+The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing,
+defining a model, and other details which are not exclusive to this page (compare
+`Cropped Decoding Tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.
+
+The goal of this tutorial is to present braindecode in the PyTorch perceptive.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Why should I care about model evaluation?
+# -----------------------------------------
+# Short answer: To produce reliable results!
+#
+# In machine learning, we usually follow the scheme of splitting the
+# data into two parts, training and testing sets. It sounds like a
+# simple division, right? But the story does not end here.
+#
+# While developing a ML model you usually have to adjust and tune
+# hyperparameters of your model or pipeline (e.g., number of layers,
+# learning rate, number of epochs). Deep learning models usually have
+# many free parameters; they could be considered complex models with
+# many degrees of freedom. If you kept using the test dataset to
+# evaluate your adjustmentyou would run into data leakage.
+#
+# This means that if you use the test set to adjust the hyperparameters
+# of your model, the model implicitly learns or memorizes the test set.
+# Therefore, the trained model is no longer independent of the test set
+# (even though it was never used for training explicitly!).
+# If you perform any hyperparameter tuning, you need a third split,
+# the so-called validation set.
+#
+# This tutorial shows the three basic schemes for training and evaluating
+# the model as well as two methods to tune your hyperparameters.
+#
+
+######################################################################
+# .. warning::
+#    You might recognize that the accuracy gets better throughout
+#    the experiments of this tutorial. The reason behind that is that
+#    we always use the same model with the same parameters in every
+#    segment to keep the tutorial short and readable. If you do your
+#    own experiments you always have to reinitialize the model before
+#    training.
+#
+
+######################################################################
+# Loading, preprocessing, defining a model, etc.
+# ----------------------------------------------
+#
+
+
+######################################################################
+# Loading the Dataset Structure
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Here, we have a data structure with equal behavior to the Pytorch Dataset.
+
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing, the offline transformation of the raw dataset
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+import numpy as np
+
+from braindecode.preprocessing import (
+    exponential_moving_standardize,
+    preprocess,
+    Preprocessor,
+)
+
+low_cut_hz = 4.0  # low cut frequency for filtering
+high_cut_hz = 38.0  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+
+transforms = [
+    Preprocessor("pick_types", eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(
+        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV
+        factor=1e6,
+    ),
+    Preprocessor("filter", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(
+        exponential_moving_standardize,  # Exponential moving standardization
+        factor_new=factor_new,
+        init_block_size=init_block_size,
+    ),
+]
+
+# Transform the data
+preprocess(dataset, transforms, n_jobs=-1)
+
+######################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~~~~~~~
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info["sfreq"]
+assert all([ds.raw.info["sfreq"] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Create Pytorch model
+# ~~~~~~~~~~~~~~~~~~~~
+#
+
+import torch
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = "cuda" if cuda else "cpu"
+if cuda:
+    torch.backends.cudnn.benchmark = True
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans and time steps from dataset
+n_channels = windows_dataset[0][0].shape[0]
+input_window_samples = windows_dataset[0][0].shape[1]
+
+# The ShallowFBCSPNet is a `nn.Sequential` model
+
+model = ShallowFBCSPNet(
+    n_channels,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length="auto",
+)
+
+# Display torchinfo table describing the model
+print(model)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# How to train and evaluate your model
+# ------------------------------------
+#
+
+######################################################################
+# Split dataset into train and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case the ``session`` column. We
+# select ``Train`` for training and ``test`` for testing.
+# For other datasets, you might have to choose another column.
+#
+# .. note::
+#    No matter which of the three schemes you use, this initial
+#    two-fold split into train_set and test_set always remains the same.
+#    Remember that you are not allowed to use the test_set during any
+#    stage of training or tuning.
+#
+
+splitted = windows_dataset.split("session")
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Option 1: Pure PyTorch training loop
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# .. image:: https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png
+#    :alt: Pytorch logo
+
+
+######################################################################
+# `model` is an instance of `torch.nn.Module`, and can as such be trained
+# using PyTorch optimization capabilities.
+# The following training scheme is simple as the dataset is only
+# split into two distinct sets (``train_set`` and ``test_set``).
+# This scheme uses no separate validation split and should only be
+# used for the final evaluation of the (previously!) found
+# hyperparameters configuration.
+#
+# .. warning::
+#    If you make any use of the ``test_set`` during training
+#    (e.g. by using EarlyStopping) there will be data leakage
+#    which will make the reported generalization capability/decoding
+#    performance of your model less credible.
+#
+# .. warning::
+#    The parameter values showcased here for optimizing the network are
+#    chosen to make this tutorial fast to run and build. Real-world values
+#    would be higher, especially when it comes to n_epochs.
+
+from torch.nn import Module
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
+
+######################################################################
+# The following method runs one training epoch over the dataloader for the
+# given model. It needs a loss function, optimization algorithm, and
+# learning rate updating callback.
+from tqdm import tqdm
+# Define a method for training one epoch
+
+
+def train_one_epoch(
+        dataloader: DataLoader, model: Module, loss_fn, optimizer,
+        scheduler: LRScheduler, epoch: int, device, print_batch_stats=True
+):
+    model.train()  # Set the model to training mode
+    train_loss, correct = 0, 0
+
+    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader),
+                        disable=not print_batch_stats)
+
+    for batch_idx, (X, y, _) in progress_bar:
+        X, y = X.to(device), y.to(device)
+        optimizer.zero_grad()
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        loss.backward()
+        optimizer.step()  # update the model weights
+        optimizer.zero_grad()
+
+        train_loss += loss.item()
+        correct += (pred.argmax(1) == y).sum().item()
+
+        if print_batch_stats:
+            progress_bar.set_description(
+                f"Epoch {epoch}/{n_epochs}, "
+                f"Batch {batch_idx + 1}/{len(dataloader)}, "
+                f"Loss: {loss.item():.6f}"
+            )
+
+    # Update the learning rate
+    scheduler.step()
+
+    correct /= len(dataloader.dataset)
+    return train_loss / len(dataloader), correct
+
+######################################################################
+# Very similarly, the evaluation function loops over the entire dataloader
+# and accumulate the metrics, but doesn't update the model weights.
+
+
+@torch.no_grad()
+def test_model(
+    dataloader: DataLoader, model: Module, loss_fn, print_batch_stats=True
+):
+    size = len(dataloader.dataset)
+    n_batches = len(dataloader)
+    model.eval()  # Switch to evaluation mode
+    test_loss, correct = 0, 0
+
+    if print_batch_stats:
+        progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
+    else:
+        progress_bar = enumerate(dataloader)
+
+    for batch_idx, (X, y, _) in progress_bar:
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        batch_loss = loss_fn(pred, y).item()
+
+        test_loss += batch_loss
+        correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+
+        if print_batch_stats:
+            progress_bar.set_description(
+                f"Batch {batch_idx + 1}/{len(dataloader)}, "
+                f"Loss: {batch_loss:.6f}"
+            )
+
+    test_loss /= n_batches
+    correct /= size
+
+    print(
+        f"Test Accuracy: {100 * correct:.1f}%, Test Loss: {test_loss:.6f}\n"
+    )
+    return test_loss, correct
+
+
+# Define the optimization
+optimizer = torch.optim.AdamW(model.parameters(),
+                              lr=lr, weight_decay=weight_decay)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
+                                                       T_max=n_epochs - 1)
+# Define the loss function
+# We used the NNLoss function, which expects log probabilities as input
+# (which is the case for our model output)
+loss_fn = torch.nn.NLLLoss()
+
+# train_set and test_set are instances of torch Datasets, and can seamlessly be
+# wrapped in data loaders.
+train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)
+test_loader = DataLoader(test_set, batch_size=batch_size)
+
+for epoch in range(1, n_epochs + 1):
+    print(f"Epoch {epoch}/{n_epochs}: ", end="")
+
+    train_loss, train_accuracy = train_one_epoch(
+        train_loader, model, loss_fn, optimizer, scheduler, epoch, device,
+    )
+
+    test_loss, test_accuracy = test_model(test_loader, model, loss_fn)
+
+    print(
+        f"Train Accuracy: {100 * train_accuracy:.2f}%, "
+        f"Average Train Loss: {train_loss:.6f}, "
+        f"Test Accuracy: {100 * test_accuracy:.1f}%, "
+        f"Average Test Loss: {test_loss:.6f}\n"
+    )
+
+
+######################################################################
+# Option 2: Train it with PyTorch Lightning
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# .. image:: https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png
+#    :alt: Pytorch Lightning logo
+
+######################################################################
+# Alternatively, lightning provides a nice interface around torch modules
+# which integrates the previous logic.
+
+
+import lightning as L
+from torchmetrics.functional import accuracy
+
+
+class LitModule(L.LightningModule):
+    def __init__(self, module):
+        super().__init__()
+        self.module = module
+        self.loss = torch.nn.NLLLoss()
+
+    def training_step(self, batch, batch_idx):
+        x, y, _ = batch
+        y_hat = self.module(x)
+        loss = self.loss(y_hat, y)
+        self.log("train_loss", loss)
+        return loss
+
+    def test_step(self, batch, batch_idx):
+        x, y, _ = batch
+        y_hat = self.module(x)
+        loss = self.loss(y_hat, y)
+        acc = accuracy(y_hat, y, "multiclass", num_classes=4)
+        metrics = {"test_acc": acc, "test_loss": loss}
+        self.log_dict(metrics)
+        return metrics
+
+    def configure_optimizers(self):
+        optimizer = torch.optim.AdamW(model.parameters(), lr=lr,
+                                      weight_decay=weight_decay)
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
+                                                               T_max=n_epochs - 1)
+        return [optimizer], [scheduler]
+
+
+# Creating the trainer with max_epochs=2 for demonstration purposes
+trainer = L.Trainer(max_epochs=n_epochs)
+# Create and train the LightningModule
+lit_model = LitModule(model)
+trainer.fit(lit_model, train_loader)
+
+# After training, you can test the model using the test DataLoader
+trainer.test(dataloaders=test_loader)
diff --git a/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
index a1470f5a02455f89861d287a36660de4096d4d6d..b7afa5cd8ee88cfb6bf1a693a7a3c3acfa0d24e6 100644
GIT binary patch
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diff --git a/stable/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py b/stable/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py
new file mode 100644
index 00000000..d11375ef
--- /dev/null
+++ b/stable/_downloads/71d05f0d0d0121cf9eb0a58dd5404e6e/plot_how_train_test_and_tune.py
@@ -0,0 +1,635 @@
+"""
+How to train, test and tune your model?
+=======================================
+
+This tutorial shows you how to properly train, tune and test your deep learning
+models with Braindecode. We will use the BCIC IV 2a dataset [1]_ as a showcase example.
+
+The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing of data,
+defining a model, and other details which are not exclusive to this page (see
+`Cropped Decoding Tutorial <./plot_bcic_iv_2a_moabb_cropped.html>`__). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.
+
+In general, we distinguish between "usual" training and evaluation and hyperparameter search.
+The tutorial is therefore split into two parts, one for the three different training schemes
+and one for the two different hyperparameter tuning methods.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+######################################################################
+# Why should I care about model evaluation?
+# -----------------------------------------
+# Short answer: To produce reliable results!
+#
+# In machine learning, we usually follow the scheme of splitting the
+# data into two parts, training and testing sets. It sounds like a
+# simple division, right? But the story does not end here.
+#
+# - What are model's parameters?
+#
+# Model's parameters are learnable weights which are used in the
+# extraction of the relevant features and in performing the final inference.
+# In the context of deep learning, these are usually fully connected weights,
+# convolutional kernels, biases, etc.
+#
+# - What are model's hyperparameters?
+#
+# Model's hyperparameters are used to set the capacity (size) of the model
+# and to guide the parameter learning process.
+# In the context of deep learning, examples of the hyperparameters are the
+# number of convolutional layers and the number of convolutional kernels in
+# each of them, the number and size of the fully connected weights,
+# choice of the optimizer and its learning rate, the number of training epochs,
+# choice of the nonlinearities, etc.
+#
+#
+# While developing a ML model you usually have to adjust and tune
+# hyperparameters of your model or pipeline (e.g., number of layers,
+# learning rate, number of epochs). Deep learning models usually have
+# many free parameters; they could be considered as complex models with
+# many degrees of freedom. If you kept using the test dataset to
+# evaluate your adjustment, you would run into data leakage.
+#
+# This means that if you use the test set to adjust the hyperparameters
+# of your model, the model implicitly learns or memorizes the test set.
+# Therefore, the trained model is no longer independent of the test set
+# (even though it was never used for training explicitly!).
+# If you perform any hyperparameter tuning, you need a third split,
+# the so-called validation set.
+#
+# This tutorial shows the three basic schemes for training and evaluating
+# the model as well as two methods to tune your hyperparameters.
+#
+
+######################################################################
+# .. warning::
+#    You might recognize that the accuracy gets better throughout
+#    the experiments of this tutorial. The reason behind that is that
+#    we always use the same model with the same parameters in every
+#    segment to keep the tutorial short and readable. If you do your
+#    own experiments you always have to reinitialize the model before
+#    training.
+#
+
+######################################################################
+# Loading and preprocessing of data, defining a model, etc.
+# ----------------------------------------------------------
+#
+#
+
+######################################################################
+# Loading data
+# ~~~~~~~~~~~~~
+#
+# In this example, we load the BCI Competition IV 2a data [1]_, for one
+# subject (subject id 3), using braindecode's wrapper to load via
+# `MOABB library <https://github.com/NeuroTechX/moabb>`__ [2]_.
+#
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing data
+# ~~~~~~~~~~~~~~~~~~
+#
+# In this example, preprocessing includes signal rescaling, the bandpass filtering
+# (low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+# the exponential moving mean and variance.
+#
+import numpy as np
+
+from braindecode.preprocessing import (
+    exponential_moving_standardize,
+    preprocess,
+    Preprocessor,
+)
+
+low_cut_hz = 4.0  # low cut frequency for filtering
+high_cut_hz = 38.0  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+
+preprocessors = [
+    Preprocessor("pick_types", eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(
+        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV
+        factor=1e6,
+    ),
+    Preprocessor("filter", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(
+        exponential_moving_standardize,  # Exponential moving standardization
+        factor_new=factor_new,
+        init_block_size=init_block_size,
+    ),
+]
+
+# Preprocess the data
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Extraction of the Windows
+# ~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Extraction of the trials (windows) from the time series is based on the
+# events inside the dataset. One event is the demarcation of the stimulus or
+# the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+# before the corresponding event and the duration of the event itself.
+# #Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the
+# ``trial_stop_offset_seconds`` to 0 [s].
+#
+# We extract from the dataset the sampling frequency, which is the same for
+# all datasets in this case, and we tested it.
+#
+# .. note::
+#    The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are
+#    defined in seconds and need to be converted into samples (multiplication
+#    with the sampling frequency), relative to the event.
+#    This variable is dataset dependent.
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info["sfreq"]
+assert all([ds.raw.info["sfreq"] == sfreq for ds in dataset.datasets])
+# Calculate the window start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to
+# define how windows should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+
+######################################################################
+# Split dataset into train and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# We can easily split the dataset BCIC IV 2a dataset using additional
+# info stored in the description attribute, in this case the ``session``
+# column. We select ``0train`` for training and ``0test`` for testing.
+# For other datasets, you might have to choose another column and/or column.
+#
+# .. note::
+#    No matter which of the three schemes you use, this initial
+#    two-fold split into train_set and test_set always remains the same.
+#    Remember that you are not allowed to use the test_set during any
+#    stage of training or tuning.
+#
+
+splitted = windows_dataset.split("session")
+train_set = splitted['0train']  # Session train
+test_set = splitted['1test']  # Session evaluation
+
+
+######################################################################
+# Create model
+# ~~~~~~~~~~~~
+#
+# In this tutorial, ShallowFBCSPNet classifier [3]_ is explored. The model
+# training is performed on GPU if it exists, otherwise on CPU.
+#
+
+import torch
+from braindecode.util import set_random_seeds
+from braindecode.models import ShallowFBCSPNet
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = "cuda" if cuda else "cpu"
+if cuda:
+    torch.backends.cudnn.benchmark = True
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans and time steps from dataset
+n_channels = windows_dataset[0][0].shape[0]
+input_window_samples = windows_dataset[0][0].shape[1]
+
+model = ShallowFBCSPNet(
+    n_channels,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length="auto",
+)
+
+# Display torchinfo table describing the model
+print(model)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# How to train and evaluate your model
+# ------------------------------------
+#
+
+######################################################################
+# Option 1: Simple Train-Test Split
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# This is the easiest training scheme to use as the dataset is only
+# split into two distinct sets (``train_set`` and ``test_set``).
+# This scheme uses no separate validation split and should only be
+# used for the final evaluation of the (previously!) found
+# hyperparameters configuration.
+#
+# .. warning::
+#    If you make any use of the ``test_set`` during training
+#    (e.g. by using EarlyStopping) there will be data leakage
+#    which will make the reported generalization capability/decoding
+#    performance of your model less credible.
+#
+
+from skorch.callbacks import LRScheduler
+
+from braindecode import EEGClassifier
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=None,
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+    max_epochs=n_epochs,
+)
+# Model training for a specified number of epochs. `y` is None as it is already supplied
+# in the dataset.
+clf.fit(train_set, y=None)
+
+# evaluated the model after training
+y_test = test_set.get_metadata().target
+test_acc = clf.score(test_set, y=y_test)
+print(f"Test acc: {(test_acc * 100):.2f}%")
+
+######################################################################
+# Let's visualize the first option with a util function.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The following figure illustrates split of entire dataset into the
+# training and testing subsets.
+#
+#
+import seaborn as sns
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+
+sns.set(font_scale=1.5)
+
+
+def plot_simple_train_test(ax, all_dataset, train_set, test_set):
+    """Create a sample plot for training-testing split."""
+    bd_cmap = ["#3A6190", "#683E00", "#DDF2FF", "#2196F3"]
+
+    ax.barh("Original\ndataset", len(all_dataset), left=0,
+            height=0.5, color=bd_cmap[0])
+    ax.barh("Train-Test\nsplit", len(train_set), left=0,
+            height=0.5, color=bd_cmap[1])
+    ax.barh("Train-Test\nsplit", len(test_set), left=len(train_set),
+            height=0.5, color=bd_cmap[2])
+
+    ax.invert_yaxis()
+    ax.set(xlabel="Number of samples.", title="Train-Test split")
+    ax.legend(["Original set", "Training set", "Testing set"], loc='lower center',
+              ncols=4, bbox_to_anchor=(0.5, 0.5))
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
+    return ax
+
+
+fig, ax = plt.subplots(figsize=(12, 8))
+plot_simple_train_test(ax=ax, all_dataset=windows_dataset,
+                       train_set=train_set, test_set=test_set)
+
+######################################################################
+# Option 2: Train-Val-Test Split
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# When evaluating different settings hyperparameters for your model,
+# there is still a risk of overfitting on the test set because the
+# parameters can be tweaked until the estimator performs optimally.
+# For more information visit `sklearns Cross-Validation Guide
+# <https://scikit-learn.org/stable/modules/cross_validation.html>`__.
+# This second option splits the original ``train_set`` into two distinct
+# sets, the training set and the validation set to avoid overfitting
+# the hyperparameters to the test set.
+#
+# .. note::
+#    If your dataset is really small, the validation split can become
+#    quite small. This may lead to unreliable tuning results. To
+#    avoid this, either use Option 3 or adjust the split ratio.
+#
+# To split the ``train_set`` we will make use of the
+# ``train_split`` argument of ``EEGClassifier``. If you leave this empty
+# (not None!), skorch will make an 80-20 train-validation split.
+# If you want to control the split manually you can do that by using
+# ``Subset`` from torch and ``predefined_split`` from skorch.
+#
+
+from torch.utils.data import Subset
+from sklearn.model_selection import train_test_split
+from skorch.helper import predefined_split, SliceDataset
+
+X_train = SliceDataset(train_set, idx=0)
+y_train = np.array([y for y in SliceDataset(train_set, idx=1)])
+train_indices, val_indices = train_test_split(
+    X_train.indices_, test_size=0.2, shuffle=False
+)
+train_subset = Subset(train_set, train_indices)
+val_subset = Subset(train_set, val_indices)
+######################################################################
+# .. note::
+#    The parameter ``shuffle`` is set to ``False``. For time-series
+#    data this should always be the case as shuffling might take
+#    advantage of correlated samples, which would make the validation
+#    performance less meaningful.
+#
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(val_subset),
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+    max_epochs=n_epochs,
+)
+clf.fit(train_subset, y=None)
+
+# evaluate the model after training and validation
+y_test = test_set.get_metadata().target
+test_acc = clf.score(test_set, y=y_test)
+print(f"Test acc: {(test_acc * 100):.2f}%")
+
+######################################################################
+# Let's visualize the second option with a util function.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# The following figure illustrates split of entire dataset into the
+# training, validation and testing subsets.
+# ``Making more compact plot_train_valid_test function.``
+#
+
+
+def plot_train_valid_test(ax, all_dataset, train_subset, val_subset, test_set):
+    """Create a sample plot for training, validation, testing."""
+
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
+
+    n_train, n_val, n_test = len(train_subset), len(val_subset), len(test_set)
+    ax.barh("Original\ndataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
+    ax.barh("Train-Test-Valid\nsplit", n_train, left=0, height=0.5, color=bd_cmap[1])
+    ax.barh("Train-Test-Valid\nsplit", n_val, left=n_train, height=0.5, color=bd_cmap[2])
+    ax.barh("Train-Test-Valid\nsplit", n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])
+
+    ax.invert_yaxis()
+    ax.set(xlabel="Number of samples.", title="Train-Test-Valid split")
+    ax.legend(["Original set", "Training set", "Validation set", "Testing set"],
+              loc="lower center", ncols=2, bbox_to_anchor=(0.5, 0.4))
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
+    return ax
+
+
+fig, ax = plt.subplots(figsize=(12, 5))
+plot_train_valid_test(ax=ax, all_dataset=windows_dataset,
+                      train_subset=train_subset, val_subset=val_subset, test_set=test_set,)
+
+######################################################################
+# Option 3: k-Fold Cross Validation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# As mentioned above, using only one validation split might not be
+# sufficient, as there might be a shift in the data distribution.
+# To compensate for this, one can run a k-fold Cross Validation,
+# where every sample of the training set is in the validation set once.
+# After averaging over the k validation scores afterwards, you get a
+# very reliable estimate of how the model would perform on unseen
+# data (test set).
+#
+# .. note::
+#    This k-Fold Cross Validation can be used without a separate
+#    (holdout) test set. If there is no test set available, e.g. in a
+#    competition, this scheme is highly recommended to get a reliable
+#    estimate of the generalization performance.
+#
+# To implement this, we will make use of sklearn function
+# `cross_val_score <https://scikit-learn.org/stable/modules/generated/
+# sklearn.model_selection.cross_val_score.html>`__ and the `KFold
+# <https://scikit-learn.org/stable/modules/generated/sklearn.model_
+# selection.KFold.html>`__. CV splitter.
+# The ``train_split`` argument has to be set to ``None``, as sklearn
+# will take care of the splitting.
+#
+from skorch.callbacks import LRScheduler
+
+from braindecode import EEGClassifier
+
+from sklearn.model_selection import KFold, cross_val_score
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=None,
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler("CosineAnnealingLR", T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+    max_epochs=n_epochs,
+)
+
+train_val_split = KFold(n_splits=5, shuffle=False)
+# By setting n_jobs=-1, cross-validation is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
+cv_results = cross_val_score(
+    clf, X_train, y_train, scoring="accuracy", cv=train_val_split, n_jobs=1
+)
+print(
+    f"Validation accuracy: {np.mean(cv_results * 100):.2f}"
+    f"+-{np.std(cv_results * 100):.2f}%"
+)
+
+######################################################################
+# Let's visualize the third option with a util function.
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+def plot_k_fold(ax, cv, all_dataset, X_train, y_train, test_set):
+    """Create a sample plot for training, validation, testing."""
+
+    bd_cmap = ["#3A6190", "#683E00", "#2196F3", "#DDF2FF", ]
+
+    ax.barh("Original\nDataset", len(all_dataset), left=0, height=0.5, color=bd_cmap[0])
+
+    # Generate the training/validation/testing data fraction visualizations for each CV split
+    for ii, (tr_idx, val_idx) in enumerate(cv.split(X=X_train, y=y_train)):
+        n_train, n_val, n_test = len(tr_idx), len(val_idx), len(test_set)
+        n_train2 = n_train + n_val - max(val_idx) - 1
+        ax.barh("cv" + str(ii + 1), min(val_idx), left=0, height=0.5, color=bd_cmap[1])
+        ax.barh("cv" + str(ii + 1), n_val, left=min(val_idx), height=0.5, color=bd_cmap[2])
+        ax.barh("cv" + str(ii + 1), n_train2, left=max(val_idx) + 1, height=0.5, color=bd_cmap[1])
+        ax.barh("cv" + str(ii + 1), n_test, left=n_train + n_val, height=0.5, color=bd_cmap[3])
+
+    ax.invert_yaxis()
+    ax.set_xlim([-int(0.1 * len(all_dataset)), int(1.1 * len(all_dataset))])
+    ax.set(xlabel="Number of samples.", title="KFold Train-Test-Valid split")
+    ax.legend([Patch(color=bd_cmap[i]) for i in range(4)],
+              ["Original set", "Training set", "Validation set", "Testing set"],
+              loc="lower center", ncols=2)
+    ax.text(-0.07, 0.45, 'Train-Valid-Test split', rotation=90,
+            verticalalignment='center', horizontalalignment='left', transform=ax.transAxes)
+    return ax
+
+
+fig, ax = plt.subplots(figsize=(15, 7))
+plot_k_fold(ax, cv=train_val_split, all_dataset=windows_dataset,
+            X_train=X_train, y_train=y_train, test_set=test_set,)
+
+
+######################################################################
+# How to tune your hyperparameters
+# --------------------------------
+#
+
+######################################################################
+# One way to do hyperparameter tuning is to run each configuration
+# manually (via Option 2 or 3 from above) and compare the validation
+# performance afterwards. In the early stages of your development
+# process this might be sufficient to get a rough understanding of
+# how your hyperparameter should look like for your model to converge.
+# However, this manual tuning process quickly becomes messy as the
+# number of hyperparameters you want to (jointly) tune increases.
+# Therefore you should, automate this process. We will present two
+# different options, analogous to Option 2 and 3 from above.
+#
+
+######################################################################
+# Option 1: Train-Val-Test Split
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# We will again make use of the `sklearn <https://scikit-learn.org/stable/>`__
+# library to do the hyperparameter search. `GridSearchCV
+# <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>`__
+# will perform a Grid Search over the parameters specified in ``param_grid``.
+# We use grid search for the model selection as a simple example, but you can use other strategies
+# as well.
+# (`List of the sklearn classes for model selection
+# <https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection>`__.)
+#
+
+import pandas as pd
+from sklearn.model_selection import GridSearchCV
+
+train_val_split = [
+    tuple(train_test_split(X_train.indices_, test_size=0.2, shuffle=False))
+]
+
+param_grid = {
+    "optimizer__lr": [0.00625, 0.000625],
+}
+
+# By setting n_jobs=-1, grid search is performed
+# with all the processors, in this case the output of the training
+# process is not printed sequentially
+search = GridSearchCV(
+    estimator=clf,
+    param_grid=param_grid,
+    cv=train_val_split,
+    return_train_score=True,
+    scoring="accuracy",
+    refit=True,
+    verbose=1,
+    error_score="raise",
+    n_jobs=1,
+)
+
+search.fit(X_train, y_train)
+search_results = pd.DataFrame(search.cv_results_)
+
+best_run = search_results[search_results["rank_test_score"] == 1].squeeze()
+
+best_parameters = best_run["params"]
+
+
+######################################################################
+# Option 2: k-Fold Cross Validation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+######################################################################
+# To perform a full k-Fold CV just replace ``train_val_split`` from
+# above with the `KFold <https://scikit-learn.org/stable/modules/generated/
+# sklearn.model_selection.KFold.html>`__ cross-validator from sklearn.
+
+train_val_split = KFold(n_splits=5, shuffle=False)
+
+#######################################################################
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Jayaram, Vinay, and Alexandre Barachant.
+#        "MOABB: trustworthy algorithm benchmarking for BCIs."
+#        Journal of neural engineering 15.6 (2018): 066011.
+#
+# .. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/748680401b0360239d00624375dd9587/plot_bcic_iv_2a_moabb_trial.ipynb b/stable/_downloads/748680401b0360239d00624375dd9587/plot_bcic_iv_2a_moabb_trial.ipynb
deleted file mode 100644
index 27a03b1f..00000000
--- a/stable/_downloads/748680401b0360239d00624375dd9587/plot_bcic_iv_2a_moabb_trial.ipynb
+++ /dev/null
@@ -1,257 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Trialwise Decoding on BCIC IV 2a Dataset\n\nThis tutorial shows you how to train and test deep learning models with\nBraindecode in a classical EEG setting: you have trials of data with\nlabels (e.g., Right Hand, Left Hand, etc.).\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Loading and preprocessing the dataset\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Loading\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, we load the data. In this tutorial, we use the functionality of\nbraindecode to load datasets through\n[MOABB](https://github.com/NeuroTechX/moabb)_ to load the BCI\nCompetition IV 2a data.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Preprocessing\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Cut Compute Windows\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we cut out compute windows, the inputs for the deep networks during\ntraining. In the case of trialwise decoding, we just have to decide if\nwe want to cut out some part before and/or after the trial. For this\ndataset, in our work, it often was beneficial to also cut out 500 ms\nbefore the trial.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split dataset into train and valid\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``session_T`` for training and ``session_E`` for validation.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "splitted = windows_dataset.split('session')\ntrain_set = splitted['session_T']\nvalid_set = splitted['session_E']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create model\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Training\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we train the network! EEGClassifier is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom skorch.NeuralNetClassifier, so the training logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note**: In this tutorial, we use some default parameters that we\nhave found to work well for motor decoding, however we strongly\nencourage you to perform your own hyperparameter optimization using\ncross validation on your training data.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\n# These values we found good for shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\", (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n# Model training for a specified number of epochs. `y` is None as it is already supplied\n# in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Plot Results\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we use the history stored by Skorch throughout training to plot\naccuracy and loss curves.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import matplotlib.pyplot as plt\nfrom matplotlib.lines import Line2D\nimport pandas as pd\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nplt.style.use('seaborn')\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Plot Confusion Matrix\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Generate a confusion matrix as in https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.metrics import confusion_matrix\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = valid_set.datasets[0].windows.event_id.items()\n# sort the labels by values (values are integer class labels)\nlabels = list(dict(sorted(list(label_dict), key=lambda kv: kv[1])).keys())\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb b/stable/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb
new file mode 100644
index 00000000..5410fb8e
--- /dev/null
+++ b/stable/_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb
@@ -0,0 +1,212 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Convolutional neural network regression model on fake data.\n\nThis example shows how to create a CNN regressor from a CNN classifier by removing `softmax`\nfunction from the classifier's output layer and how to train it on a fake regression dataset.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#          Sara Sedlar <sara.sedlar@gmail.com>\n# License: BSD-3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fake regression data\nFunction for generation of the fake regression dataset generates `n_fake_recs` recordings,\neach containing sinusoidal signals with Gaussian noise. Each fake recording signal has\n`n_fake_chs` channels, it lasts `fake_duration` [s] and it is sampled with `fake_sfreq` [Hz].\nThe recordings are split into train, validation and testing sessions.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nimport pandas as pd\n\nfrom braindecode.datasets import BaseConcatDataset, BaseDataset\nfrom braindecode.util import create_mne_dummy_raw"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Function for generating fake regression data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq,\n                            fake_duration, n_fake_targets,\n                            fake_data_split=[0.6, 0.2, 0.2]):\n    \"\"\"Generate a fake regression dataset.\n\n    Parameters\n    ----------\n    n_fake_recs : int\n        Number of fake recordings.\n    n_fake_chs : int\n        Number of fake EEG channels.\n    fake_sfreq : float\n        Fake sampling frequency in Hz.\n    fake_duration : float\n        Fake recording duration in seconds.\n    n_fake_targets : int\n        Number of targets.\n    fake_data_split : list\n        List of train/valid/test subset fractions.\n\n    Returns\n    -------\n    dataset : BaseConcatDataset object\n        The generated dataset object.\n    \"\"\"\n\n    datasets = []\n    for i in range(n_fake_recs):\n        if i < int(fake_data_split[0] * n_fake_recs):\n            target_subset = \"train\"\n        elif i < int((1 - fake_data_split[2]) * n_fake_recs):\n            target_subset = \"valid\"\n        else:\n            target_subset = \"test\"\n        raw, _ = create_mne_dummy_raw(n_channels=n_fake_chs,\n                                      n_times=fake_duration * fake_sfreq,\n                                      sfreq=fake_sfreq)\n\n        target = np.random.randint(0, 10, n_fake_targets)\n        for j in range(n_fake_targets):\n            x = np.sin(2 * np.pi * target[j] * raw.times)\n            raw._data += np.expand_dims(x, axis=0)\n\n        if n_fake_targets == 1:\n            target = target[0]\n        fake_description = pd.Series(data=[target, target_subset],\n                                     index=[\"target\", \"session\"])\n        datasets.append(\n            BaseDataset(raw, fake_description, target_name=\"target\"))\n\n    return BaseConcatDataset(datasets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Generating fake regression dataset\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "n_fake_rec = 20\nn_fake_chans = 21\nfake_sfreq = 100\nfake_duration = 30\nn_fake_targets = 1\ndataset = fake_regression_dataset(n_fake_recs=n_fake_rec,\n                                  n_fake_chs=n_fake_chans,\n                                  fake_sfreq=fake_sfreq,\n                                  fake_duration=fake_duration,\n                                  n_fake_targets=n_fake_targets)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Defining a CNN regression model\n\nChoosing and defining a CNN classifier, `ShallowFBCSPNet` or `Deep4Net`, introduced in [1]_.\nTo convert a classifier to a regressor, `softmax` function is removed from its output layer.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.util import set_random_seeds\nfrom braindecode.models import Deep4Net\nfrom braindecode.models import ShallowFBCSPNet\nimport torch\n\n# Choosing a CNN model\nmodel_name = \"shallow\"  # 'shallow' or 'deep'\n\n# Defining a CNN model\nif model_name in [\"shallow\", \"Shallow\", \"ShallowConvNet\"]:\n    model = ShallowFBCSPNet(in_chans=n_fake_chans,\n                            n_classes=n_fake_targets,\n                            input_window_samples=fake_sfreq * fake_duration,\n                            n_filters_time=40, n_filters_spat=40,\n                            final_conv_length=35,\n                            add_log_softmax=False,)\nelif model_name in [\"deep\", \"Deep\", \"DeepConvNet\"]:\n    model = Deep4Net(in_chans=n_fake_chans, n_classes=n_fake_targets,\n                     input_window_samples=fake_sfreq * fake_duration,\n                     n_filters_time=25, n_filters_spat=25,\n                     stride_before_pool=True,\n                     n_filters_2=n_fake_chans * 2,\n                     n_filters_3=n_fake_chans * 4,\n                     n_filters_4=n_fake_chans * 8,\n                     final_conv_length=1,\n                     add_log_softmax=False, )\nelse:\n    raise ValueError(f'{model_name} unknown')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Choosing between GPU and CPU processors\nBy default, model's training and evaluation take place at GPU if it exists, otherwise on CPU.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "cuda = torch.cuda.is_available()\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\n# Setting a random seed\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data windowing\nWindowing data with a sliding window into the epochs of the size `window_size_samples`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models.util import to_dense_prediction_model, get_output_shape\nfrom braindecode.preprocessing import create_fixed_length_windows\n\nwindow_size_samples = fake_sfreq * fake_duration // 3\nto_dense_prediction_model(model)\nn_preds_per_input = get_output_shape(model, n_fake_chans, window_size_samples)[\n    2]\nwindows_dataset = create_fixed_length_windows(dataset,\n                                              start_offset_samples=0,\n                                              stop_offset_samples=0,\n                                              window_size_samples=window_size_samples,\n                                              window_stride_samples=n_preds_per_input,\n                                              drop_last_window=False,\n                                              preload=True)\n\n# Splitting windowed data into train, valid and test subsets.\nsplits = windows_dataset.split(\"session\")\ntrain_set = splits[\"train\"]\nvalid_set = splits[\"valid\"]\ntest_set = splits[\"test\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model training\nModel is trained by minimizing MSE loss between ground truth and estimated value averaged over\na period of time using AdamW optimizer [2]_, [3]_. Learning rate is managed by CosineAnnealingLR\nlearning rate scheduler.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode import EEGRegressor\nfrom braindecode.training.losses import CroppedLoss\nfrom skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nbatch_size = 4\nn_epochs = 3\noptimizer_lr = 0.001\noptimizer_weight_decay = 0.0\nregressor = EEGRegressor(model, cropped=True,\n                         criterion=CroppedLoss,\n                         criterion__loss_function=torch.nn.functional.mse_loss,\n                         optimizer=torch.optim.AdamW,\n                         optimizer__lr=optimizer_lr,\n                         optimizer__weight_decay=optimizer_weight_decay,\n                         train_split=predefined_split(valid_set),\n                         iterator_train__shuffle=True,\n                         batch_size=batch_size,\n                         callbacks=[\"neg_root_mean_squared_error\",\n                                    (\"lr_scheduler\",\n                                     LRScheduler('CosineAnnealingLR',\n                                                 T_max=n_epochs - 1))],\n                         device=device, )\nregressor.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model evaluation\nPlotting training and validation losses and negative root mean square error\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 5))\naxes[0].set_title(\"Train and valid losses\")\naxes[0].plot(regressor.history[:, \"train_loss\"])\naxes[0].plot(regressor.history[:, \"valid_loss\"])\naxes[0].set_xlabel(\"Epochs\")\naxes[0].set_ylabel(\"Cropped MSE loss\")\naxes[0].legend([\"Train\", \"Valid\"])\n\naxes[1].set_title(\"Train and valid errors\")\naxes[1].plot(regressor.history[:, \"train_neg_root_mean_squared_error\"])\naxes[1].plot(regressor.history[:, \"valid_neg_root_mean_squared_error\"])\naxes[1].set_xlabel(\"Epochs\")\naxes[1].set_ylabel(\"Negative RMSE\")\naxes[1].legend([\"Train\", \"Valid\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model testing\nPlotting a scatter plot of estimated versus target values and corresponding trend line.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "fig, axes = plt.subplots(1, 1, figsize=(5, 5))\ny_estim = np.ravel(regressor.predict(test_set))\ny_gt = test_set.get_metadata()[\"target\"].to_numpy()\n\n_ = axes.scatter(y_gt, y_estim)\n_ = axes.set_ylabel(\"Estimated targets.\")\n_ = axes.set_xlabel(\"Ground truth targets.\")\n\nz = np.polyfit(y_gt, y_estim, 1)\np = np.poly1d(z)\nplt.plot(y_gt, p(y_gt), \"r--\")\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., ... & Ball, T. (2017).\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Human brain mapping, 38(11), 5391-5420.\n\n.. [2] Kingma, Diederik P., and Jimmy Ba.\n       \"Adam: A method for stochastic optimization.\" arXiv preprint arXiv:1412.6980 (2014).\n\n.. [3] Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.\n       \"On the convergence of adam and beyond.\" arXiv preprint arXiv:1904.09237 (2019).\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/76b6af988880f614494e6948dcfe4128/plot_load_save_datasets.py b/stable/_downloads/76b6af988880f614494e6948dcfe4128/plot_load_save_datasets.py
new file mode 100644
index 00000000..db39651c
--- /dev/null
+++ b/stable/_downloads/76b6af988880f614494e6948dcfe4128/plot_load_save_datasets.py
@@ -0,0 +1,97 @@
+"""
+Load and save dataset example
+=============================
+
+In this example, we show how to load and save braindecode datasets.
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+import tempfile
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import preprocess, Preprocessor
+from braindecode.datautil import load_concat_dataset
+from braindecode.preprocessing import create_windows_from_events
+
+
+###############################################################################
+# First, we load some dataset using MOABB.
+dataset = MOABBDataset(
+    dataset_name='BNCI2014001',
+    subject_ids=[1],
+)
+
+###############################################################################
+# We can apply preprocessing steps to the dataset. It is also possible to skip
+# this step and not apply any preprocessing.
+preprocess(
+    concat_ds=dataset,
+    preprocessors=[Preprocessor(fn='resample', sfreq=10)]
+)
+
+###############################################################################
+# We save the dataset to a an existing directory. It will create a '.fif' file
+# for every dataset in the concat dataset. Additionally it will create two
+# JSON files, the first holding the description of the dataset, the second
+# holding the name of the target. If you want to store to the same directory
+# several times, for example due to trying different preprocessing, you can
+# choose to overwrite the existing files.
+
+tmpdir = tempfile.mkdtemp()  # write in a temporary directory
+dataset.save(
+    path=tmpdir,
+    overwrite=False,
+)
+
+##############################################################################
+# We load the saved dataset from a directory. Signals can be preloaded in
+# compliance with mne. Optionally, only specific '.fif' files can be loaded
+# by specifying their ids. The target name can be changed, if the dataset
+# supports it (TUHAbnormal for example supports 'pathological', 'age', and
+# 'gender'. If you stored a preprocessed version with target 'pathological'
+# it is possible to change the target upon loading).
+dataset_loaded = load_concat_dataset(
+    path=tmpdir,
+    preload=True,
+    ids_to_load=[1, 3],
+    target_name=None,
+)
+
+##############################################################################
+# The serialization utility also supports WindowsDatasets, so we create
+# compute windows next.
+windows_dataset = create_windows_from_events(
+    concat_ds=dataset_loaded,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+)
+
+windows_dataset.description
+
+##############################################################################
+# Again, we save the dataset to an existing directory. It will create a
+# '-epo.fif' file for every dataset in the concat dataset. Additionally it
+# will create a JSON file holding the description of the dataset. If you
+# want to store to the same directory several times, for example due to
+# trying different windowing parameters, you can choose to overwrite the
+# existing files.
+windows_dataset.save(
+    path=tmpdir,
+    overwrite=True,
+)
+
+##############################################################################
+# Load the saved dataset from a directory. Signals can be preloaded in
+# compliance with mne. Optionally, only specific '-epo.fif' files can be
+# loaded by specifying their ids.
+windows_dataset_loaded = load_concat_dataset(
+    path=tmpdir,
+    preload=False,
+    ids_to_load=[0],
+    target_name=None,
+)
+
+windows_dataset_loaded.description
diff --git a/stable/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb b/stable/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb
new file mode 100644
index 00000000..b134cc81
--- /dev/null
+++ b/stable/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb
@@ -0,0 +1,291 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Sleep staging on the Sleep Physionet dataset using Eldele2021\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We use the attention-based model from [1]_ with the time distributed approach of [2]_\nto learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset [3]_ [4]_.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Divyesh Narayanan <divyesh.narayanan@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading\n\nFirst, we load the data using the\n:class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load\ntwo recordings from two different individuals: we will use the first one to\ntrain our network and the second one to evaluate performance (as in the `MNE`_\nsleep staging example).\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from numbers import Integral\nfrom braindecode.datasets import SleepPhysionet\n\nsubject_ids = [0, 1]\ncrop = (0, 30 * 400)  # we only keep 400 windows of 30s to speed example\ndataset = SleepPhysionet(\n    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30,\n    crop=crop)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nNext, we preprocess the raw data. We convert the data to microvolts and apply\na lowpass filter.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extract windows\n\nWe extract 30-s windows to be used in the classification task.\nThe Eldele2021 model takes a single channel as input. Here, the Fpz-Cz channel is used as it\nwas found to give better performance than using the Pz-Oz channel\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    picks=\"Fpz-Cz\",  # the other option is Pz-Oz,\n    preload=True,\n    mapping=mapping\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Window preprocessing\n\nWe also preprocess the windows by applying channel-wise z-score normalization\nin each window.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import scale as standard_scale\n\npreprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\nWe split the dataset into training and validation set taking\nevery other subject as train or valid.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])\nsplits = windows_dataset.split(split_ids)\ntrain_set, valid_set = splits[\"train\"], splits[\"valid\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create sequence samplers\n\nFollowing the time distributed approach of [2]_, we need to provide our\nneural network with sequences of windows, such that the embeddings of\nmultiple consecutive windows can be concatenated and provided to a final\nclassifier. We can achieve this by defining Sampler objects that return\nsequences of window indices.\nTo simplify the example, we train the whole model end-to-end on sequences,\nrather than using the two-step approach of [2]_ (i.e. training the feature\nextractor on single windows, then freezing its weights and training the\nclassifier).\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows\nn_windows_stride = 3  # Maximally overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint('Training examples: ', len(train_sampler))\nprint('Validation examples: ', len(valid_sampler))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also implement a transform to extract the label of the center window of a\nsequence to use it as target.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\n\n# Use label of center window in the sequence\ndef get_center_label(x):\n    if isinstance(x, Integral):\n        return x\n    return x[np.ceil(len(x) / 2).astype(int)] if len(x) > 1 else x\n\n\ntrain_set.target_transform = get_center_label\nvalid_set.target_transform = get_center_label"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, since some sleep stages appear a lot more often than others (e.g.\nmost of the night is spent in the N2 stage), the classes are imbalanced. To\navoid overfitting on the more frequent classes, we compute weights that we\nwill provide to the loss function when training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.utils import compute_class_weight\n\ny_train = [train_set[idx][1] for idx in train_sampler]\nclass_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\nWe can now create the deep learning model. In this tutorial, we use the sleep\nstaging architecture introduced in [1]_, which is an attention-based\nneural network. We use the time distributed version of the model, where the\nfeature vectors of a sequence of windows are concatenated and passed to a\nlinear layer for classification.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerEldele2021, TimeDistributed\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = train_set[0][0].shape\n\nfeat_extractor = SleepStagerEldele2021(\n    sfreq,\n    n_outputs=n_classes,\n    n_times=input_size_samples,\n    return_feats=True,\n)\n\nmodel = nn.Sequential(\n    TimeDistributed(feat_extractor),  # apply model on each 30-s window\n    nn.Sequential(  # apply linear layer on concatenated feature vectors\n        nn.Flatten(start_dim=1),\n        nn.Dropout(0.5),\n        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)\n    )\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\nWe can now train our network. :class:`braindecode.EEGClassifier` is a\nbraindecode object that is responsible for managing the training of neural\nnetworks. It inherits from :class:`skorch.NeuralNetClassifier`, so the\ntraining logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\ntrain_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',\n    lower_is_better=False)\nvalid_bal_acc = EpochScoring(\n    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',\n    lower_is_better=False)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=np.unique(y_train)\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot results\n\nWe use the history stored by Skorch during training to plot the performance of\nthe model throughout training. Specifically, we plot the loss and the balanced\nbalanced accuracy for the training and validation sets.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\ndf.index.name = \"Epoch\"\nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\ndf[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)\ndf[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)\nax1.set_ylabel('Loss')\nax2.set_ylabel('Balanced accuracy')\nax1.legend(['Train', 'Valid'])\nax2.legend(['Train', 'Valid'])\nfig.tight_layout()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we also display the confusion matrix and classification report:\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, classification_report\nfrom braindecode.visualization import plot_confusion_matrix\n\ny_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\nplot_confusion_matrix(confusion_mat=confusion_mat)\n#                      class_names=['Wake', 'N1', 'N2', 'N3', 'N4', 'REM'])\n\nprint(classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true, color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model was able to learn despite the low amount of data that was available\n(only two recordings in this example) and reached a balanced accuracy of\nabout 43% in a 5-class classification task (chance-level = 20%) on held-out\ndata over 10 epochs.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To further improve performance, the number of epochs should be increased.\n   It has been reduced here for faster run-time in document generation. In\n   testing, 10 epochs provided reasonable performance with around 89% balanced\n   accuracy on training data and around 43% on held out validation data.\n   Increasing the number of training recordings and optimizing the hyperparameters\n   will also help increase performance</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] E. Eldele et al., \"An Attention-Based Deep Learning Approach for Sleep Stage\n        Classification With Single-Channel EEG,\" in IEEE Transactions on Neural Systems and\n        Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.\n\n.. [2] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n.. [3] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [4] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb b/stable/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb
new file mode 100644
index 00000000..000a746b
--- /dev/null
+++ b/stable/_downloads/8616a7f968141825e56ab3e3d59be449/plot_tuh_discrete_multitarget.ipynb
@@ -0,0 +1,169 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Multiple discrete targets with the TUH EEG Corpus\n\nWelcome to this tutorial where we demonstrate how to work with multiple discrete\n targets for each recording in the TUH EEG Corpus. We'll guide you through the\n process step by step.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom torch.utils.data import DataLoader\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import create_fixed_length_windows\n\n# Setting Logging Level\n# ----------------------\n#\n# We'll set the logging level to 'ERROR' to avoid excessive messages when\n# extracting windows:\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you want to try this code with the actual data, please delete the next\nsection. We are required to mock some dataset functionality, since the data\nis not available at creation time of this example.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating Temple University Hospital (TUH) EEG Corpus Dataset\n\nWe start by creating a TUH dataset. Instead of just a `str, we give it\nmultiple strings as target names. Each of the strings has to exist as a\ncolumn in the description DataFrame.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "TUH_PATH = 'please insert actual path to data here'\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=('age', 'gender'),  # use both age and gender as decoding target\n    preload=False,\n    add_physician_reports=False,\n)\nprint(tuh.description)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Exploring Data\n\nIterating through the dataset gives `x` as an ndarray with shape\n`(n_channels x 1)` and `y` as a list containing `[age of the subject, gender\nof the subject]`.\nLet's look at the last example as it has more interesting age/gender labels\n(compare to the last row of the dataframe above).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "x, y = tuh[-1]\n\nprint(f'{x=}\\n{y=}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating Windows\n\nWe will skip preprocessing steps for now, since it is not the aim of this\nexample. Instead, we will directly create compute windows. We specify a\nmapping from genders 'M' and 'F' to integers, since this is required for\ndecoding.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "tuh_windows = create_fixed_length_windows(\n    tuh,\n    start_offset_samples=0,\n    stop_offset_samples=None,\n    window_size_samples=1000,\n    window_stride_samples=1000,\n    drop_last_window=False,\n    mapping={'M': 0, 'F': 1},  # map non-digit targets\n)\n# store the number of windows required for loading later on\ntuh_windows.set_description({\n    \"n_windows\": [len(d) for d in tuh_windows.datasets]})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Exploring Windows\n\nIterating through the dataset gives `x` as an ndarray with shape\n`(n_channels x 1000)`, `y` as `[age, gender]`, and `ind`.\nLet's look at the last example again.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "x, y, ind = tuh_windows[-1]\nprint(f'{x=}\\n{y=}\\n{ind=}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## DataLoader for Model Training\n\nWe give the dataset to a pytorch DataLoader, such that it can be used for\nmodel training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dl = DataLoader(\n    dataset=tuh_windows,\n    batch_size=4,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Exploring DataLoader\n\nWhen iterating through the DataLoader, we get `batch_X` as a tensor with shape\n`(4 x n_channels x 1000)`, `batch_y` as `[tensor([4 x age of subject]),\ntensor([4 x gender of subject])]`, and `batch_ind`. To view the last example,\nsimply iterate through the DataLoader:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for batch_X, batch_y, batch_ind in dl:\n    pass\n\nprint(f'{batch_X=}\\n{batch_y=}\\n{batch_ind=}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py b/stable/_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py
new file mode 100644
index 00000000..438fa0a8
--- /dev/null
+++ b/stable/_downloads/885d9ee3c77215b539444b199aa0fd1b/plot_mne_dataset_example.py
@@ -0,0 +1,60 @@
+"""
+MNE Dataset Example
+===================
+"""
+##############################################################################
+# This example shows how to convert data from mne.Raws or mne.Epochs to a
+# braindecode compatible data format.
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+import mne
+
+from braindecode.datasets import (
+    create_from_mne_raw, create_from_mne_epochs)
+
+###############################################################################
+# First, fetch some data using mne:
+
+# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet
+subject_id = 22
+event_codes = [5, 6, 9, 10, 13, 14]
+# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+
+# This will download the files if you don't have them yet,
+# and then return the paths to the files.
+physionet_paths = mne.datasets.eegbci.load_data(
+    subject_id, event_codes, update_path=False)
+
+# Load each of the files
+parts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')
+         for path in physionet_paths]
+
+###############################################################################
+# Convert Raw objects to a compatible data format:
+descriptions = [{"event_code": code, "subject": subject_id}
+                for code in event_codes]
+windows_dataset = create_from_mne_raw(
+    parts,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+    window_size_samples=500,
+    window_stride_samples=500,
+    drop_last_window=False,
+    descriptions=descriptions,
+)
+
+###############################################################################
+# If trials were already cut beforehand and are available as mne.Epochs:
+list_of_epochs = [mne.Epochs(raw, [[0, 0, 0]], tmin=0, baseline=None)
+                  for raw in parts]
+windows_dataset = create_from_mne_epochs(
+    list_of_epochs,
+    window_size_samples=50,
+    window_stride_samples=50,
+    drop_last_window=False
+)
+
+windows_dataset.description
diff --git a/stable/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb b/stable/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb
new file mode 100644
index 00000000..cc3edce9
--- /dev/null
+++ b/stable/_downloads/8b5ba06718764b959e8dea1dd0bb97df/plot_sleep_staging_usleep.ipynb
@@ -0,0 +1,255 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Sleep staging on the Sleep Physionet dataset using U-Sleep network\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Please take a look at the simpler sleep staging example\n    `here <sphx_glr_auto_examples_plot_sleep_staging.py>`\n    before going through this example. The current example uses a more complex\n    architecture and a sequence-to-sequence (seq2seq) approach.</p></div>\n\nThis tutorial shows how to train and test a sleep staging neural network with\nBraindecode. We adapt the U-Sleep approach of [1]_ to learn on sequences of EEG\nwindows using the openly accessible Sleep Physionet dataset [2]_ [3]_.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>The example is written to have a very short execution time.\n    This number of epochs is here too small and very few recordings are used.\n    To obtain competitive results you need to use more data and more epochs.</p></div>\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Theo Gnassounou <theo.gnassounou@inria.fr>\n#          Omar Chehab <l-emir-omar.chehab@inria.fr>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading\n\nFirst, we load the data using the\n:class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load\ntwo recordings from two different individuals: we will use the first one to\ntrain our network and the second one to evaluate performance (as in the `MNE`_\nsleep staging example).\n\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import SleepPhysionet\n\nsubject_ids = [0, 1]\ncrop = (0, 30 * 400)  # we only keep 400 windows of 30s to speed example\ndataset = SleepPhysionet(\n    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30,\n    crop=crop)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nNext, we preprocess the raw data. We scale each channel recording-wise to\nhave zero median and unit interquartile range. We don't upsample to 128 Hz as\ndone in [1]_ so that we keep the example as light as possible. No filtering\nis described in [1]_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom sklearn.preprocessing import robust_scale\n\npreprocessors = [Preprocessor(robust_scale, channel_wise=True)]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extract windows\n\nWe extract 30-s windows to be used in the classification task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4,\n}\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples,\n    preload=True,\n    mapping=mapping,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\nWe split the dataset into training and validation set taking\nevery other subject as train or valid.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])\nsplits = windows_dataset.split(split_ids)\ntrain_set, valid_set = splits[\"train\"], splits[\"valid\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create sequence samplers\n\nFollowing the sequence-to-sequence approach of [1]_, we need to provide our\nneural network with sequences of windows. We can achieve this by defining\nSampler objects that return sequences of windows.\nNon-overlapping sequences of 35 windows are used in [1]_, however to limit\nthe memory requirements for this example we use shorter sequences of 3\nwindows.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.samplers import SequenceSampler\n\nn_windows = 3  # Sequences of 3 consecutive windows; originally 35 in paper\nn_windows_stride = 3  # Non-overlapping sequences\n\ntrain_sampler = SequenceSampler(\n    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True\n)\nvalid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)\n\n# Print number of examples per class\nprint(len(train_sampler))\nprint(len(valid_sampler))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, since some sleep stages appear a lot more often than others (e.g.\nmost of the night is spent in the N2 stage), the classes are imbalanced. To\navoid overfitting to the more frequent classes, we compute weights that we\nwill provide to the loss function when training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom sklearn.utils import compute_class_weight\n\ny_train = [train_set[idx][1][1] for idx in train_sampler]\nclass_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\nWe can now create the deep learning model. In this tutorial, we use the\nU-Sleep architecture introduced in [1]_, which is fully convolutional\nneural network.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import USleep\n\ncuda = torch.cuda.is_available()  # check if GPU is available\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=31, cuda=cuda)\n\nn_classes = 5\nclasses = list(range(n_classes))\n# Extract number of channels and time steps from dataset\nin_chans, input_size_samples = train_set[0][0].shape\nmodel = USleep(\n    n_chans=in_chans,\n    sfreq=sfreq,\n    depth=12,\n    with_skip_connection=True,\n    n_outputs=n_classes,\n    n_times=input_size_samples\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\nWe can now train our network. :class:`braindecode.EEGClassifier` is a\nbraindecode object that is responsible for managing the training of neural\nnetworks. It inherits from :class:`skorch.NeuralNetClassifier`, so the\ntraining logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>We use different hyperparameters from [1]_, as these hyperparameters were\n   optimized on different datasets and with a different number of recordings.\n   Generally speaking, it is recommended to perform hyperparameter\n   optimization if reusing this code on a different dataset or with more\n   recordings.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.helper import predefined_split\nfrom skorch.callbacks import EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 1e-3\nbatch_size = 32\nn_epochs = 3  # we use few epochs for speed and but more than one for plotting\n\nfrom sklearn.metrics import balanced_accuracy_score\n\n\ndef balanced_accuracy_multi(model, X, y):\n    y_pred = model.predict(X)\n    return balanced_accuracy_score(y.flatten(), y_pred.flatten())\n\n\ntrain_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=True,\n    name='train_bal_acc',\n    lower_is_better=False,\n)\nvalid_bal_acc = EpochScoring(\n    scoring=balanced_accuracy_multi,\n    on_train=False,\n    name='valid_bal_acc',\n    lower_is_better=False,\n)\ncallbacks = [\n    ('train_bal_acc', train_bal_acc),\n    ('valid_bal_acc', valid_bal_acc)\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.CrossEntropyLoss,\n    criterion__weight=torch.Tensor(class_weights).to(device),\n    optimizer=torch.optim.Adam,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=classes,\n)\n# Deactivate the default valid_acc callback:\nclf.set_params(callbacks__valid_acc=None)\n\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot results\n\nWe use the history stored by Skorch during training to plot the performance of\nthe model throughout training. Specifically, we plot the loss and the balanced\nbalanced accuracy for the training and validation sets.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\ndf.index.name = \"Epoch\"\nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)\ndf[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)\ndf[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)\nax1.set_ylabel('Loss')\nax2.set_ylabel('Balanced accuracy')\nax1.legend(['Train', 'Valid'])\nax2.legend(['Train', 'Valid'])\nfig.tight_layout()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we also display the confusion matrix and classification report:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.visualization import plot_confusion_matrix\nfrom sklearn.metrics import confusion_matrix, classification_report\n\ny_true = np.array([valid_set[i][1] for i in valid_sampler])\ny_pred = clf.predict(valid_set)\n\nconfusion_mat = confusion_matrix(y_true.flatten(), y_pred.flatten())\n\nplot_confusion_matrix(confusion_mat=confusion_mat,\n                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])\n\nprint(classification_report(y_true.flatten(), y_pred.flatten()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Finally, we can also visualize the hypnogram of the recording we used for\nvalidation, with the predicted sleep stages overlaid on top of the true\nsleep stages. We can see that the model cannot correctly identify the\ndifferent sleep stages with this amount of training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfig, ax = plt.subplots(figsize=(15, 5))\nax.plot(y_true.flatten(), color='b', label='Expert annotations')\nax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)\nax.set_xlabel('Time (epochs)')\nax.set_ylabel('Sleep stage')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Our model was able to learn, as shown by the decreasing training and\nvalidation loss values, despite the low amount of data that was available\n(only two recordings in this example). To further improve performance, more\nrecordings should be included in the training set, the model should be\ntrained for more epochs and hyperparameters should be optimized.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.\n       U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).\n       https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py\n\n.. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Obery\u00e9. Analysis of\n       a sleep-dependent neuronal feedback loop: the slow-wave\n       microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).\n\n.. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,\n       Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)\n       PhysioBank, PhysioToolkit, and PhysioNet: Components of a New\n       Research Resource for Complex Physiologic Signals.\n       Circulation 101(23):e215-e220\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py b/stable/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py
new file mode 100644
index 00000000..0318ffd1
--- /dev/null
+++ b/stable/_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py
@@ -0,0 +1,116 @@
+"""
+Split Dataset Example
+=====================
+
+In this example, we aim to show multiple ways of how you can split your datasets for
+training, testing, and evaluating your models.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import create_windows_from_events
+
+###############################################################################
+# Loading the dataset
+# -------------------------------------
+#
+# Firstly, we create a dataset using the braindecode class <MOABBDataset> to load
+# it fetched from MOABB. In this example, we're using Dataset 2a from BCI
+# Competition IV.
+
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+###############################################################################
+# Splitting
+# -------------------------------------
+#
+# By description information
+# ~~~~~~~~~~~~~
+#
+# The class <MOABBDataset> has a pandas DataFrame containing additional
+# description of its internal datasets, which can be used to help splitting the data
+# based on recording information, such as subject, session, and run of each trial.
+
+dataset.description
+
+###############################################################################
+# Here, we're splitting the data based on different runs. The method split returns
+# a dictionary with string keys corresponding to unique entries in the description
+# DataFrame column.
+
+splits = dataset.split("run")
+print(splits)
+splits["4"].description
+
+###############################################################################
+# By row index
+# ~~~~~~~~~~~~~
+#
+# Another way we can split the dataset is based on a list of integers corresponding to
+# rows in the description. In this case, the returned dictionary will have
+# '0' as the only key.
+
+splits = dataset.split([0, 1, 5])
+print(splits)
+splits["0"].description
+
+###############################################################################
+# However, if we want multiple splits based on indices, we can also define a list
+# containing lists of integers. In this case, the dictionary will have string keys
+# representing the index of the dataset split in the order of the given list of
+# integers.
+
+splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
+print(splits)
+splits["2"].description
+
+###############################################################################
+# You can also name each split in the output dictionary by specifying the keys
+# of each list of indexes in the input dictionary:
+
+splits = dataset.split(
+    {"train": [0, 1, 5], "valid": [2, 3, 4], "test": [6, 7, 8, 9, 10, 11]}
+)
+print(splits)
+splits["test"].description
+
+###############################################################################
+# Observation
+# -------------------------------------
+#
+# Similarly, we can split datasets after creating windows using the same methods.
+
+windows = create_windows_from_events(
+    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)
+
+###############################################################################
+
+# Splitting by different runs
+print("Using description info")
+splits = windows.split("run")
+print(splits)
+print()
+
+# Splitting by row index
+print("Splitting by row index")
+splits = windows.split([4, 8])
+print(splits)
+print()
+
+print("Multiple row index split")
+splits = windows.split([[4, 8], [5, 9, 11]])
+print(splits)
+print()
+
+# Specifying output's keys
+print("Specifying keys")
+splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))
+print(splits)
diff --git a/stable/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py b/stable/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py
new file mode 100644
index 00000000..6e1aeb8c
--- /dev/null
+++ b/stable/_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py
@@ -0,0 +1,387 @@
+"""
+Fingers flexion decoding on BCIC IV 4 ECoG Dataset
+==================================================
+
+This tutorial shows you how to train and test deep learning models with
+Braindecode on ECoG BCI IV competition dataset 4. For this dataset we will
+predict 5 regression targets corresponding to flexion of each finger.
+The targets were recorded as a time series (each 25 Hz), so this tutorial is
+an example of time series target prediction.
+"""
+
+# Authors: Maciej Sliwowski <maciek.sliwowski@gmail.com>
+#          Mohammed Fattouh <mo.fattouh@gmail.com>
+#
+# License: BSD (3-clause)
+
+######################################################################
+# Loading and preparing the dataset
+# -------------------------------------
+#
+
+
+######################################################################
+# Loading
+# ~~~~~~~
+#
+
+
+######################################################################
+# First, we load the data. In this tutorial, we use the functionality of braindecode
+# to load `BCI IV competition dataset 4 <http://www.bbci.de/competition/iv/#dataset4>`__.
+# The dataset is available as a part of ECoG library:
+# https://searchworks.stanford.edu/view/zk881ps0522
+#
+# The dataset contains ECoG signal and time series of 5 targets corresponding
+# to each finger flexion. This is different than standard decoding setup for EEG with
+# multiple trials and usually one target per trial. Here, fingers flexions change in time
+# and are recorded with sampling frequency equals to 25 Hz.
+#
+# If this dataset is used please cite [1].
+#
+# [1] Miller, Kai J. "A library of human electrocorticographic data and analyses.
+# "Nature human behaviour 3, no. 11 (2019): 1225-1235. https://doi.org/10.1038/s41562-019-0678-3
+
+
+import numpy as np
+
+from braindecode.datasets import BCICompetitionIVDataset4
+
+subject_id = 1
+dataset = BCICompetitionIVDataset4(subject_ids=[subject_id])
+
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Now we apply preprocessing like bandpass filtering to our dataset. You
+# can either apply functions provided by
+# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
+# `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
+# or apply your own functions, either to the MNE object or the underlying
+# numpy array.
+#
+# .. note::
+#    Preprocessing steps are taken from a standard EEG processing pipeline.
+#    The only change is the cutoff frequency of the filter. For a proper ECoG
+#    decoding other preprocessing steps may be needed.
+#
+# .. note::
+#    These prepocessings are now directly applied to the loaded
+#    data, and not on-the-fly applied as transformations in
+#    PyTorch-libraries like
+#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
+#
+
+
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
+
+low_cut_hz = 1.  # low cut frequency for filtering
+high_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+
+######################################################################
+# We select only first 30 seconds from each dataset to limit time and memory
+# to run this example. To obtain results on the whole datasets you should remove this line.
+preprocess(dataset, [Preprocessor('crop', tmin=0, tmax=30)])
+
+######################################################################
+# In time series targets setup, targets variables are stored in mne.Raw object as channels
+# of type `misc`. Thus those channels have to be selected for further processing. However,
+# many mne functions ignore `misc` channels and perform operations only on data channels
+# (see https://mne.tools/stable/glossary.html#term-data-channels).
+preprocessors = [
+    Preprocessor('pick_types', ecog=True, misc=True),
+    Preprocessor(lambda x: x / 1e6, picks='ecog'),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size, picks='ecog')
+]
+
+# Transform the data
+preprocess(dataset, preprocessors)
+
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Extract target sampling frequency
+target_sfreq = dataset.datasets[0].raw.info['temp']['target_sfreq']
+
+
+######################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# Now we cut out compute windows, the inputs for the deep networks during
+# training. In the case of trialwise decoding of time series targets, we just have to
+# decide about length windows that will be selected from the signal preceding each target.
+# We use different windowing function than in standard trialwise decoding as our targets
+# are stored as target channels in mne.Raw.
+from braindecode.preprocessing import create_windows_from_target_channels
+
+windows_dataset = create_windows_from_target_channels(
+    dataset,
+    window_size_samples=1000,
+    preload=False,
+    last_target_only=True
+)
+
+######################################################################
+# We select only the thumb's finger flexion to create one model per finger.
+#
+# .. note::
+#    Methods to predict all 5 fingers flexion with the same model may be cnosidered as well.
+#    We encourage you to find your own way to use braindecode models to predict finers fexions.
+#
+windows_dataset.target_transform = lambda x: x[0: 1]
+
+
+######################################################################
+# Split dataset into train, valid, and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+
+######################################################################
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case ``session`` column. We select `train` dataset
+# for training and validation and `test` for final evaluation.
+
+subsets = windows_dataset.split('session')
+train_set = subsets['train']
+test_set = subsets['test']
+
+######################################################################
+# We can split train dataset into training and validation datasets using
+# ``sklearn.model_selection.train_test_split`` and ``torch.utils.data.Subset``.
+import torch
+from sklearn.model_selection import train_test_split
+
+idx_train, idx_valid = train_test_split(np.arange(len(train_set)),
+                                        random_state=100,
+                                        test_size=0.2,
+                                        shuffle=False)
+
+valid_set = torch.utils.data.Subset(train_set, idx_valid)
+train_set = torch.utils.data.Subset(train_set, idx_train)
+
+
+######################################################################
+# Create model
+# ------------
+#
+
+
+######################################################################
+# Now we create the deep learning model! Braindecode comes with some
+# predefined convolutional neural network architectures for raw
+# time-domain EEG. Here, we use the shallow ConvNet model from `Deep
+# learning with convolutional neural networks for EEG decoding and
+# visualization <https://arxiv.org/abs/1703.05051>`__. These models are
+# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
+# to use your own model, it just has to be a normal PyTorch
+# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
+
+
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_out_chans = train_set[0][1].shape[0]
+# Extract number of chans and time steps from dataset
+n_chans = train_set[0][0].shape[0]
+input_window_samples = 1000  # 1 second long windows
+
+model = ShallowFBCSPNet(
+    n_chans,
+    n_out_chans,
+    input_window_samples=input_window_samples,
+    final_conv_length='auto',
+    add_log_softmax=False,
+)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+
+#######################################################################
+# Training
+# --------
+#
+
+
+######################################################################
+# Now we train the network! EEGRegressor is a Braindecode object
+# responsible for managing the training of neural networks. It inherits
+# from skorch.NeuralNetRegressor, so the training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+# .. note::
+#    In this tutorial, we use some default parameters that we
+#    have found to work well for EEG motor decoding, however we strongly
+#    encourage you to perform your own hyperparameter and preprocessing optimization using
+#    cross validation on your training data.
+#
+from skorch.callbacks import EpochScoring, LRScheduler
+from skorch.helper import predefined_split
+from mne import set_log_level
+
+from braindecode import EEGRegressor
+
+# These values we found good for shallow network for EEG MI decoding:
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 64
+n_epochs = 2
+
+
+# Function to compute Pearson correlation coefficient
+def pearson_r_score(net, dataset, y):
+    preds = net.predict(dataset)
+    corr_coeffs = []
+    for i in range(y.shape[1]):
+        corr_coeffs.append(np.corrcoef(y[:, i], preds[:, i])[0, 1])
+    return np.mean(corr_coeffs)
+
+
+regressor = EEGRegressor(
+    model,
+    criterion=torch.nn.MSELoss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(valid_set),  # using valid_set for validation,
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        'r2',
+        ('valid_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=False,
+                                         name='valid_pearson_r')),
+        ('train_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=True,
+                                         name='train_pearson_r')),
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+)
+set_log_level(verbose='WARNING')
+
+######################################################################
+# Model training for a specified number of epochs. ``y`` is None as it is already supplied
+# in the dataset.
+regressor.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Obtaining predictions and targets for the test, train, and validation dataset
+preds_test = regressor.predict(test_set)
+y_test = np.stack([data[1] for data in test_set])
+preds_train = regressor.predict(train_set)
+y_train = np.stack([data[1] for data in train_set])
+preds_valid = regressor.predict(valid_set)
+y_valid = np.stack([data[1] for data in valid_set])
+
+######################################################################
+# Plot Results
+# ------------
+
+
+######################################################################
+# We plot target and predicted finger flexion on training, validation, and test sets.
+#
+# .. note::
+#    The model is trained and validated on limited dataset (to decrease the time needed to run
+#    this example) which does not contain diverse dataset in terms of fingers flexions and may
+#    cause overfitting. To obtain better results use whole dataset as well as improve the decoding
+#    pipeline which may be not optimal for ECoG.
+#
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.lines import Line2D
+
+fig, axes = plt.subplots(3, 1, figsize=(8, 9))
+
+axes[0].set_title('Training dataset')
+axes[0].plot(np.arange(0, y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')
+axes[0].plot(np.arange(0, preds_train.shape[0]) / target_sfreq, preds_train[:, 0],
+             label='Predicted')
+axes[0].set_ylabel('Finger flexion')
+axes[0].legend()
+
+axes[1].set_title('Validation dataset')
+axes[1].plot(np.arange(0, y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')
+axes[1].plot(np.arange(0, preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],
+             label='Predicted')
+axes[1].set_ylabel('Finger flexion')
+axes[1].legend()
+
+axes[2].set_title('Test dataset')
+axes[2].plot(np.arange(0, y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')
+axes[2].plot(np.arange(0, preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')
+axes[2].set_xlabel('Time [s]')
+axes[2].set_ylabel('Finger flexion')
+axes[2].legend()
+plt.tight_layout()
+
+######################################################################
+# We can compute correlation coefficients for each finger
+#
+corr_coeffs = []
+for dim in range(y_test.shape[1]):
+    corr_coeffs.append(
+        np.corrcoef(preds_test[:, dim], y_test[:, dim])[0, 1]
+    )
+print('Correlation coefficient for each dimension: ', np.round(corr_coeffs, 2))
+
+######################################################################
+# Now we use the history stored by Skorch throughout training to plot
+# accuracy and loss curves.
+# Extract loss and accuracy values for plotting from history object
+results_columns = ['train_loss', 'valid_loss', 'train_pearson_r', 'valid_pearson_r']
+df = pd.DataFrame(regressor.history[:, results_columns], columns=results_columns,
+                  index=regressor.history[:, 'epoch'])
+
+fig, ax1 = plt.subplots(figsize=(8, 4))
+df.loc[:, ['train_loss', 'valid_loss']].plot(
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
+
+ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
+ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
+
+ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+df.loc[:, ['train_pearson_r', 'valid_pearson_r']].plot(
+    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
+ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
+ax2.set_ylabel("Pearson correlation coefficient", color='tab:red', fontsize=14)
+ax1.set_xlabel("Epoch", fontsize=14)
+
+# where some data has already been plotted to ax
+handles = []
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-',
+                      label='Train'))
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':',
+                      label='Valid'))
+plt.legend(handles, [h.get_label() for h in handles], fontsize=14, loc='center right')
+plt.tight_layout()
diff --git a/stable/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb b/stable/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb
new file mode 100644
index 00000000..ff79871f
--- /dev/null
+++ b/stable/_downloads/91651c9d4fde110b4a53f5775a91acc5/plot_mne_dataset_example.ipynb
@@ -0,0 +1,104 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# MNE Dataset Example\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This example shows how to convert data from mne.Raws or mne.Epochs to a\nbraindecode compatible data format.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\n\nfrom braindecode.datasets import (\n    create_from_mne_raw, create_from_mne_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, fetch some data using mne:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet\nsubject_id = 22\nevent_codes = [5, 6, 9, 10, 13, 14]\n# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n\n# This will download the files if you don't have them yet,\n# and then return the paths to the files.\nphysionet_paths = mne.datasets.eegbci.load_data(\n    subject_id, event_codes, update_path=False)\n\n# Load each of the files\nparts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')\n         for path in physionet_paths]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Convert Raw objects to a compatible data format:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "descriptions = [{\"event_code\": code, \"subject\": subject_id}\n                for code in event_codes]\nwindows_dataset = create_from_mne_raw(\n    parts,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n    window_size_samples=500,\n    window_stride_samples=500,\n    drop_last_window=False,\n    descriptions=descriptions,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If trials were already cut beforehand and are available as mne.Epochs:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "list_of_epochs = [mne.Epochs(raw, [[0, 0, 0]], tmin=0, baseline=None)\n                  for raw in parts]\nwindows_dataset = create_from_mne_epochs(\n    list_of_epochs,\n    window_size_samples=50,\n    window_stride_samples=50,\n    drop_last_window=False\n)\n\nwindows_dataset.description"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb b/stable/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb
new file mode 100644
index 00000000..3d6f928a
--- /dev/null
+++ b/stable/_downloads/9a4447462c3b255ba7e5ca212bbadd52/plot_bcic_iv_2a_moabb_trial.ipynb
@@ -0,0 +1,253 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Basic Brain Decoding on EEG Data\n\nThis tutorial shows you how to train and test deep learning models with\nBraindecode in a classical EEG setting: you have trials of data with\nlabels (e.g., Right Hand, Left Hand, etc.).\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preparing the data\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading the dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load the data. In this tutorial, we load the BCI Competition\nIV 2a data [1]_ using braindecode's wrapper to load via\n[MOABB library](https://github.com/NeuroTechX/moabb)_ [2]_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Generally, braindecode prepocessing is directly applied to the loaded\n   data, and not applied on-the-fly as transformations, such as in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from numpy import multiply\n\nfrom braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extracting Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we extract compute windows from the signals, these will be the inputs\nto the deep networks during training. In the case of trialwise\ndecoding, we just have to decide if we want to include some part\nbefore and/or after the trial. For our work with this dataset,\nit was often beneficial to also include the 500 ms before the trial.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Splitting the dataset into training and validation sets\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``T`` for training and ``test`` for validation.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\nvalid_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating a model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [3]_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model = model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model Training\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we will train the network! ``EEGClassifier`` is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom skorch [NeuralNetClassifier](https://skorch.readthedocs.io/en/stable/classifier.html#)_,\nso the training logic is the same as in [Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n   have found to work well for motor decoding, however we strongly\n   encourage you to perform your own hyperparameter optimization using\n   cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGClassifier\n\n# We found these values to be good for the shallow network:\nlr = 0.0625 * 0.01\nweight_decay = 0\n\n# For deep4 they should be:\n# lr = 1 * 0.01\n# weight_decay = 0.5 * 0.001\n\nbatch_size = 64\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        \"accuracy\", (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n    classes=classes,\n)\n# Model training for the specified number of epochs. `y` is None as it is\n# already supplied in the dataset.\n_ = clf.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting Results\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we use the history stored by Skorch throughout training to plot\naccuracy and loss curves.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\n# Extract loss and accuracy values for plotting from history object\nresults_columns = ['train_loss', 'valid_loss', 'train_accuracy', 'valid_accuracy']\ndf = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n                  index=clf.history[:, 'epoch'])\n\n# get percent of misclass for better visual comparison to loss\ndf = df.assign(train_misclass=100 - 100 * df.train_accuracy,\n               valid_misclass=100 - 100 * df.valid_accuracy)\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_misclass', 'valid_misclass']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\nax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14)\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting a  Confusion Matrix\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here we generate a confusion matrix as in [3]_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n\nfrom braindecode.visualization import plot_confusion_matrix\n\n# generate confusion matrices\n# get the targets\ny_true = valid_set.get_metadata().target\ny_pred = clf.predict(valid_set)\n\n# generating confusion matrix\nconfusion_mat = confusion_matrix(y_true, y_pred)\n\n# add class labels\n# label_dict is class_name : str -> i_class : int\nlabel_dict = windows_dataset.datasets[0].window_kwargs[0][1]['mapping']\n# sort the labels by values (values are integer class labels)\nlabels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]\n\n# plot the basic conf. matrix\nplot_confusion_matrix(confusion_mat, class_names=labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/9c64c2c15907a661b1fe2d7dd209a434/plot_regression.ipynb b/stable/_downloads/9c64c2c15907a661b1fe2d7dd209a434/plot_regression.ipynb
deleted file mode 100644
index 5f192c67..00000000
--- a/stable/_downloads/9c64c2c15907a661b1fe2d7dd209a434/plot_regression.ipynb
+++ /dev/null
@@ -1,54 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Regression example on fake data\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD-3\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom skorch.callbacks import LRScheduler\nfrom skorch.helper import predefined_split\n\nfrom braindecode import EEGRegressor\nfrom braindecode.preprocessing import create_fixed_length_windows\nfrom braindecode.datasets import BaseDataset, BaseConcatDataset\nfrom braindecode.training.losses import CroppedLoss\nfrom braindecode.models import Deep4Net\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.models.util import to_dense_prediction_model, get_output_shape\nfrom braindecode.util import set_random_seeds, create_mne_dummy_raw\n\nmodel_name = \"shallow\"  # 'shallow' or 'deep'\nn_epochs = 3\nseed = 20200220\n\ninput_window_samples = 6000\nbatch_size = 64\ncuda = torch.cuda.is_available()\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n\nn_chans = 21\n# set to how many targets you want to regress (age -> 1, [x, y, z] -> 3)\nn_classes = 1\n\nset_random_seeds(seed=seed, cuda=cuda)\n\n# initialize a model, transform to dense and move to gpu\nif model_name == \"shallow\":\n    model = ShallowFBCSPNet(\n        in_chans=n_chans,\n        n_classes=n_classes,\n        input_window_samples=input_window_samples,\n        n_filters_time=40,\n        n_filters_spat=40,\n        final_conv_length=35,\n    )\n    optimizer_lr = 0.000625\n    optimizer_weight_decay = 0\nelif model_name == \"deep\":\n    model = Deep4Net(\n        in_chans=n_chans,\n        n_classes=n_classes,\n        input_window_samples=input_window_samples,\n        n_filters_time=25,\n        n_filters_spat=25,\n        stride_before_pool=True,\n        n_filters_2=int(n_chans * 2),\n        n_filters_3=int(n_chans * (2 ** 2.0)),\n        n_filters_4=int(n_chans * (2 ** 3.0)),\n        final_conv_length=1,\n    )\n    optimizer_lr = 0.01\n    optimizer_weight_decay = 0.0005\nelse:\n    raise ValueError(f'{model_name} unknown')\n\nnew_model = torch.nn.Sequential()\nfor name, module_ in model.named_children():\n    if \"softmax\" in name:\n        continue\n    new_model.add_module(name, module_)\nmodel = new_model\n\nif cuda:\n    model.cuda()\n\nto_dense_prediction_model(model)\nn_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]\n\n\ndef fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq, fake_duration_s):\n    datasets = []\n    for i in range(n_fake_recs):\n        train_or_eval = \"eval\" if i == 0 else \"train\"\n        raw, save_fname = create_mne_dummy_raw(\n            n_channels=n_fake_chs, n_times=fake_duration_s * fake_sfreq,\n            sfreq=fake_sfreq, savedir=None)\n        target = np.random.randint(0, 100, n_classes)\n        if n_classes == 1:\n            target = target[0]\n        fake_descrition = pd.Series(\n            data=[target, train_or_eval],\n            index=[\"target\", \"session\"])\n        base_ds = BaseDataset(raw, fake_descrition, target_name=\"target\")\n        datasets.append(base_ds)\n    dataset = BaseConcatDataset(datasets)\n    return dataset\n\n\ndataset = fake_regression_dataset(\n    n_fake_recs=5, n_fake_chs=21, fake_sfreq=100, fake_duration_s=60)\n\nwindows_dataset = create_fixed_length_windows(\n    dataset,\n    start_offset_samples=0,\n    stop_offset_samples=0,\n    window_size_samples=input_window_samples,\n    window_stride_samples=n_preds_per_input,\n    drop_last_window=False,\n    drop_bad_windows=True,\n)\n\nsplits = windows_dataset.split(\"session\")\ntrain_set = splits[\"train\"]\nvalid_set = splits[\"eval\"]\n\nregressor = EEGRegressor(\n    model,\n    cropped=True,\n    criterion=CroppedLoss,\n    criterion__loss_function=torch.nn.functional.mse_loss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),\n    optimizer__lr=optimizer_lr,\n    optimizer__weight_decay=optimizer_weight_decay,\n    iterator_train__shuffle=True,\n    batch_size=batch_size,\n    callbacks=[\n        \"neg_root_mean_squared_error\",\n        # seems n_epochs -1 leads to desired behavior of lr=0 after end of training?\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\n\nregressor.fit(train_set, y=None, epochs=n_epochs)"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb b/stable/_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb
new file mode 100644
index 00000000..00748e86
--- /dev/null
+++ b/stable/_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb
@@ -0,0 +1,257 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Hyperparameter tuning with scikit-learn\n\nThe braindecode provides some compatibility with\n[scikit-learn](https://scikit-learn.org/stable/)_. This allows us\nto use scikit-learn functionality to find the best hyperparameters for our\nmodel. This is especially useful to tune hyperparameters or\nparameters for one decoding task or a specific dataset.\n\n.. topic:: Why do you need to tune the neural networks model?\n\n    Deep learning models are often sensitive to the choice of hyperparameters\n    and parameters. Hyperparameters are the parameters set before\n    training the model. The hyeperparameters determine (1) the capacity of the model,\n    e.g. its depth (the number of layers) and its width (the number of\n    convolutional kernels, sizes of fully connected layers) and (2) the\n    learning process via the choice of optimizer and its learning rate,\n    the number of epochs, the batch size, the choice of non-linearities,\n    the strategies to initialize the learning weights, etc.\n    On the other hand, parameters are learned during training,\n    such as the neural network weights. The choice of these can have a\n    significant impact on the performance of the model.\n    Therefore, it is important to tune these to maximize the discriminative\n    power of the model, in the case of decoding tasks (classification,\n    regression, etc.), such as sleep staging, BCI, pathology detection, etc.\n    We recommend the Deep Learning Tuning Playbook by Google to learn more\n    about hyperparameters and parameters tuning [1]_.\n\n\nIn this tutorial, we will use the standard decoding approach to show the impact\nof the learning rate and dropout probability on the model's performance.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load the data. In this tutorial, we use the functionality of\nbraindecode to load datasets via\n[MOABB](https://github.com/NeuroTechX/moabb)_ [2]_ to load the BCI\nCompetition IV 2a data [3]_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.moabb import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, preprocessing includes signal rescaling, the bandpass filtering\n(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using\nthe exponential moving mean and variance.\nYou can either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/stable/generated/mne.Epochs.html)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.preprocess import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),\n    # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),\n    # Bandpass filter\n    Preprocessor(exponential_moving_standardize,\n                 # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Preprocess the data\npreprocess(dataset, preprocessors, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extraction of the Windows\n\nExtraction of the trials (windows) from the time series is based on the\nevents inside the dataset. One event is the demarcation of the stimulus or\nthe beginning of the trial. In this example, we want to analyse 0.5 [s] long\nbefore the corresponding event and the duration of the event itself.\n#Therefore, we set the ``trial_start_offset_seconds`` to -0.5 [s] and the\n``trial_stop_offset_seconds`` to 0 [s].\n\nWe extract from the dataset the sampling frequency, which is the same for\nall datasets in this case, and we tested it.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The ``trial_start_offset_seconds`` and ``trial_stop_offset_seconds`` are\n   defined in seconds and need to be converted into samples (multiplication\n   with the sampling frequency), relative to the event.\n   This variable is dataset dependent.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.windowers import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and valid\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``0train`` for training and ``1test`` for evaluation.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split('session')\ntrain_set = splitted['0train']  # Session train\neval_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the ShallowFBCSPNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_ [4]_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from functools import partial\nimport torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\n# check if GPU is available, if True chooses to use it\ncuda = torch.cuda.is_available()\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220  # random seed to make results reproducible\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\n# To analyze the impact of the different parameters inside the torch model, we\n# need to create partial initialisations. This is because the\n# GridSearchCV of scikit-learn will try to initialize the model with the\n# parameters we want to tune. If we do not do this, the GridSearchCV will\n# try to initialize the model with the parameters we want to tune but\n# without the parameters we do not want to tune. This will result in an\n# error.\nmodel = partial(ShallowFBCSPNet, n_chans, n_classes,\n                input_window_samples=input_window_samples,\n                final_conv_length='auto', )\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we train the network! EEGClassifier is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom [skorch.NeuralNetClassifier](https://skorch.readthedocs.io/\nen/latest/classifier.html)_,\nso the training logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import LRScheduler\nfrom skorch.dataset import ValidSplit\nfrom braindecode import EEGClassifier\n\nbatch_size = 16\nn_epochs = 2\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    optimizer__lr=[],  # This will be handled by GridSearchCV\n    batch_size=batch_size,\n    train_split=ValidSplit(0.2, random_state=seed),\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We use scikit-learn [GridSearchCV](https://scikit-learn.org/stable/modules/generated/\nsklearn.model_selection.GridSearchCV.html)_ to tune hyperparameters.\nTo be able to do this, we slice the braindecode datasets that by default\nreturn a 3-tuple to return X and y, respectively.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>The KFold object splits the datasets based on their\n   length which corresponds to the number of compute windows. In\n   this (trialwise) example this is fine to do. In a cropped setting\n   this is not advisable since this might split compute windows\n   of a single trial into both train and valid set.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import GridSearchCV, KFold\nfrom skorch.helper import SliceDataset\nfrom numpy import array\nimport pandas as pd\n\ntrain_X = SliceDataset(train_set, idx=0)\ntrain_y = array([y for y in SliceDataset(train_set, idx=1)])\ncv = KFold(n_splits=2, shuffle=True, random_state=42)\n\nlearning_rates = [0.00625, 0.0000625]\ndrop_probs = [0.2, 0.5, 0.8]\n\nfit_params = {'epochs': n_epochs}\nparam_grid = {\n    'optimizer__lr': learning_rates,\n    'module__drop_prob': drop_probs\n}\n\n# By setting n_jobs=-1, grid search is performed\n# with all the processors, in this case the output of the training\n# process is not printed sequentially\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=cv,\n    return_train_score=True,\n    scoring='accuracy',\n    refit=True,\n    verbose=1,\n    error_score='raise',\n    n_jobs=1,\n)\n\nsearch.fit(train_X, train_y, **fit_params)\n\n# Extract the results into a DataFrame\nsearch_results = pd.DataFrame(search.cv_results_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plotting the results\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# Create a pivot table for the heatmap\npivot_table = search_results.pivot(index='param_optimizer__lr',\n                                   columns='param_module__drop_prob',\n                                   values='mean_test_score')\n# Create the heatmap\nfig, ax = plt.subplots()\nsns.heatmap(pivot_table, annot=True, fmt=\".3f\",\n            cmap=\"YlGnBu\", cbar=True)\nplt.title('Grid Search Mean Test Scores')\nplt.ylabel('Learning Rate')\nplt.xlabel('Dropout Probability')\nplt.tight_layout()\nplt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get the best hyperparameters\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "best_run = search_results[search_results['rank_test_score'] == 1].squeeze()\nprint(\n    f\"Best hyperparameters were {best_run['params']} which gave a validation \"\n    f\"accuracy of {best_run['mean_test_score'] * 100:.2f}% (training \"\n    f\"accuracy of {best_run['mean_train_score'] * 100:.2f}%).\")\n\neval_X = SliceDataset(eval_set, idx=0)\neval_y = SliceDataset(eval_set, idx=1)\nscore = search.score(eval_X, eval_y)\nprint(f\"Eval accuracy is {score * 100:.2f}%.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,\n      Zachary Nado (2022). Deep Learning Tuning Playbook.\n      Github https://github.com/google-research/tuning_playbook\n\n.. [2] Jayaram, Vinay, and Alexandre Barachant.\n       \"MOABB: trustworthy algorithm benchmarking for BCIs.\"\n       Journal of neural engineering 15.6 (2018): 066011.\n\n.. [3] Tangermann, M., M\u00fcller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,\n       Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.\n       and Nolte, G., 2012. Review of the BCI competition IV.\n       Frontiers in neuroscience, 6, p.55.\n\n.. [4] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,\n       Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb b/stable/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb
new file mode 100644
index 00000000..e8f5e304
--- /dev/null
+++ b/stable/_downloads/a6249715d3c30cb41c4af85938cca008/plot_moabb_dataset_example.ipynb
@@ -0,0 +1,140 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# MOABB Dataset Example\n\nIn this example, we show how to fetch and prepare a MOABB dataset for usage\nwith Braindecode.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#          Hubert Banville <hubert.jbanville@gmail.com>\n#          Simon Brandt <simonbrandt@protonmail.com>\n#          Daniel Wilson <dan.c.wil@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom braindecode.datasets import MOABBDataset\nfrom braindecode.preprocessing import preprocess, Preprocessor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we create a dataset based on BCIC IV 2a fetched with MOABB,\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The dataset has a pandas DataFrame with additional description of its internal datasets\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset.description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can iterate through dataset which yields one time point of a continuous signal x,\nand a target y (which can be None if targets are not defined for the entire\ncontinuous signal).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for x, y in dataset:\n    print(x.shape, y)\n    break"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can apply preprocessing transforms that are defined in mne and work\nin-place, such as resampling, bandpass filtering, or electrode selection.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=True),\n    Preprocessor('resample', sfreq=100)\n]\nprint(dataset.datasets[0].raw.info[\"sfreq\"])\npreprocess(dataset, preprocessors)\nprint(dataset.datasets[0].raw.info[\"sfreq\"])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset based on a criteria applied to the description\nDataFrame:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subsets = dataset.split(\"session\")\nprint({subset_name: len(subset) for subset_name, subset in subsets.items()})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "See our [Trialwise Decoding](../model_building/plot_bcic_iv_2a_moabb_trial.html)_ and\n[Cropped Decoding](../model_building/plot_bcic_iv_2a_moabb_cropped.html)_ examples for\ntraining with this dataset.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/a7671a37e509d66f8a25812de650cb55/plot_hyperparameter_tuning_with_scikit-learn.py b/stable/_downloads/a7671a37e509d66f8a25812de650cb55/plot_hyperparameter_tuning_with_scikit-learn.py
deleted file mode 100644
index df3eac9d..00000000
--- a/stable/_downloads/a7671a37e509d66f8a25812de650cb55/plot_hyperparameter_tuning_with_scikit-learn.py
+++ /dev/null
@@ -1,266 +0,0 @@
-"""
-Hyperparameter tuning with scikit-learn
-=======================================
-
-This tutorial shows you how to tune hyperparameters with scikit-learn
-(GridSearchCV) in the setting of trialwise decoding on dataset
-BCIC IV 2a.
-
-"""
-
-######################################################################
-# Loading and preprocessing the dataset
-# -------------------------------------
-#
-
-
-######################################################################
-# Loading
-# ~~~~~~~
-#
-
-
-######################################################################
-# First, we load the data. In this tutorial, we use the functionality of
-# braindecode to load datasets through
-# `MOABB <https://github.com/NeuroTechX/moabb>`__ to load the BCI
-# Competition IV 2a data.
-#
-# .. note::
-#    To load your own datasets either via mne or from
-#    preprocessed X/y numpy arrays, see `MNE Dataset
-#    Tutorial <./plot_mne_dataset_example.html>`__ and `Numpy Dataset
-#    Tutorial <./plot_custom_dataset_example.html>`__.
-#
-
-from braindecode.datasets.moabb import MOABBDataset
-
-subject_id = 3
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
-
-
-######################################################################
-# Preprocessing
-# ~~~~~~~~~~~~~
-#
-
-
-######################################################################
-# Now we apply preprocessing like bandpass filtering to our dataset. You
-# can either apply functions provided by
-# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
-# `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
-# or apply your own functions, either to the MNE object or the underlying
-# numpy array.
-#
-# .. note::
-#    These prepocessings are now directly applied to the loaded
-#    data, and not on-the-fly applied as transformations in
-#    PyTorch-libraries like
-#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
-#
-
-from braindecode.preprocessing.preprocess import (
-    exponential_moving_standardize, preprocess, Preprocessor)
-from numpy import multiply
-
-low_cut_hz = 4.  # low cut frequency for filtering
-high_cut_hz = 38.  # high cut frequency for filtering
-# Parameters for exponential moving standardization
-factor_new = 1e-3
-init_block_size = 1000
-# Factor to convert from V to uV
-factor = 1e6
-
-preprocessors = [
-    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
-    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
-    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
-    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
-                 factor_new=factor_new, init_block_size=init_block_size)
-]
-
-# Transform the data
-preprocess(dataset, preprocessors)
-
-
-######################################################################
-# Cut Compute Windows
-# ~~~~~~~~~~~~~~~~~~~
-#
-
-
-######################################################################
-# Now we cut out compute windows, the inputs for the deep networks during
-# training. In the case of trialwise decoding, we just have to decide if
-# we want to cut out some part before and/or after the trial. For this
-# dataset, in our work, it often was beneficial to also cut out 500 ms
-# before the trial.
-#
-
-from braindecode.preprocessing.windowers import create_windows_from_events
-
-trial_start_offset_seconds = -0.5
-# Extract sampling frequency, check that they are same in all datasets
-sfreq = dataset.datasets[0].raw.info['sfreq']
-assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
-# Calculate the trial start offset in samples.
-trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
-
-# Create windows using braindecode function for this. It needs parameters to define how
-# trials should be used.
-windows_dataset = create_windows_from_events(
-    dataset,
-    trial_start_offset_samples=trial_start_offset_samples,
-    trial_stop_offset_samples=0,
-    preload=True,
-)
-
-
-######################################################################
-# Split dataset into train and valid
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-#
-
-
-######################################################################
-# We can easily split the dataset using additional info stored in the
-# description attribute, in this case ``session`` column. We select
-# ``session_T`` for training and ``session_E`` for evaluation.
-#
-
-splitted = windows_dataset.split('session')
-train_set = splitted['session_T']
-eval_set = splitted['session_E']
-
-
-######################################################################
-# Create model
-# ------------
-#
-
-
-######################################################################
-# Now we create the deep learning model! Braindecode comes with some
-# predefined convolutional neural network architectures for raw
-# time-domain EEG. Here, we use the shallow ConvNet model from `Deep
-# learning with convolutional neural networks for EEG decoding and
-# visualization <https://arxiv.org/abs/1703.05051>`__. These models are
-# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
-# to use your own model, it just has to be a normal PyTorch
-# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
-#
-
-import torch
-from braindecode.util import set_random_seeds
-from braindecode.models import ShallowFBCSPNet
-
-cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
-device = 'cuda' if cuda else 'cpu'
-if cuda:
-    torch.backends.cudnn.benchmark = True
-seed = 20200220  # random seed to make results reproducible
-# Set random seed to be able to reproduce results
-set_random_seeds(seed=seed, cuda=cuda)
-
-n_classes = 4
-# Extract number of chans and time steps from dataset
-n_chans = train_set[0][0].shape[0]
-input_window_samples = train_set[0][0].shape[1]
-
-model = ShallowFBCSPNet(
-    n_chans,
-    n_classes,
-    input_window_samples=input_window_samples,
-    final_conv_length='auto',
-)
-
-# Send model to GPU
-if cuda:
-    model.cuda()
-
-
-######################################################################
-# Training
-# --------
-#
-
-
-######################################################################
-# Now we train the network! EEGClassifier is a Braindecode object
-# responsible for managing the training of neural networks. It inherits
-# from skorch.NeuralNetClassifier, so the training logic is the same as in
-# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
-#
-
-from skorch.callbacks import LRScheduler
-
-from braindecode import EEGClassifier
-batch_size = 16
-n_epochs = 4
-
-clf = EEGClassifier(
-    model,
-    criterion=torch.nn.NLLLoss,
-    optimizer=torch.optim.AdamW,
-    optimizer__lr=[],
-    batch_size=batch_size,
-    train_split=None,  # train /test split is handled by GridSearchCV
-    callbacks=[
-        "accuracy",
-        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
-    ],
-    device=device,
-)
-
-######################################################################
-# Use scikit-learn GridSearchCV to tune hyperparameters. To be able
-# to do this, we slice the braindecode datasets that by default return
-# a 3-tuple to return X and y, respectively.
-#
-
-######################################################################
-#    **Note**: The KFold object splits the datasets based on their
-#    length which corresponds to the number of compute windows. In
-#    this (trialwise) example this is fine to do. In a cropped setting
-#    this is not advisable since this might split compute windows
-#    of a single trial into both train and valid set.
-#
-
-from sklearn.model_selection import GridSearchCV, KFold
-from skorch.helper import SliceDataset
-from numpy import array
-import pandas as pd
-
-train_X = SliceDataset(train_set, idx=0)
-train_y = array([y for y in SliceDataset(train_set, idx=1)])
-cv = KFold(n_splits=2, shuffle=True, random_state=42)
-
-fit_params = {'epochs': n_epochs}
-param_grid = {
-    'optimizer__lr': [0.00625, 0.000625, 0.0000625],
-}
-search = GridSearchCV(
-    estimator=clf,
-    param_grid=param_grid,
-    cv=cv,
-    return_train_score=True,
-    scoring='accuracy',
-    refit=True,
-    verbose=1,
-    error_score='raise'
-)
-
-search.fit(train_X, train_y, **fit_params)
-
-search_results = pd.DataFrame(search.cv_results_)
-
-best_run = search_results[search_results['rank_test_score'] == 1].squeeze()
-print(f"Best hyperparameters were {best_run['params']} which gave a validation "
-      f"accuracy of {best_run['mean_test_score']*100:.2f}% (training "
-      f"accuracy of {best_run['mean_train_score']*100:.2f}%).")
-
-eval_X = SliceDataset(eval_set, idx=0)
-eval_y = SliceDataset(eval_set, idx=1)
-score = search.score(eval_X, eval_y)
-print(f"Eval accuracy is {score*100:.2f}%.")
diff --git a/stable/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb b/stable/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb
new file mode 100644
index 00000000..cebb2e59
--- /dev/null
+++ b/stable/_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb
@@ -0,0 +1,369 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Fingers flexion decoding on BCIC IV 4 ECoG Dataset\n\nThis tutorial shows you how to train and test deep learning models with\nBraindecode on ECoG BCI IV competition dataset 4. For this dataset we will\npredict 5 regression targets corresponding to flexion of each finger.\nThe targets were recorded as a time series (each 25 Hz), so this tutorial is\nan example of time series target prediction.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Maciej Sliwowski <maciek.sliwowski@gmail.com>\n#          Mohammed Fattouh <mo.fattouh@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preparing the dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load the data. In this tutorial, we use the functionality of braindecode\nto load [BCI IV competition dataset 4](http://www.bbci.de/competition/iv/#dataset4)_.\nThe dataset is available as a part of ECoG library:\nhttps://searchworks.stanford.edu/view/zk881ps0522\n\nThe dataset contains ECoG signal and time series of 5 targets corresponding\nto each finger flexion. This is different than standard decoding setup for EEG with\nmultiple trials and usually one target per trial. Here, fingers flexions change in time\nand are recorded with sampling frequency equals to 25 Hz.\n\nIf this dataset is used please cite [1].\n\n[1] Miller, Kai J. \"A library of human electrocorticographic data and analyses.\n\"Nature human behaviour 3, no. 11 (2019): 1225-1235. https://doi.org/10.1038/s41562-019-0678-3\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom braindecode.datasets import BCICompetitionIVDataset4\n\nsubject_id = 1\ndataset = BCICompetitionIVDataset4(subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Preprocessing steps are taken from a standard EEG processing pipeline.\n   The only change is the cutoff frequency of the filter. For a proper ECoG\n   decoding other preprocessing steps may be needed.</p></div>\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import (Preprocessor,\n                                       exponential_moving_standardize,\n                                       preprocess)\n\nlow_cut_hz = 1.  # low cut frequency for filtering\nhigh_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We select only first 30 seconds from each dataset to limit time and memory\nto run this example. To obtain results on the whole datasets you should remove this line.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocess(dataset, [Preprocessor('crop', tmin=0, tmax=30)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In time series targets setup, targets variables are stored in mne.Raw object as channels\nof type `misc`. Thus those channels have to be selected for further processing. However,\nmany mne functions ignore `misc` channels and perform operations only on data channels\n(see https://mne.tools/stable/glossary.html#term-data-channels).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocessors = [\n    Preprocessor('pick_types', ecog=True, misc=True),\n    Preprocessor(lambda x: x / 1e6, picks='ecog'),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size, picks='ecog')\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)\n\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Extract target sampling frequency\ntarget_sfreq = dataset.datasets[0].raw.info['temp']['target_sfreq']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we cut out compute windows, the inputs for the deep networks during\ntraining. In the case of trialwise decoding of time series targets, we just have to\ndecide about length windows that will be selected from the signal preceding each target.\nWe use different windowing function than in standard trialwise decoding as our targets\nare stored as target channels in mne.Raw.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_target_channels\n\nwindows_dataset = create_windows_from_target_channels(\n    dataset,\n    window_size_samples=1000,\n    preload=False,\n    last_target_only=True\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We select only the thumb's finger flexion to create one model per finger.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Methods to predict all 5 fingers flexion with the same model may be cnosidered as well.\n   We encourage you to find your own way to use braindecode models to predict finers fexions.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows_dataset.target_transform = lambda x: x[0: 1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train, valid, and test\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select `train` dataset\nfor training and validation and `test` for final evaluation.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "subsets = windows_dataset.split('session')\ntrain_set = subsets['train']\ntest_set = subsets['test']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can split train dataset into training and validation datasets using\n``sklearn.model_selection.train_test_split`` and ``torch.utils.data.Subset``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom sklearn.model_selection import train_test_split\n\nidx_train, idx_valid = train_test_split(np.arange(len(train_set)),\n                                        random_state=100,\n                                        test_size=0.2,\n                                        shuffle=False)\n\nvalid_set = torch.utils.data.Subset(train_set, idx_valid)\ntrain_set = torch.utils.data.Subset(train_set, idx_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_out_chans = train_set[0][1].shape[0]\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = 1000  # 1 second long windows\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_out_chans,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n    add_log_softmax=False,\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we train the network! EEGRegressor is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom skorch.NeuralNetRegressor, so the training logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>In this tutorial, we use some default parameters that we\n   have found to work well for EEG motor decoding, however we strongly\n   encourage you to perform your own hyperparameter and preprocessing optimization using\n   cross validation on your training data.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.callbacks import EpochScoring, LRScheduler\nfrom skorch.helper import predefined_split\nfrom mne import set_log_level\n\nfrom braindecode import EEGRegressor\n\n# These values we found good for shallow network for EEG MI decoding:\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2\n\n\n# Function to compute Pearson correlation coefficient\ndef pearson_r_score(net, dataset, y):\n    preds = net.predict(dataset)\n    corr_coeffs = []\n    for i in range(y.shape[1]):\n        corr_coeffs.append(np.corrcoef(y[:, i], preds[:, i])[0, 1])\n    return np.mean(corr_coeffs)\n\n\nregressor = EEGRegressor(\n    model,\n    criterion=torch.nn.MSELoss,\n    optimizer=torch.optim.AdamW,\n    train_split=predefined_split(valid_set),  # using valid_set for validation,\n    optimizer__lr=lr,\n    optimizer__weight_decay=weight_decay,\n    batch_size=batch_size,\n    callbacks=[\n        'r2',\n        ('valid_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=False,\n                                         name='valid_pearson_r')),\n        ('train_pearson_r', EpochScoring(pearson_r_score, lower_is_better=False, on_train=True,\n                                         name='train_pearson_r')),\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)\nset_log_level(verbose='WARNING')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Model training for a specified number of epochs. ``y`` is None as it is already supplied\nin the dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "regressor.fit(train_set, y=None, epochs=n_epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Obtaining predictions and targets for the test, train, and validation dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preds_test = regressor.predict(test_set)\ny_test = np.stack([data[1] for data in test_set])\npreds_train = regressor.predict(train_set)\ny_train = np.stack([data[1] for data in train_set])\npreds_valid = regressor.predict(valid_set)\ny_valid = np.stack([data[1] for data in valid_set])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot Results\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We plot target and predicted finger flexion on training, validation, and test sets.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The model is trained and validated on limited dataset (to decrease the time needed to run\n   this example) which does not contain diverse dataset in terms of fingers flexions and may\n   cause overfitting. To obtain better results use whole dataset as well as improve the decoding\n   pipeline which may be not optimal for ECoG.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom matplotlib.lines import Line2D\n\nfig, axes = plt.subplots(3, 1, figsize=(8, 9))\n\naxes[0].set_title('Training dataset')\naxes[0].plot(np.arange(0, y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')\naxes[0].plot(np.arange(0, preds_train.shape[0]) / target_sfreq, preds_train[:, 0],\n             label='Predicted')\naxes[0].set_ylabel('Finger flexion')\naxes[0].legend()\n\naxes[1].set_title('Validation dataset')\naxes[1].plot(np.arange(0, y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')\naxes[1].plot(np.arange(0, preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],\n             label='Predicted')\naxes[1].set_ylabel('Finger flexion')\naxes[1].legend()\n\naxes[2].set_title('Test dataset')\naxes[2].plot(np.arange(0, y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')\naxes[2].plot(np.arange(0, preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')\naxes[2].set_xlabel('Time [s]')\naxes[2].set_ylabel('Finger flexion')\naxes[2].legend()\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can compute correlation coefficients for each finger\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "corr_coeffs = []\nfor dim in range(y_test.shape[1]):\n    corr_coeffs.append(\n        np.corrcoef(preds_test[:, dim], y_test[:, dim])[0, 1]\n    )\nprint('Correlation coefficient for each dimension: ', np.round(corr_coeffs, 2))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we use the history stored by Skorch throughout training to plot\naccuracy and loss curves.\nExtract loss and accuracy values for plotting from history object\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "results_columns = ['train_loss', 'valid_loss', 'train_pearson_r', 'valid_pearson_r']\ndf = pd.DataFrame(regressor.history[:, results_columns], columns=results_columns,\n                  index=regressor.history[:, 'epoch'])\n\nfig, ax1 = plt.subplots(figsize=(8, 4))\ndf.loc[:, ['train_loss', 'valid_loss']].plot(\n    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)\n\nax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\nax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n\nax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\ndf.loc[:, ['train_pearson_r', 'valid_pearson_r']].plot(\n    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)\nax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\nax2.set_ylabel(\"Pearson correlation coefficient\", color='tab:red', fontsize=14)\nax1.set_xlabel(\"Epoch\", fontsize=14)\n\n# where some data has already been plotted to ax\nhandles = []\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-',\n                      label='Train'))\nhandles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':',\n                      label='Valid'))\nplt.legend(handles, [h.get_label() for h in handles], fontsize=14, loc='center right')\nplt.tight_layout()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb b/stable/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb
new file mode 100644
index 00000000..f03b191d
--- /dev/null
+++ b/stable/_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb
@@ -0,0 +1,243 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Training a Braindecode model in PyTorch\n\nThis tutorial shows you how to train a Braindecode model with PyTorch. The data\npreparation and model instantiation steps are identical to that of the tutorial\n[How to train, test and tune your model](./plot_how_train_test_and_tune.html)_\n\nWe will use the BCIC IV 2a dataset as a showcase example.\n\nThe methods shown can be applied to any standard supervised trial-based decoding setting.\nThis tutorial will include additional parts of code like loading and preprocessing,\ndefining a model, and other details which are not exclusive to this page (compare\n[Cropped Decoding Tutorial](./plot_bcic_iv_2a_moabb_trial.html)_). Therefore we\nwill not further elaborate on these parts and you can feel free to skip them.\n\nThe goal of this tutorial is to present braindecode in the PyTorch perceptive.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Why should I care about model evaluation?\nShort answer: To produce reliable results!\n\nIn machine learning, we usually follow the scheme of splitting the\ndata into two parts, training and testing sets. It sounds like a\nsimple division, right? But the story does not end here.\n\nWhile developing a ML model you usually have to adjust and tune\nhyperparameters of your model or pipeline (e.g., number of layers,\nlearning rate, number of epochs). Deep learning models usually have\nmany free parameters; they could be considered complex models with\nmany degrees of freedom. If you kept using the test dataset to\nevaluate your adjustmentyou would run into data leakage.\n\nThis means that if you use the test set to adjust the hyperparameters\nof your model, the model implicitly learns or memorizes the test set.\nTherefore, the trained model is no longer independent of the test set\n(even though it was never used for training explicitly!).\nIf you perform any hyperparameter tuning, you need a third split,\nthe so-called validation set.\n\nThis tutorial shows the three basic schemes for training and evaluating\nthe model as well as two methods to tune your hyperparameters.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>You might recognize that the accuracy gets better throughout\n   the experiments of this tutorial. The reason behind that is that\n   we always use the same model with the same parameters in every\n   segment to keep the tutorial short and readable. If you do your\n   own experiments you always have to reinitialize the model before\n   training.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading, preprocessing, defining a model, etc.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Loading the Dataset Structure\nHere, we have a data structure with equal behavior to the Pytorch Dataset.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014_001\", subject_ids=[subject_id])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing, the offline transformation of the raw dataset\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n\nfrom braindecode.preprocessing import (\n    exponential_moving_standardize,\n    preprocess,\n    Preprocessor,\n)\n\nlow_cut_hz = 4.0  # low cut frequency for filtering\nhigh_cut_hz = 38.0  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n\ntransforms = [\n    Preprocessor(\"pick_types\", eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(\n        lambda data, factor: np.multiply(data, factor),  # Convert from V to uV\n        factor=1e6,\n    ),\n    Preprocessor(\"filter\", l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(\n        exponential_moving_standardize,  # Exponential moving standardization\n        factor_new=factor_new,\n        init_block_size=init_block_size,\n    ),\n]\n\n# Transform the data\npreprocess(dataset, transforms, n_jobs=-1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Cut Compute Windows\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info[\"sfreq\"]\nassert all([ds.raw.info[\"sfreq\"] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create Pytorch model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom braindecode.models import ShallowFBCSPNet\nfrom braindecode.util import set_random_seeds\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = \"cuda\" if cuda else \"cpu\"\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\nclasses = list(range(n_classes))\n# Extract number of chans and time steps from dataset\nn_channels = windows_dataset[0][0].shape[0]\ninput_window_samples = windows_dataset[0][0].shape[1]\n\n# The ShallowFBCSPNet is a `nn.Sequential` model\n\nmodel = ShallowFBCSPNet(\n    n_channels,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length=\"auto\",\n)\n\n# Display torchinfo table describing the model\nprint(model)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## How to train and evaluate your model\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Split dataset into train and test\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case the ``session`` column. We\nselect ``Train`` for training and ``test`` for testing.\nFor other datasets, you might have to choose another column.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>No matter which of the three schemes you use, this initial\n   two-fold split into train_set and test_set always remains the same.\n   Remember that you are not allowed to use the test_set during any\n   stage of training or tuning.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splitted = windows_dataset.split(\"session\")\ntrain_set = splitted['0train']  # Session train\ntest_set = splitted['1test']  # Session evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 1: Pure PyTorch training loop\n\n<img src=\"https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png\" alt=\"Pytorch logo\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "`model` is an instance of `torch.nn.Module`, and can as such be trained\nusing PyTorch optimization capabilities.\nThe following training scheme is simple as the dataset is only\nsplit into two distinct sets (``train_set`` and ``test_set``).\nThis scheme uses no separate validation split and should only be\nused for the final evaluation of the (previously!) found\nhyperparameters configuration.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>If you make any use of the ``test_set`` during training\n   (e.g. by using EarlyStopping) there will be data leakage\n   which will make the reported generalization capability/decoding\n   performance of your model less credible.</p></div>\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>The parameter values showcased here for optimizing the network are\n   chosen to make this tutorial fast to run and build. Real-world values\n   would be higher, especially when it comes to n_epochs.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from torch.nn import Module\nfrom torch.optim.lr_scheduler import LRScheduler\nfrom torch.utils.data import DataLoader\n\nlr = 0.0625 * 0.01\nweight_decay = 0\nbatch_size = 64\nn_epochs = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following method runs one training epoch over the dataloader for the\ngiven model. It needs a loss function, optimization algorithm, and\nlearning rate updating callback.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm import tqdm\n# Define a method for training one epoch\n\n\ndef train_one_epoch(\n        dataloader: DataLoader, model: Module, loss_fn, optimizer,\n        scheduler: LRScheduler, epoch: int, device, print_batch_stats=True\n):\n    model.train()  # Set the model to training mode\n    train_loss, correct = 0, 0\n\n    progress_bar = tqdm(enumerate(dataloader), total=len(dataloader),\n                        disable=not print_batch_stats)\n\n    for batch_idx, (X, y, _) in progress_bar:\n        X, y = X.to(device), y.to(device)\n        optimizer.zero_grad()\n        pred = model(X)\n        loss = loss_fn(pred, y)\n        loss.backward()\n        optimizer.step()  # update the model weights\n        optimizer.zero_grad()\n\n        train_loss += loss.item()\n        correct += (pred.argmax(1) == y).sum().item()\n\n        if print_batch_stats:\n            progress_bar.set_description(\n                f\"Epoch {epoch}/{n_epochs}, \"\n                f\"Batch {batch_idx + 1}/{len(dataloader)}, \"\n                f\"Loss: {loss.item():.6f}\"\n            )\n\n    # Update the learning rate\n    scheduler.step()\n\n    correct /= len(dataloader.dataset)\n    return train_loss / len(dataloader), correct"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Very similarly, the evaluation function loops over the entire dataloader\nand accumulate the metrics, but doesn't update the model weights.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "@torch.no_grad()\ndef test_model(\n    dataloader: DataLoader, model: Module, loss_fn, print_batch_stats=True\n):\n    size = len(dataloader.dataset)\n    n_batches = len(dataloader)\n    model.eval()  # Switch to evaluation mode\n    test_loss, correct = 0, 0\n\n    if print_batch_stats:\n        progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))\n    else:\n        progress_bar = enumerate(dataloader)\n\n    for batch_idx, (X, y, _) in progress_bar:\n        X, y = X.to(device), y.to(device)\n        pred = model(X)\n        batch_loss = loss_fn(pred, y).item()\n\n        test_loss += batch_loss\n        correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n\n        if print_batch_stats:\n            progress_bar.set_description(\n                f\"Batch {batch_idx + 1}/{len(dataloader)}, \"\n                f\"Loss: {batch_loss:.6f}\"\n            )\n\n    test_loss /= n_batches\n    correct /= size\n\n    print(\n        f\"Test Accuracy: {100 * correct:.1f}%, Test Loss: {test_loss:.6f}\\n\"\n    )\n    return test_loss, correct\n\n\n# Define the optimization\noptimizer = torch.optim.AdamW(model.parameters(),\n                              lr=lr, weight_decay=weight_decay)\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                       T_max=n_epochs - 1)\n# Define the loss function\n# We used the NNLoss function, which expects log probabilities as input\n# (which is the case for our model output)\nloss_fn = torch.nn.NLLLoss()\n\n# train_set and test_set are instances of torch Datasets, and can seamlessly be\n# wrapped in data loaders.\ntrain_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\ntest_loader = DataLoader(test_set, batch_size=batch_size)\n\nfor epoch in range(1, n_epochs + 1):\n    print(f\"Epoch {epoch}/{n_epochs}: \", end=\"\")\n\n    train_loss, train_accuracy = train_one_epoch(\n        train_loader, model, loss_fn, optimizer, scheduler, epoch, device,\n    )\n\n    test_loss, test_accuracy = test_model(test_loader, model, loss_fn)\n\n    print(\n        f\"Train Accuracy: {100 * train_accuracy:.2f}%, \"\n        f\"Average Train Loss: {train_loss:.6f}, \"\n        f\"Test Accuracy: {100 * test_accuracy:.1f}%, \"\n        f\"Average Test Loss: {test_loss:.6f}\\n\"\n    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Option 2: Train it with PyTorch Lightning\n<img src=\"https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png\" alt=\"Pytorch Lightning logo\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Alternatively, lightning provides a nice interface around torch modules\nwhich integrates the previous logic.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import lightning as L\nfrom torchmetrics.functional import accuracy\n\n\nclass LitModule(L.LightningModule):\n    def __init__(self, module):\n        super().__init__()\n        self.module = module\n        self.loss = torch.nn.NLLLoss()\n\n    def training_step(self, batch, batch_idx):\n        x, y, _ = batch\n        y_hat = self.module(x)\n        loss = self.loss(y_hat, y)\n        self.log(\"train_loss\", loss)\n        return loss\n\n    def test_step(self, batch, batch_idx):\n        x, y, _ = batch\n        y_hat = self.module(x)\n        loss = self.loss(y_hat, y)\n        acc = accuracy(y_hat, y, \"multiclass\", num_classes=4)\n        metrics = {\"test_acc\": acc, \"test_loss\": loss}\n        self.log_dict(metrics)\n        return metrics\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.AdamW(model.parameters(), lr=lr,\n                                      weight_decay=weight_decay)\n        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,\n                                                               T_max=n_epochs - 1)\n        return [optimizer], [scheduler]\n\n\n# Creating the trainer with max_epochs=2 for demonstration purposes\ntrainer = L.Trainer(max_epochs=n_epochs)\n# Create and train the LightningModule\nlit_model = LitModule(model)\ntrainer.fit(lit_model, train_loader)\n\n# After training, you can test the model using the test DataLoader\ntrainer.test(dataloaders=test_loader)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb b/stable/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb
new file mode 100644
index 00000000..7cf096be
--- /dev/null
+++ b/stable/_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb
@@ -0,0 +1,266 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Self-supervised learning on EEG with relative positioning\n\nThis example shows how to train a neural network with self-supervision on sleep\nEEG data. We follow the relative positioning approach of [1]_ on the openly\naccessible Sleep Physionet dataset [2]_ [3]_.\n\n.. topic:: Self-supervised learning\n\n    Self-supervised learning (SSL) is a learning paradigm that leverages\n    unlabelled data to train neural networks. First, neural networks are\n    trained on a \"pretext task\" which uses unlabelled data only. The pretext\n    task is designed based on a prior understanding of the data under study\n    (e.g., EEG has an underlying autocorrelation structure) and such that the\n    processing required to perform well on this pretext task is related to the\n    processing required to perform well on another task of interest.\n    Once trained, these neural networks can be reused as feature extractors or\n    weight initialization in a \"downstream task\", which is the task that we are\n    actually interested in (e.g., sleep staging). The pretext task step can\n    help reduce the quantity of labelled data needed to perform well on the\n    downstream task and/or improve downstream performance as compared to a\n    strictly supervised approach [1]_.\n\nHere, we use relative positioning (RP) as our pretext task, and perform sleep\nstaging as our downstream task. RP is a simple SSL task, in which a neural\nnetwork is trained to predict whether two randomly sampled EEG windows are\nclose or far apart in time. This method was shown to yield physiologically- and\nclinically-relevant features and to boost classification performance in\nlow-labels data regimes [1]_.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Hubert Banville <hubert.jbanville@gmail.com>\n#\n# License: BSD (3-clause)\n\n\nrandom_state = 87\nn_jobs = 1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading and preprocessing the dataset\n\n### Loading the raw recordings\n\nFirst, we load a few recordings from the Sleep Physionet dataset. Running\nthis example with more recordings should yield better representations and\ndownstream classification performance.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.datasets.sleep_physionet import SleepPhysionet\n\ndataset = SleepPhysionet(\n    subject_ids=[0, 1, 2], recording_ids=[1], crop_wake_mins=30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing\n\nNext, we preprocess the raw data. We convert the data to microvolts and apply\na lowpass filter. Since the Sleep Physionet data is already sampled at 100 Hz\nwe don't need to apply resampling.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.preprocess import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz, n_jobs=n_jobs)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Extracting windows\n\nWe extract 30-s windows to be used in both the pretext and downstream tasks.\nAs RP (and SSL in general) don't require labelled data, the pretext task\ncould be performed using unlabelled windows extracted with\n:func:`braindecode.datautil.windower.create_fixed_length_window`.\nHere however, purely for convenience, we directly extract labelled windows so\nthat we can reuse them in the sleep staging downstream task later.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.preprocessing.windowers import create_windows_from_events\n\nwindow_size_s = 30\nsfreq = 100\nwindow_size_samples = window_size_s * sfreq\n\nmapping = {  # We merge stages 3 and 4 following AASM standards.\n    'Sleep stage W': 0,\n    'Sleep stage 1': 1,\n    'Sleep stage 2': 2,\n    'Sleep stage 3': 3,\n    'Sleep stage 4': 3,\n    'Sleep stage R': 4\n}\n\nwindows_dataset = create_windows_from_events(\n    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_size_samples, preload=True, mapping=mapping)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Preprocessing windows\n\nWe also preprocess the windows by applying channel-wise z-score normalization.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import scale as standard_scale\n\npreprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Splitting dataset into train, valid and test sets\n\nWe randomly split the recordings by subject into train, validation and\ntesting sets. We further define a new Dataset class which can receive a pair\nof indices and return the corresponding windows. This will be needed when\ntraining and evaluating on the pretext task.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom braindecode.datasets import BaseConcatDataset\n\nsubjects = np.unique(windows_dataset.description['subject'])\nsubj_train, subj_test = train_test_split(\n    subjects, test_size=0.4, random_state=random_state)\nsubj_valid, subj_test = train_test_split(\n    subj_test, test_size=0.5, random_state=random_state)\n\n\nclass RelativePositioningDataset(BaseConcatDataset):\n    \"\"\"BaseConcatDataset with __getitem__ that expects 2 indices and a target.\n    \"\"\"\n\n    def __init__(self, list_of_ds):\n        super().__init__(list_of_ds)\n        self.return_pair = True\n\n    def __getitem__(self, index):\n        if self.return_pair:\n            ind1, ind2, y = index\n            return (super().__getitem__(ind1)[0],\n                    super().__getitem__(ind2)[0]), y\n        else:\n            return super().__getitem__(index)\n\n    @property\n    def return_pair(self):\n        return self._return_pair\n\n    @return_pair.setter\n    def return_pair(self, value):\n        self._return_pair = value\n\n\nsplit_ids = {'train': subj_train, 'valid': subj_valid, 'test': subj_test}\nsplitted = dict()\nfor name, values in split_ids.items():\n    splitted[name] = RelativePositioningDataset(\n        [ds for ds in windows_dataset.datasets\n         if ds.description['subject'] in values])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Creating samplers\n\nNext, we need to create samplers. These samplers will be used to randomly\nsample pairs of examples to train and validate our model with\nself-supervision.\n\nThe RP samplers have two main hyperparameters. `tau_pos` and `tau_neg`\ncontrol the size of the \"positive\" and \"negative\" contexts, respectively.\nPairs of windows that are separated by less than `tau_pos` samples will be\ngiven a label of `1`, while pairs of windows that are separated by more than\n`tau_neg` samples will be given a label of `0`. Here, we use the same values\nas in [1]_, i.e., `tau_pos`= 1 min and `tau_neg`= 15 mins.\n\nThe samplers also control the number of pairs to be sampled (defined with\n`n_examples`). This number can be large to help regularize the pretext task\ntraining, for instance 2,000 pairs per recording as in [1]_. Here, we use a\nlower number of 250 pairs per recording to reduce training time.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.samplers import RelativePositioningSampler\n\ntau_pos, tau_neg = int(sfreq * 60), int(sfreq * 15 * 60)\nn_examples_train = 250 * len(splitted['train'].datasets)\nn_examples_valid = 250 * len(splitted['valid'].datasets)\nn_examples_test = 250 * len(splitted['test'].datasets)\n\ntrain_sampler = RelativePositioningSampler(\n    splitted['train'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,\n    n_examples=n_examples_train, same_rec_neg=True, random_state=random_state)\nvalid_sampler = RelativePositioningSampler(\n    splitted['valid'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,\n    n_examples=n_examples_valid, same_rec_neg=True,\n    random_state=random_state).presample()\ntest_sampler = RelativePositioningSampler(\n    splitted['test'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,\n    n_examples=n_examples_test, same_rec_neg=True,\n    random_state=random_state).presample()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Creating the model\n\nWe can now create the deep learning model. In this tutorial, we use a\nmodified version of the sleep staging architecture introduced in [4]_ -\na four-layer convolutional neural network - as our embedder.\nWe change the dimensionality of the last layer to obtain a 100-dimension\nembedding, use 16 convolutional channels instead of 8, and add batch\nnormalization after both temporal convolution layers.\n\nWe further wrap the model into a siamese architecture using the\n# :class:`ContrastiveNet` class defined below. This allows us to train the\nfeature extractor end-to-end.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom torch import nn\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import SleepStagerChambon2018\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nif device == 'cuda':\n    torch.backends.cudnn.benchmark = True\n# Set random seed to be able to roughly reproduce results\n# Note that with cudnn benchmark set to True, GPU indeterminism\n# may still make results substantially different between runs.\n# To obtain more consistent results at the cost of increased computation time,\n# you can set `cudnn_benchmark=False` in `set_random_seeds`\n# or remove `torch.backends.cudnn.benchmark = True`\nset_random_seeds(seed=random_state, cuda=device == 'cuda')\n\n# Extract number of channels and time steps from dataset\nn_channels, input_size_samples = windows_dataset[0][0].shape\nemb_size = 100\nclasses = list(range(5))\n\nemb = SleepStagerChambon2018(\n    n_channels,\n    sfreq,\n    n_outputs=emb_size,\n    n_conv_chs=16,\n    n_times=input_size_samples,\n    dropout=0,\n    apply_batch_norm=True,\n)\n\n\nclass ContrastiveNet(nn.Module):\n    \"\"\"Contrastive module with linear layer on top of siamese embedder.\n\n    Parameters\n    ----------\n    emb : nn.Module\n        Embedder architecture.\n    emb_size : int\n        Output size of the embedder.\n    dropout : float\n        Dropout rate applied to the linear layer of the contrastive module.\n    \"\"\"\n\n    def __init__(self, emb, emb_size, dropout=0.5):\n        super().__init__()\n        self.emb = emb\n        self.clf = nn.Sequential(\n            nn.Dropout(dropout),\n            nn.Linear(emb_size, 1)\n        )\n\n    def forward(self, x):\n        x1, x2 = x\n        z1, z2 = self.emb(x1), self.emb(x2)\n        return self.clf(torch.abs(z1 - z2)).flatten()\n\n\nmodel = ContrastiveNet(emb, emb_size).to(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training\n\nWe can now train our network on the pretext task. We use similar\nhyperparameters as in [1]_, but reduce the number of epochs and\nincrease the learning rate to account for the smaller setting of\nthis example.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\n\nfrom skorch.helper import predefined_split\nfrom skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring\nfrom braindecode import EEGClassifier\n\nlr = 5e-3\nbatch_size = 128  # 512 if data large enough\nn_epochs = 25\nnum_workers = 0 if n_jobs <= 1 else n_jobs\n\ncp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)\nearly_stopping = EarlyStopping(patience=10)\ntrain_acc = EpochScoring(\n    scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)\n\ncallbacks = [\n    ('cp', cp),\n    ('patience', early_stopping),\n    ('train_acc', train_acc),\n]\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.BCEWithLogitsLoss,\n    optimizer=torch.optim.Adam,\n    max_epochs=n_epochs,\n    iterator_train__shuffle=False,\n    iterator_train__sampler=train_sampler,\n    iterator_valid__sampler=valid_sampler,\n    iterator_train__num_workers=num_workers,\n    iterator_valid__num_workers=num_workers,\n    train_split=predefined_split(splitted['valid']),\n    optimizer__lr=lr,\n    batch_size=batch_size,\n    callbacks=callbacks,\n    device=device,\n    classes=classes,\n)\n# Model training for a specified number of epochs. `y` is None as it is already\n# supplied in the dataset.\nclf.fit(splitted['train'], y=None)\nclf.load_params(checkpoint=cp)  # Load the model with the lowest valid_loss\n\nos.remove('./params.pt')  # Delete parameters file"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualizing the results\n\n### Inspecting pretext task performance\n\nWe plot the loss and pretext task performance for the training and validation\nsets.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\nimport pandas as pd\n\n# Extract loss and balanced accuracy values for plotting from history object\ndf = pd.DataFrame(clf.history.to_list())\n\ndf['train_acc'] *= 100\ndf['valid_acc'] *= 100\n\nys1 = ['train_loss', 'valid_loss']\nys2 = ['train_acc', 'valid_acc']\nstyles = ['-', ':']\nmarkers = ['.', '.']\n\nfig, ax1 = plt.subplots(figsize=(8, 3))\nax2 = ax1.twinx()\nfor y1, y2, style, marker in zip(ys1, ys2, styles, markers):\n    ax1.plot(df['epoch'], df[y1], ls=style, marker=marker, ms=7,\n             c='tab:blue', label=y1)\n    ax2.plot(df['epoch'], df[y2], ls=style, marker=marker, ms=7,\n             c='tab:orange', label=y2)\n\nax1.tick_params(axis='y', labelcolor='tab:blue')\nax1.set_ylabel('Loss', color='tab:blue')\nax2.tick_params(axis='y', labelcolor='tab:orange')\nax2.set_ylabel('Accuracy [%]', color='tab:orange')\nax1.set_xlabel('Epoch')\n\nlines1, labels1 = ax1.get_legend_handles_labels()\nlines2, labels2 = ax2.get_legend_handles_labels()\nax2.legend(lines1 + lines2, labels1 + labels2)\n\nplt.tight_layout()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We also display the confusion matrix and classification report for the\npretext task:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\n\n# Switch to the test sampler\nclf.iterator_valid__sampler = test_sampler\ny_pred = clf.forward(splitted['test'], training=False) > 0\ny_true = [y for _, _, y in test_sampler]\n\nprint(confusion_matrix(y_true, y_pred))\nprint(classification_report(y_true, y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Using the learned representation for sleep staging\n\nWe can now use the trained convolutional neural network as a feature\nextractor. We perform sleep stage classification from the learned feature\nrepresentation using a linear logistic regression classifier.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import DataLoader\nfrom sklearn.metrics import balanced_accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import make_pipeline\n\n# Extract features with the trained embedder\ndata = dict()\nfor name, split in splitted.items():\n    split.return_pair = False  # Return single windows\n    loader = DataLoader(split, batch_size=batch_size, num_workers=num_workers)\n    with torch.no_grad():\n        feats = [emb(batch_x.to(device)).cpu().numpy()\n                 for batch_x, _, _ in loader]\n    data[name] = (np.concatenate(feats), split.get_metadata()['target'].values)\n\n# Initialize the logistic regression model\nlog_reg = LogisticRegression(\n    penalty='l2', C=1.0, class_weight='balanced', solver='lbfgs',\n    multi_class='multinomial', random_state=random_state)\nclf_pipe = make_pipeline(StandardScaler(), log_reg)\n\n# Fit and score the logistic regression\nclf_pipe.fit(*data['train'])\ntrain_y_pred = clf_pipe.predict(data['train'][0])\nvalid_y_pred = clf_pipe.predict(data['valid'][0])\ntest_y_pred = clf_pipe.predict(data['test'][0])\n\ntrain_bal_acc = balanced_accuracy_score(data['train'][1], train_y_pred)\nvalid_bal_acc = balanced_accuracy_score(data['valid'][1], valid_y_pred)\ntest_bal_acc = balanced_accuracy_score(data['test'][1], test_y_pred)\n\nprint('Sleep staging performance with logistic regression:')\nprint(f'Train bal acc: {train_bal_acc:0.4f}')\nprint(f'Valid bal acc: {valid_bal_acc:0.4f}')\nprint(f'Test bal acc: {test_bal_acc:0.4f}')\n\nprint('Results on test set:')\nprint(confusion_matrix(data['test'][1], test_y_pred))\nprint(classification_report(data['test'][1], test_y_pred))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The balanced accuracy is much higher than chance-level (i.e., 20% for our\n5-class classification problem). Finally, we perform a quick 2D visualization\nof the feature space using a PCA:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.decomposition import PCA\nfrom matplotlib import cm\n\nX = np.concatenate([v[0] for k, v in data.items()])\ny = np.concatenate([v[1] for k, v in data.items()])\n\npca = PCA(n_components=2)\n# tsne = TSNE(n_components=2)\ncomponents = pca.fit_transform(X)\n\nfig, ax = plt.subplots()\ncolors = cm.get_cmap('viridis', 5)(range(5))\nfor i, stage in enumerate(['W', 'N1', 'N2', 'N3', 'R']):\n    mask = y == i\n    ax.scatter(components[mask, 0], components[mask, 1], s=10, alpha=0.7,\n               color=colors[i], label=stage)\nax.legend()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We see that there is sleep stage-related structure in the embedding. A\nnonlinear projection method (e.g., tSNE, UMAP) might yield more insightful\nvisualizations. Using a similar approach, the embedding space could also be\nexplored with respect to subject-level features, e.g., age and sex.\n\n## Conclusion\n\nIn this example, we used self-supervised learning (SSL) as a way to learn\nrepresentations from unlabelled raw EEG data. Specifically, we used the\nrelative positioning (RP) pretext task to train a feature extractor on a\nsubset of the Sleep Physionet dataset. We then reused these features in a\ndownstream sleep staging task. We achieved reasonable downstream performance\nand further showed with a 2D projection that the learned embedding space\ncontained sleep-related structure.\n\nMany avenues could be taken to improve on these results. For instance, using\nthe entire Sleep Physionet dataset or training on larger datasets should help\nthe feature extractor learn better representations during the pretext task.\nOther SSL tasks such as those described in [1]_ could further help discover\nmore powerful features.\n\n\n## References\n\n.. [1] Banville, H., Chehab, O., Hyv\u00e4rinen, A., Engemann, D. A., & Gramfort, A.\n      (2020). Uncovering the structure of clinical EEG signals with\n      self-supervised learning. arXiv preprint arXiv:2007.16104.\n\n.. [2] Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A., & Oberye, J. J.\n       (2000). Analysis of a sleep-dependent neuronal feedback loop: the\n       slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical\n       Engineering, 47(9), 1185-1194.\n\n.. [3] Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov,\n       P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank,\n       PhysioToolkit, and PhysioNet: components of a new research resource for\n       complex physiologic signals. circulation, 101(23), e215-e220.\n\n.. [4] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.\n      (2018)A Deep Learning Architecture for Temporal Sleep Stage\n      Classification Using Multivariate and Multimodal Time Series.\n      IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:\n      (758-769)\n\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py b/stable/_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py
new file mode 100644
index 00000000..8eb185e3
--- /dev/null
+++ b/stable/_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py
@@ -0,0 +1,64 @@
+"""
+Custom Dataset Example
+======================
+
+This example shows how to convert data X and y as numpy arrays to a braindecode
+compatible data format.
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+import mne
+
+from braindecode.datasets import create_from_X_y
+
+###############################################################################
+# To set up the example, we first fetch some data using mne:
+
+# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet
+subject_id = 22
+event_codes = [5, 6, 9, 10, 13, 14]
+# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
+
+# This will download the files if you don't have them yet,
+# and then return the paths to the files.
+physionet_paths = mne.datasets.eegbci.load_data(
+    subject_id, event_codes, update_path=False)
+
+# Load each of the files
+parts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')
+         for path in physionet_paths]
+
+###############################################################################
+# We take the required data, targets and additional information sampling
+# frequency and channel names from the loaded data. Note that this data and
+# information can originate from any source.
+X = [raw.get_data() for raw in parts]
+y = event_codes
+sfreq = parts[0].info["sfreq"]
+ch_names = parts[0].info["ch_names"]
+
+###############################################################################
+# Convert to data format compatible with skorch and braindecode:
+windows_dataset = create_from_X_y(
+    X, y, drop_last_window=False, sfreq=sfreq, ch_names=ch_names,
+    window_stride_samples=500,
+    window_size_samples=500,
+)
+
+windows_dataset.description  # look as dataset description
+
+###############################################################################
+# You can manipulate the dataset
+print(len(windows_dataset))  # get the number of samples
+
+###############################################################################
+# You can now index the data
+i = 0
+x_i, y_i, window_ind = windows_dataset[0]
+n_channels, n_times = x_i.shape  # the EEG data
+_, start_ind, stop_ind = window_ind
+print(f"n_channels={n_channels}  -- n_times={n_times} -- y_i={y_i}")
+print(f"start_ind={start_ind} -- stop_ind={stop_ind}")
diff --git a/stable/_downloads/b6d17638dc8505e659bc7a9abe0c7335/plot_split_dataset.py b/stable/_downloads/b6d17638dc8505e659bc7a9abe0c7335/plot_split_dataset.py
deleted file mode 100644
index 7db3b28f..00000000
--- a/stable/_downloads/b6d17638dc8505e659bc7a9abe0c7335/plot_split_dataset.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""
-Split Dataset Example
-=====================
-
-In this example, we show multiple ways of how to split datasets.
-"""
-
-# Authors: Lukas Gemein <l.gemein@gmail.com>
-#
-# License: BSD (3-clause)
-
-from braindecode.datasets import MOABBDataset
-from braindecode.preprocessing import create_windows_from_events
-
-###############################################################################
-# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
-
-###############################################################################
-# ds has a pandas DataFrame with additional description of its internal datasets
-dataset.description
-
-###############################################################################
-# We can split the dataset based on the info in the description, for example
-# based on different runs. The returned dictionary will have string keys
-# corresponding to unique entries in the description DataFrame column
-splits = dataset.split("run")
-print(splits)
-splits["run_4"].description
-
-###############################################################################
-# We can also split the dataset based on a list of integers corresponding to
-# rows in the description. In this case, the returned dictionary will have
-# '0' as the only key
-splits = dataset.split([0, 1, 5])
-print(splits)
-splits["0"].description
-
-###############################################################################
-# If we want multiple splits based on indices, we can also specify a list of
-# list of integers. In this case, the dictionary will have string keys
-# representing the id of the dataset split in the order of the given list of
-# integers
-splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])
-print(splits)
-splits["2"].description
-
-###############################################################################
-# If we want to split based on a list of indices but you want to specify
-# the keys in the output dictionary you can pass a dict as:
-splits = dataset.split(
-    {"train": [0, 1, 5], "valid": [2, 3, 4], "test": [6, 7, 8, 9, 10, 11]}
-)
-print(splits)
-splits["test"].description
-
-###############################################################################
-# Similarly, we can split datasets after creating windows
-windows = create_windows_from_events(
-    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)
-splits = windows.split("run")
-splits
-
-###############################################################################
-splits = windows.split([4, 8])
-splits
-
-###############################################################################
-splits = windows.split([[4, 8], [5, 9, 11]])
-splits
-
-###############################################################################
-splits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))
-splits
diff --git a/stable/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb b/stable/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb
new file mode 100644
index 00000000..51a5b0f2
--- /dev/null
+++ b/stable/_downloads/b9d19304ecd233ea7a79d4365316ea49/plot_load_save_datasets.ipynb
@@ -0,0 +1,169 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Load and save dataset example\n\nIn this example, we show how to load and save braindecode datasets.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nfrom braindecode.datasets import MOABBDataset\nfrom braindecode.preprocessing import preprocess, Preprocessor\nfrom braindecode.datautil import load_concat_dataset\nfrom braindecode.preprocessing import create_windows_from_events"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "First, we load some dataset using MOABB.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = MOABBDataset(\n    dataset_name='BNCI2014001',\n    subject_ids=[1],\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We can apply preprocessing steps to the dataset. It is also possible to skip\nthis step and not apply any preprocessing.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "preprocess(\n    concat_ds=dataset,\n    preprocessors=[Preprocessor(fn='resample', sfreq=10)]\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We save the dataset to a an existing directory. It will create a '.fif' file\nfor every dataset in the concat dataset. Additionally it will create two\nJSON files, the first holding the description of the dataset, the second\nholding the name of the target. If you want to store to the same directory\nseveral times, for example due to trying different preprocessing, you can\nchoose to overwrite the existing files.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "tmpdir = tempfile.mkdtemp()  # write in a temporary directory\ndataset.save(\n    path=tmpdir,\n    overwrite=False,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We load the saved dataset from a directory. Signals can be preloaded in\ncompliance with mne. Optionally, only specific '.fif' files can be loaded\nby specifying their ids. The target name can be changed, if the dataset\nsupports it (TUHAbnormal for example supports 'pathological', 'age', and\n'gender'. If you stored a preprocessed version with target 'pathological'\nit is possible to change the target upon loading).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset_loaded = load_concat_dataset(\n    path=tmpdir,\n    preload=True,\n    ids_to_load=[1, 3],\n    target_name=None,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The serialization utility also supports WindowsDatasets, so we create\ncompute windows next.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows_dataset = create_windows_from_events(\n    concat_ds=dataset_loaded,\n    trial_start_offset_samples=0,\n    trial_stop_offset_samples=0,\n)\n\nwindows_dataset.description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Again, we save the dataset to an existing directory. It will create a\n'-epo.fif' file for every dataset in the concat dataset. Additionally it\nwill create a JSON file holding the description of the dataset. If you\nwant to store to the same directory several times, for example due to\ntrying different windowing parameters, you can choose to overwrite the\nexisting files.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows_dataset.save(\n    path=tmpdir,\n    overwrite=True,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Load the saved dataset from a directory. Signals can be preloaded in\ncompliance with mne. Optionally, only specific '-epo.fif' files can be\nloaded by specifying their ids.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows_dataset_loaded = load_concat_dataset(\n    path=tmpdir,\n    preload=False,\n    ids_to_load=[0],\n    target_name=None,\n)\n\nwindows_dataset_loaded.description"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py b/stable/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py
new file mode 100644
index 00000000..7331f09d
--- /dev/null
+++ b/stable/_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py
@@ -0,0 +1,525 @@
+"""
+Self-supervised learning on EEG with relative positioning
+=========================================================
+
+This example shows how to train a neural network with self-supervision on sleep
+EEG data. We follow the relative positioning approach of [1]_ on the openly
+accessible Sleep Physionet dataset [2]_ [3]_.
+
+.. topic:: Self-supervised learning
+
+    Self-supervised learning (SSL) is a learning paradigm that leverages
+    unlabelled data to train neural networks. First, neural networks are
+    trained on a "pretext task" which uses unlabelled data only. The pretext
+    task is designed based on a prior understanding of the data under study
+    (e.g., EEG has an underlying autocorrelation structure) and such that the
+    processing required to perform well on this pretext task is related to the
+    processing required to perform well on another task of interest.
+    Once trained, these neural networks can be reused as feature extractors or
+    weight initialization in a "downstream task", which is the task that we are
+    actually interested in (e.g., sleep staging). The pretext task step can
+    help reduce the quantity of labelled data needed to perform well on the
+    downstream task and/or improve downstream performance as compared to a
+    strictly supervised approach [1]_.
+
+Here, we use relative positioning (RP) as our pretext task, and perform sleep
+staging as our downstream task. RP is a simple SSL task, in which a neural
+network is trained to predict whether two randomly sampled EEG windows are
+close or far apart in time. This method was shown to yield physiologically- and
+clinically-relevant features and to boost classification performance in
+low-labels data regimes [1]_.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Hubert Banville <hubert.jbanville@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+random_state = 87
+n_jobs = 1
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading the raw recordings
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# First, we load a few recordings from the Sleep Physionet dataset. Running
+# this example with more recordings should yield better representations and
+# downstream classification performance.
+
+from braindecode.datasets.sleep_physionet import SleepPhysionet
+
+dataset = SleepPhysionet(
+    subject_ids=[0, 1, 2], recording_ids=[1], crop_wake_mins=30)
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# Next, we preprocess the raw data. We convert the data to microvolts and apply
+# a lowpass filter. Since the Sleep Physionet data is already sampled at 100 Hz
+# we don't need to apply resampling.
+
+from braindecode.preprocessing.preprocess import preprocess, Preprocessor
+from numpy import multiply
+
+high_cut_hz = 30
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz, n_jobs=n_jobs)
+]
+
+# Transform the data
+preprocess(dataset, preprocessors)
+
+######################################################################
+# Extracting windows
+# ~~~~~~~~~~~~~~~~~~
+#
+# We extract 30-s windows to be used in both the pretext and downstream tasks.
+# As RP (and SSL in general) don't require labelled data, the pretext task
+# could be performed using unlabelled windows extracted with
+# :func:`braindecode.datautil.windower.create_fixed_length_window`.
+# Here however, purely for convenience, we directly extract labelled windows so
+# that we can reuse them in the sleep staging downstream task later.
+
+from braindecode.preprocessing.windowers import create_windows_from_events
+
+window_size_s = 30
+sfreq = 100
+window_size_samples = window_size_s * sfreq
+
+mapping = {  # We merge stages 3 and 4 following AASM standards.
+    'Sleep stage W': 0,
+    'Sleep stage 1': 1,
+    'Sleep stage 2': 2,
+    'Sleep stage 3': 3,
+    'Sleep stage 4': 3,
+    'Sleep stage R': 4
+}
+
+windows_dataset = create_windows_from_events(
+    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0,
+    window_size_samples=window_size_samples,
+    window_stride_samples=window_size_samples, preload=True, mapping=mapping)
+
+######################################################################
+# Preprocessing windows
+# ~~~~~~~~~~~~~~~~~~~~~
+#
+# We also preprocess the windows by applying channel-wise z-score normalization.
+
+from sklearn.preprocessing import scale as standard_scale
+
+preprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])
+
+######################################################################
+# Splitting dataset into train, valid and test sets
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We randomly split the recordings by subject into train, validation and
+# testing sets. We further define a new Dataset class which can receive a pair
+# of indices and return the corresponding windows. This will be needed when
+# training and evaluating on the pretext task.
+
+import numpy as np
+from sklearn.model_selection import train_test_split
+from braindecode.datasets import BaseConcatDataset
+
+subjects = np.unique(windows_dataset.description['subject'])
+subj_train, subj_test = train_test_split(
+    subjects, test_size=0.4, random_state=random_state)
+subj_valid, subj_test = train_test_split(
+    subj_test, test_size=0.5, random_state=random_state)
+
+
+class RelativePositioningDataset(BaseConcatDataset):
+    """BaseConcatDataset with __getitem__ that expects 2 indices and a target.
+    """
+
+    def __init__(self, list_of_ds):
+        super().__init__(list_of_ds)
+        self.return_pair = True
+
+    def __getitem__(self, index):
+        if self.return_pair:
+            ind1, ind2, y = index
+            return (super().__getitem__(ind1)[0],
+                    super().__getitem__(ind2)[0]), y
+        else:
+            return super().__getitem__(index)
+
+    @property
+    def return_pair(self):
+        return self._return_pair
+
+    @return_pair.setter
+    def return_pair(self, value):
+        self._return_pair = value
+
+
+split_ids = {'train': subj_train, 'valid': subj_valid, 'test': subj_test}
+splitted = dict()
+for name, values in split_ids.items():
+    splitted[name] = RelativePositioningDataset(
+        [ds for ds in windows_dataset.datasets
+         if ds.description['subject'] in values])
+
+######################################################################
+# Creating samplers
+# ~~~~~~~~~~~~~~~~~
+#
+# Next, we need to create samplers. These samplers will be used to randomly
+# sample pairs of examples to train and validate our model with
+# self-supervision.
+#
+# The RP samplers have two main hyperparameters. `tau_pos` and `tau_neg`
+# control the size of the "positive" and "negative" contexts, respectively.
+# Pairs of windows that are separated by less than `tau_pos` samples will be
+# given a label of `1`, while pairs of windows that are separated by more than
+# `tau_neg` samples will be given a label of `0`. Here, we use the same values
+# as in [1]_, i.e., `tau_pos`= 1 min and `tau_neg`= 15 mins.
+#
+# The samplers also control the number of pairs to be sampled (defined with
+# `n_examples`). This number can be large to help regularize the pretext task
+# training, for instance 2,000 pairs per recording as in [1]_. Here, we use a
+# lower number of 250 pairs per recording to reduce training time.
+
+from braindecode.samplers import RelativePositioningSampler
+
+tau_pos, tau_neg = int(sfreq * 60), int(sfreq * 15 * 60)
+n_examples_train = 250 * len(splitted['train'].datasets)
+n_examples_valid = 250 * len(splitted['valid'].datasets)
+n_examples_test = 250 * len(splitted['test'].datasets)
+
+train_sampler = RelativePositioningSampler(
+    splitted['train'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
+    n_examples=n_examples_train, same_rec_neg=True, random_state=random_state)
+valid_sampler = RelativePositioningSampler(
+    splitted['valid'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
+    n_examples=n_examples_valid, same_rec_neg=True,
+    random_state=random_state).presample()
+test_sampler = RelativePositioningSampler(
+    splitted['test'].get_metadata(), tau_pos=tau_pos, tau_neg=tau_neg,
+    n_examples=n_examples_test, same_rec_neg=True,
+    random_state=random_state).presample()
+
+######################################################################
+# Creating the model
+# ------------------
+#
+# We can now create the deep learning model. In this tutorial, we use a
+# modified version of the sleep staging architecture introduced in [4]_ -
+# a four-layer convolutional neural network - as our embedder.
+# We change the dimensionality of the last layer to obtain a 100-dimension
+# embedding, use 16 convolutional channels instead of 8, and add batch
+# normalization after both temporal convolution layers.
+#
+# We further wrap the model into a siamese architecture using the
+# # :class:`ContrastiveNet` class defined below. This allows us to train the
+# feature extractor end-to-end.
+
+import torch
+from torch import nn
+from braindecode.util import set_random_seeds
+from braindecode.models import SleepStagerChambon2018
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+if device == 'cuda':
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+set_random_seeds(seed=random_state, cuda=device == 'cuda')
+
+# Extract number of channels and time steps from dataset
+n_channels, input_size_samples = windows_dataset[0][0].shape
+emb_size = 100
+classes = list(range(5))
+
+emb = SleepStagerChambon2018(
+    n_channels,
+    sfreq,
+    n_outputs=emb_size,
+    n_conv_chs=16,
+    n_times=input_size_samples,
+    dropout=0,
+    apply_batch_norm=True,
+)
+
+
+class ContrastiveNet(nn.Module):
+    """Contrastive module with linear layer on top of siamese embedder.
+
+    Parameters
+    ----------
+    emb : nn.Module
+        Embedder architecture.
+    emb_size : int
+        Output size of the embedder.
+    dropout : float
+        Dropout rate applied to the linear layer of the contrastive module.
+    """
+
+    def __init__(self, emb, emb_size, dropout=0.5):
+        super().__init__()
+        self.emb = emb
+        self.clf = nn.Sequential(
+            nn.Dropout(dropout),
+            nn.Linear(emb_size, 1)
+        )
+
+    def forward(self, x):
+        x1, x2 = x
+        z1, z2 = self.emb(x1), self.emb(x2)
+        return self.clf(torch.abs(z1 - z2)).flatten()
+
+
+model = ContrastiveNet(emb, emb_size).to(device)
+
+######################################################################
+# Training
+# ---------
+#
+# We can now train our network on the pretext task. We use similar
+# hyperparameters as in [1]_, but reduce the number of epochs and
+# increase the learning rate to account for the smaller setting of
+# this example.
+
+import os
+
+from skorch.helper import predefined_split
+from skorch.callbacks import Checkpoint, EarlyStopping, EpochScoring
+from braindecode import EEGClassifier
+
+lr = 5e-3
+batch_size = 128  # 512 if data large enough
+n_epochs = 25
+num_workers = 0 if n_jobs <= 1 else n_jobs
+
+cp = Checkpoint(dirname='', f_criterion=None, f_optimizer=None, f_history=None)
+early_stopping = EarlyStopping(patience=10)
+train_acc = EpochScoring(
+    scoring='accuracy', on_train=True, name='train_acc', lower_is_better=False)
+
+callbacks = [
+    ('cp', cp),
+    ('patience', early_stopping),
+    ('train_acc', train_acc),
+]
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.BCEWithLogitsLoss,
+    optimizer=torch.optim.Adam,
+    max_epochs=n_epochs,
+    iterator_train__shuffle=False,
+    iterator_train__sampler=train_sampler,
+    iterator_valid__sampler=valid_sampler,
+    iterator_train__num_workers=num_workers,
+    iterator_valid__num_workers=num_workers,
+    train_split=predefined_split(splitted['valid']),
+    optimizer__lr=lr,
+    batch_size=batch_size,
+    callbacks=callbacks,
+    device=device,
+    classes=classes,
+)
+# Model training for a specified number of epochs. `y` is None as it is already
+# supplied in the dataset.
+clf.fit(splitted['train'], y=None)
+clf.load_params(checkpoint=cp)  # Load the model with the lowest valid_loss
+
+os.remove('./params.pt')  # Delete parameters file
+
+######################################################################
+# Visualizing the results
+# -----------------------
+#
+# Inspecting pretext task performance
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We plot the loss and pretext task performance for the training and validation
+# sets.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Extract loss and balanced accuracy values for plotting from history object
+df = pd.DataFrame(clf.history.to_list())
+
+df['train_acc'] *= 100
+df['valid_acc'] *= 100
+
+ys1 = ['train_loss', 'valid_loss']
+ys2 = ['train_acc', 'valid_acc']
+styles = ['-', ':']
+markers = ['.', '.']
+
+fig, ax1 = plt.subplots(figsize=(8, 3))
+ax2 = ax1.twinx()
+for y1, y2, style, marker in zip(ys1, ys2, styles, markers):
+    ax1.plot(df['epoch'], df[y1], ls=style, marker=marker, ms=7,
+             c='tab:blue', label=y1)
+    ax2.plot(df['epoch'], df[y2], ls=style, marker=marker, ms=7,
+             c='tab:orange', label=y2)
+
+ax1.tick_params(axis='y', labelcolor='tab:blue')
+ax1.set_ylabel('Loss', color='tab:blue')
+ax2.tick_params(axis='y', labelcolor='tab:orange')
+ax2.set_ylabel('Accuracy [%]', color='tab:orange')
+ax1.set_xlabel('Epoch')
+
+lines1, labels1 = ax1.get_legend_handles_labels()
+lines2, labels2 = ax2.get_legend_handles_labels()
+ax2.legend(lines1 + lines2, labels1 + labels2)
+
+plt.tight_layout()
+
+######################################################################
+# We also display the confusion matrix and classification report for the
+# pretext task:
+
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+
+# Switch to the test sampler
+clf.iterator_valid__sampler = test_sampler
+y_pred = clf.forward(splitted['test'], training=False) > 0
+y_true = [y for _, _, y in test_sampler]
+
+print(confusion_matrix(y_true, y_pred))
+print(classification_report(y_true, y_pred))
+
+######################################################################
+# Using the learned representation for sleep staging
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We can now use the trained convolutional neural network as a feature
+# extractor. We perform sleep stage classification from the learned feature
+# representation using a linear logistic regression classifier.
+
+from torch.utils.data import DataLoader
+from sklearn.metrics import balanced_accuracy_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
+
+# Extract features with the trained embedder
+data = dict()
+for name, split in splitted.items():
+    split.return_pair = False  # Return single windows
+    loader = DataLoader(split, batch_size=batch_size, num_workers=num_workers)
+    with torch.no_grad():
+        feats = [emb(batch_x.to(device)).cpu().numpy()
+                 for batch_x, _, _ in loader]
+    data[name] = (np.concatenate(feats), split.get_metadata()['target'].values)
+
+# Initialize the logistic regression model
+log_reg = LogisticRegression(
+    penalty='l2', C=1.0, class_weight='balanced', solver='lbfgs',
+    multi_class='multinomial', random_state=random_state)
+clf_pipe = make_pipeline(StandardScaler(), log_reg)
+
+# Fit and score the logistic regression
+clf_pipe.fit(*data['train'])
+train_y_pred = clf_pipe.predict(data['train'][0])
+valid_y_pred = clf_pipe.predict(data['valid'][0])
+test_y_pred = clf_pipe.predict(data['test'][0])
+
+train_bal_acc = balanced_accuracy_score(data['train'][1], train_y_pred)
+valid_bal_acc = balanced_accuracy_score(data['valid'][1], valid_y_pred)
+test_bal_acc = balanced_accuracy_score(data['test'][1], test_y_pred)
+
+print('Sleep staging performance with logistic regression:')
+print(f'Train bal acc: {train_bal_acc:0.4f}')
+print(f'Valid bal acc: {valid_bal_acc:0.4f}')
+print(f'Test bal acc: {test_bal_acc:0.4f}')
+
+print('Results on test set:')
+print(confusion_matrix(data['test'][1], test_y_pred))
+print(classification_report(data['test'][1], test_y_pred))
+
+######################################################################
+# The balanced accuracy is much higher than chance-level (i.e., 20% for our
+# 5-class classification problem). Finally, we perform a quick 2D visualization
+# of the feature space using a PCA:
+
+from sklearn.decomposition import PCA
+from matplotlib import cm
+
+X = np.concatenate([v[0] for k, v in data.items()])
+y = np.concatenate([v[1] for k, v in data.items()])
+
+pca = PCA(n_components=2)
+# tsne = TSNE(n_components=2)
+components = pca.fit_transform(X)
+
+fig, ax = plt.subplots()
+colors = cm.get_cmap('viridis', 5)(range(5))
+for i, stage in enumerate(['W', 'N1', 'N2', 'N3', 'R']):
+    mask = y == i
+    ax.scatter(components[mask, 0], components[mask, 1], s=10, alpha=0.7,
+               color=colors[i], label=stage)
+ax.legend()
+
+######################################################################
+# We see that there is sleep stage-related structure in the embedding. A
+# nonlinear projection method (e.g., tSNE, UMAP) might yield more insightful
+# visualizations. Using a similar approach, the embedding space could also be
+# explored with respect to subject-level features, e.g., age and sex.
+#
+# Conclusion
+# ----------
+#
+# In this example, we used self-supervised learning (SSL) as a way to learn
+# representations from unlabelled raw EEG data. Specifically, we used the
+# relative positioning (RP) pretext task to train a feature extractor on a
+# subset of the Sleep Physionet dataset. We then reused these features in a
+# downstream sleep staging task. We achieved reasonable downstream performance
+# and further showed with a 2D projection that the learned embedding space
+# contained sleep-related structure.
+#
+# Many avenues could be taken to improve on these results. For instance, using
+# the entire Sleep Physionet dataset or training on larger datasets should help
+# the feature extractor learn better representations during the pretext task.
+# Other SSL tasks such as those described in [1]_ could further help discover
+# more powerful features.
+#
+#
+# References
+# ----------
+#
+# .. [1] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., & Gramfort, A.
+#       (2020). Uncovering the structure of clinical EEG signals with
+#       self-supervised learning. arXiv preprint arXiv:2007.16104.
+#
+# .. [2] Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A., & Oberye, J. J.
+#        (2000). Analysis of a sleep-dependent neuronal feedback loop: the
+#        slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical
+#        Engineering, 47(9), 1185-1194.
+#
+# .. [3] Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov,
+#        P. C., Mark, R. G., ... & Stanley, H. E. (2000). PhysioBank,
+#        PhysioToolkit, and PhysioNet: components of a new research resource for
+#        complex physiologic signals. circulation, 101(23), e215-e220.
+#
+# .. [4] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
+#       Classification Using Multivariate and Multimodal Time Series.
+#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+#       (758-769)
+#
diff --git a/stable/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py b/stable/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py
new file mode 100644
index 00000000..a1fa4a57
--- /dev/null
+++ b/stable/_downloads/bddf8708329187cc5d6a3688028e9825/plot_tuh_discrete_multitarget.py
@@ -0,0 +1,127 @@
+"""
+Multiple discrete targets with the TUH EEG Corpus
+=================================================
+
+Welcome to this tutorial where we demonstrate how to work with multiple discrete
+ targets for each recording in the TUH EEG Corpus. We'll guide you through the
+ process step by step.
+
+"""
+
+# Author: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+import mne
+from torch.utils.data import DataLoader
+
+from braindecode.datasets import TUH
+from braindecode.preprocessing import create_fixed_length_windows
+
+# Setting Logging Level
+# ----------------------
+#
+# We'll set the logging level to 'ERROR' to avoid excessive messages when
+# extracting windows:
+
+mne.set_log_level('ERROR')  # avoid messages every time a window is extracted
+
+
+###############################################################################
+# If you want to try this code with the actual data, please delete the next
+# section. We are required to mock some dataset functionality, since the data
+# is not available at creation time of this example.
+from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811
+
+
+###############################################################################
+# Creating Temple University Hospital (TUH) EEG Corpus Dataset
+# ------------------------------------------------------------
+#
+# We start by creating a TUH dataset. Instead of just a `str, we give it
+# multiple strings as target names. Each of the strings has to exist as a
+# column in the description DataFrame.
+
+TUH_PATH = 'please insert actual path to data here'
+tuh = TUH(
+    path=TUH_PATH,
+    recording_ids=None,
+    target_name=('age', 'gender'),  # use both age and gender as decoding target
+    preload=False,
+    add_physician_reports=False,
+)
+print(tuh.description)
+
+###############################################################################
+# Exploring Data
+# --------------
+#
+# Iterating through the dataset gives `x` as an ndarray with shape
+# `(n_channels x 1)` and `y` as a list containing `[age of the subject, gender
+# of the subject]`.
+# Let's look at the last example as it has more interesting age/gender labels
+# (compare to the last row of the dataframe above).
+x, y = tuh[-1]
+
+print(f'{x=}\n{y=}')
+
+
+###############################################################################
+# Creating Windows
+# ----------------
+#
+# We will skip preprocessing steps for now, since it is not the aim of this
+# example. Instead, we will directly create compute windows. We specify a
+# mapping from genders 'M' and 'F' to integers, since this is required for
+# decoding.
+
+tuh_windows = create_fixed_length_windows(
+    tuh,
+    start_offset_samples=0,
+    stop_offset_samples=None,
+    window_size_samples=1000,
+    window_stride_samples=1000,
+    drop_last_window=False,
+    mapping={'M': 0, 'F': 1},  # map non-digit targets
+)
+# store the number of windows required for loading later on
+tuh_windows.set_description({
+    "n_windows": [len(d) for d in tuh_windows.datasets]})
+
+
+###############################################################################
+# Exploring Windows
+# -----------------
+#
+# Iterating through the dataset gives `x` as an ndarray with shape
+# `(n_channels x 1000)`, `y` as `[age, gender]`, and `ind`.
+# Let's look at the last example again.
+x, y, ind = tuh_windows[-1]
+print(f'{x=}\n{y=}\n{ind=}')
+
+
+###############################################################################
+# DataLoader for Model Training
+# -----------------------------
+#
+# We give the dataset to a pytorch DataLoader, such that it can be used for
+# model training.
+dl = DataLoader(
+    dataset=tuh_windows,
+    batch_size=4,
+)
+
+
+###############################################################################
+# Exploring DataLoader
+# --------------------
+#
+# When iterating through the DataLoader, we get `batch_X` as a tensor with shape
+# `(4 x n_channels x 1000)`, `batch_y` as `[tensor([4 x age of subject]),
+# tensor([4 x gender of subject])]`, and `batch_ind`. To view the last example,
+# simply iterate through the DataLoader:
+
+for batch_X, batch_y, batch_ind in dl:
+    pass
+
+print(f'{batch_X=}\n{batch_y=}\n{batch_ind=}')
diff --git a/stable/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py b/stable/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py
new file mode 100644
index 00000000..2aaf1b05
--- /dev/null
+++ b/stable/_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py
@@ -0,0 +1,324 @@
+"""Benchmarking eager and lazy loading
+======================================
+
+In this example, we compare the execution time and memory requirements of 1)
+eager loading, i.e., preloading the entire data into memory and 2) lazy loading,
+i.e., only loading examples from disk when they are required. We also include
+some other experiment parameters in the comparison for the sake of completeness
+(e.g., `num_workers`, `cuda`, `batch_size`, etc.).
+
+While eager loading might be required for some preprocessing steps that require
+continuous data (e.g., temporal filtering, resampling), it also allows
+fast access to the data during training. However, this might come at the expense
+of large memory usage, and can ultimately become impossible if the dataset does
+not fit into memory (e.g., the TUH EEG dataset's >1,5 TB of recordings will
+not fit in the memory of most machines).
+
+Lazy loading avoids this potential memory issue by loading examples from disk
+when they are required. This means large datasets can be used for training,
+however this introduces some file-reading overhead every time an example must
+be extracted. Some preprocessing steps that require continuous data also have to
+be implemented differently to accommodate the nature of windowed data. Overall
+though, we can reduce the impact of lazy loading by using the `num_workers`
+parameter of pytorch's `Dataloader` class, which dispatches the data loading to
+multiple processes.
+
+To enable lazy loading in braindecode, data files must be saved in an
+MNE-compatible format (e.g., 'fif', 'edf', etc.), and the `Dataset` object must
+have been instantiated with parameter `preload=False`.
+"""
+
+# Authors: Hubert Banville <hubert.jbanville@gmail.com>
+#
+# License: BSD (3-clause)
+
+from itertools import product
+import time
+
+import torch
+from torch import nn, optim
+from torch.utils.data import DataLoader
+
+import mne
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+from braindecode.datasets import TUHAbnormal
+from braindecode.preprocessing import create_fixed_length_windows
+from braindecode.models import ShallowFBCSPNet, Deep4Net
+
+
+mne.set_log_level('WARNING')  # avoid messages every time a window is extracted
+
+###############################################################################
+# We start by setting two pytorch internal parameters that can affect the
+# comparison::
+N_JOBS = 8
+torch.backends.cudnn.benchmark = True  # Enables automatic algorithm optimizations
+torch.set_num_threads(N_JOBS)  # Sets the available number of threads
+
+
+###############################################################################
+# Next, we define a few functions to automate the benchmarking.
+# For the purpose of this example, we load some recordings from the TUH Abnormal
+# corpus, extract sliding windows, and bundle them in a braindecode Dataset.
+# We then train a neural network for a few epochs.
+#
+# Each one of these steps will be timed, so we can report the total time taken
+# to prepare the data and train the model.
+
+def load_example_data(preload, window_len_s, n_recordings=10):
+    """Create windowed dataset from subjects of the TUH Abnormal dataset.
+
+    Parameters
+    ----------
+    preload: bool
+        If True, use eager loading, otherwise use lazy loading.
+    window_len_s: int
+        Window length in seconds.
+    n_recordings: list of int
+        Number of recordings to load.
+
+    Returns
+    -------
+    windows_ds: BaseConcatDataset
+        Windowed data.
+
+    .. warning::
+        The recordings from the TUH Abnormal corpus do not all share the same
+        sampling rate. The following assumes that the files have already been
+        resampled to a common sampling rate.
+    """
+
+    recording_ids = list(range(n_recordings))
+
+    ds = TUHAbnormal(
+        TUH_PATH, recording_ids=recording_ids,
+        target_name='pathological',
+        preload=preload)
+
+    fs = ds.datasets[0].raw.info['sfreq']
+    window_len_samples = int(fs * window_len_s)
+    window_stride_samples = int(fs * 4)
+    # window_stride_samples = int(fs * window_len_s)
+    windows_ds = create_fixed_length_windows(
+        ds, start_offset_samples=0, stop_offset_samples=None,
+        window_size_samples=window_len_samples,
+        window_stride_samples=window_stride_samples, drop_last_window=True,
+        preload=preload)
+
+    # Drop bad epochs
+    # XXX: This could be parallelized.
+    # XXX: Also, this could be implemented in the Dataset object itself.
+    for ds in windows_ds.datasets:
+        ds.windows.drop_bad()
+        assert ds.windows.preload == preload
+
+    return windows_ds
+
+
+def create_example_model(n_channels, n_classes, window_len_samples,
+                         kind='shallow', cuda=False):
+    """Create model, loss and optimizer.
+
+    Parameters
+    ----------
+    n_channels : int
+        Number of channels in the input
+    n_times : int
+        Window length in the input
+    n_classes : int
+        Number of classes in the output
+    kind : str
+        'shallow' or 'deep'
+    cuda : bool
+        If True, move the model to a CUDA device.
+
+    Returns
+    -------
+    model : torch.nn.Module
+        Model to train.
+    loss :
+        Loss function
+    optimizer :
+        Optimizer
+    """
+    if kind == 'shallow':
+        model = ShallowFBCSPNet(
+            n_channels, n_classes, input_window_samples=window_len_samples,
+            n_filters_time=40, filter_time_length=25, n_filters_spat=40,
+            pool_time_length=75, pool_time_stride=15, final_conv_length='auto',
+            split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1,
+            drop_prob=0.5)
+    elif kind == 'deep':
+        model = Deep4Net(
+            n_channels, n_classes, input_window_samples=window_len_samples,
+            final_conv_length='auto', n_filters_time=25, n_filters_spat=25,
+            filter_time_length=10, pool_time_length=3, pool_time_stride=3,
+            n_filters_2=50, filter_length_2=10, n_filters_3=100,
+            filter_length_3=10, n_filters_4=200, filter_length_4=10,
+            first_pool_mode="max", later_pool_mode="max", drop_prob=0.5,
+            double_time_convs=False, split_first_layer=True, batch_norm=True,
+            batch_norm_alpha=0.1, stride_before_pool=False)
+    else:
+        raise ValueError
+
+    if cuda:
+        model.cuda()
+
+    optimizer = optim.Adam(model.parameters())
+    loss = nn.NLLLoss()
+
+    return model, loss, optimizer
+
+
+def run_training(model, dataloader, loss, optimizer, n_epochs=1, cuda=False):
+    """Run training loop.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to train.
+    dataloader : torch.utils.data.Dataloader
+        Data loader which will serve examples to the model during training.
+    loss :
+        Loss function.
+    optimizer :
+        Optimizer.
+    n_epochs : int
+        Number of epochs to train the model for.
+    cuda : bool
+        If True, move X and y to CUDA device.
+
+    Returns
+    -------
+    model : torch.nn.Module
+        Trained model.
+    """
+    for i in range(n_epochs):
+        loss_vals = list()
+        for X, y, _ in dataloader:
+            model.train()
+            model.zero_grad()
+
+            y = y.long()
+            if cuda:
+                X, y = X.cuda(), y.cuda()
+
+            loss_val = loss(model(X), y)
+            loss_vals.append(loss_val.item())
+
+            loss_val.backward()
+            optimizer.step()
+
+        print(f'Epoch {i + 1} - mean training loss: {np.mean(loss_vals)}')
+
+    return model
+
+
+###############################################################################
+# Next, we define the different hyperparameters that we want to compare:
+
+PRELOAD = [True, False]  # True -> eager loading; False -> lazy loading
+N_RECORDINGS = [10]  # Number of recordings to load from the TUH Abnormal corpus
+WINDOW_LEN_S = [2, 4, 15]  # Window length, in seconds
+N_EPOCHS = [2]  # Number of epochs to train the model for
+BATCH_SIZE = [64, 256]  # Training minibatch size
+MODEL = ['shallow', 'deep']
+
+NUM_WORKERS = [8, 0]  # number of processes used by pytorch's Dataloader
+PIN_MEMORY = [False]  # whether to use pinned memory
+CUDA = [True, False] if torch.cuda.is_available() else [False]  # whether to use a CUDA device
+
+N_REPETITIONS = 3  # Number of times to repeat the experiment (to get better time estimates)
+
+###############################################################################
+# The following path needs to be changed to your local folder containing the
+# TUH Abnormal corpus:
+TUH_PATH = ('/storage/store/data/tuh_eeg/www.isip.piconepress.com/projects/'
+            'tuh_eeg/downloads/tuh_eeg_abnormal/v2.0.0/edf/')
+
+###############################################################################
+# We can finally cycle through all the different combinations of the parameters
+# we set above to evaluate their execution time:
+
+all_results = list()
+for (i, preload, n_recordings, win_len_s, n_epochs, batch_size, model_kind,
+        num_workers, pin_memory, cuda) in product(
+            range(N_REPETITIONS), PRELOAD, N_RECORDINGS, WINDOW_LEN_S, N_EPOCHS,
+            BATCH_SIZE, MODEL, NUM_WORKERS, PIN_MEMORY, CUDA):
+
+    results = {
+        'repetition': i,
+        'preload': preload,
+        'n_recordings': n_recordings,
+        'win_len_s': win_len_s,
+        'n_epochs': n_epochs,
+        'batch_size': batch_size,
+        'model_kind': model_kind,
+        'num_workers': num_workers,
+        'pin_memory': pin_memory,
+        'cuda': cuda
+    }
+    print(f'\nRepetition {i + 1}/{N_REPETITIONS}:\n{results}')
+
+    # Load the dataset
+    data_loading_start = time.time()
+    dataset = load_example_data(preload, win_len_s, n_recordings=n_recordings)
+    data_loading_end = time.time()
+
+    # Create the data loader
+    training_setup_start = time.time()
+    dataloader = DataLoader(
+        dataset, batch_size=batch_size, shuffle=False, pin_memory=pin_memory,
+        num_workers=num_workers, worker_init_fn=None)
+
+    # Instantiate model and optimizer
+    n_channels = len(dataset.datasets[0].windows.ch_names)
+    n_times = len(dataset.datasets[0].windows.times)
+    n_classes = 2
+    model, loss, optimizer = create_example_model(
+        n_channels, n_classes, n_times, kind=model_kind, cuda=cuda)
+    training_setup_end = time.time()
+
+    # Start training loop
+    model_training_start = time.time()
+    trained_model = run_training(
+        model, dataloader, loss, optimizer, n_epochs=n_epochs, cuda=cuda)
+    model_training_end = time.time()
+
+    del dataset, model, loss, optimizer, trained_model
+
+    # Record timing results
+    results['data_preparation'] = data_loading_end - data_loading_start
+    results['training_setup'] = training_setup_end - training_setup_start
+    results['model_training'] = model_training_end - model_training_start
+    all_results.append(results)
+
+###############################################################################
+# The results are formatted into a pandas DataFrame and saved locally as a CSV
+# file.
+
+results_df = pd.DataFrame(all_results)
+fname = 'lazy_vs_eager_loading_results.csv'
+results_df.to_csv(fname)
+print(f'Results saved under {fname}.')
+
+###############################################################################
+# We can finally summarize this information into the following plot:
+
+sns.catplot(
+    data=results_df, row='cuda', x='model_kind', y='model_training',
+    hue='num_workers', col='preload', kind='strip')
+
+###############################################################################
+# .. warning::
+#   The results of this comparison will change depending on the hyperparameters
+#   that were set above, and on the actual hardware that is being used.
+
+###############################################################################
+# Generally speaking, we expect lazy loading to be slower than eager loading
+# during model training, but to potentially be pretty competitive if multiple
+# workers were enabled (i.e.., `num_workers > 0`). Training on a CUDA device
+# should also yield substantial speedups.
diff --git a/stable/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py b/stable/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py
new file mode 100644
index 00000000..e33346f3
--- /dev/null
+++ b/stable/_downloads/c494db8fb7ce5921127c670340744634/plot_bcic_iv_4_ecog_cropped.py
@@ -0,0 +1,428 @@
+"""
+Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset
+==========================================================
+
+This tutorial shows you how to train and test deep learning models with
+Braindecode on ECoG BCI IV competition dataset 4 using cropped mode. For this
+dataset we will predict 5 regression targets corresponding to flexion of each finger.
+The targets were recorded as a time series (each 25 Hz), so this tutorial is
+an example of time series target prediction.
+"""
+
+# Authors: Maciej Sliwowski <maciek.sliwowski@gmail.com>
+#          Mohammed Fattouh <mo.fattouh@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+######################################################################
+# Loading and preparing the dataset
+# ---------------------------------
+#
+# Loading
+# ~~~~~~~
+#
+# First, we load the data. In this tutorial, we use the functionality of braindecode
+# to load `BCI IV competition dataset 4 <http://www.bbci.de/competition/iv/#dataset4>`__.
+# The dataset is available as a part of ECoG library:
+# https://searchworks.stanford.edu/view/zk881ps0522
+#
+# The dataset contains ECoG signal and time series of 5 targets corresponding
+# to each finger flexion. This is different than standard decoding setup for EEG with
+# multiple trials and usually one target per trial. Here, fingers flexions change in time
+# and are recorded with sampling frequency equals to 25 Hz.
+#
+# If this dataset is used please cite [1].
+#
+# [1] Miller, Kai J. "A library of human electrocorticographic data and analyses.
+# "Nature human behaviour 3, no. 11 (2019): 1225-1235. https://doi.org/10.1038/s41562-019-0678-3
+import copy
+
+import numpy as np
+import sklearn
+from mne import set_log_level
+
+from braindecode.datasets import BCICompetitionIVDataset4
+
+subject_id = 1
+dataset = BCICompetitionIVDataset4(subject_ids=[subject_id])
+
+######################################################################
+# Split dataset into train and test
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We can easily split the dataset using additional info stored in the
+# description attribute, in this case ``session`` column. We select `train` dataset
+# for training and validation and `test` for final evaluation.
+dataset = dataset.split('session')
+train_set = dataset['train']
+test_set = dataset['test']
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# Now we apply preprocessing like bandpass filtering to our dataset. You
+# can either apply functions provided by
+# `mne.Raw <https://mne.tools/stable/generated/mne.io.Raw.html>`__ or
+# `mne.Epochs <https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs>`__
+# or apply your own functions, either to the MNE object or the underlying
+# numpy array.
+#
+# .. note::
+#    Preprocessing steps are taken from a standard EEG processing pipeline.
+#    The only change is the cutoff frequency of the filter. For a proper ECoG
+#    decoding other preprocessing steps may be needed.
+#
+# .. note::
+#    These prepocessings are now directly applied to the loaded
+#    data, and not on-the-fly applied as transformations in
+#    PyTorch-libraries like
+#    `torchvision <https://pytorch.org/docs/stable/torchvision/index.html>`__.
+#
+
+
+from braindecode.preprocessing import (Preprocessor,
+                                       exponential_moving_standardize,
+                                       preprocess)
+
+low_cut_hz = 1.  # low cut frequency for filtering
+high_cut_hz = 200.  # high cut frequency for filtering, for ECoG higher than for EEG
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+
+######################################################################
+# We select only first 30 seconds from the training dataset to limit time and memory
+# to run this example. We split training dataset into train and validation (only 6 seconds).
+# To obtain full results whole datasets should be used.
+valid_set = preprocess(copy.deepcopy(train_set),
+                       [Preprocessor('crop', tmin=24, tmax=30)], n_jobs=-1)
+preprocess(train_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)
+preprocess(test_set, [Preprocessor('crop', tmin=0, tmax=24)], n_jobs=-1)
+
+
+######################################################################
+# In time series targets setup, targets variables are stored in mne.Raw object as channels
+# of type `misc`. Thus those channels have to be selected for further processing. However,
+# many mne functions ignore `misc` channels and perform operations only on data channels
+# (see https://mne.tools/stable/glossary.html#term-data-channels).
+preprocessors = [
+    # TODO: ensure that misc is not removed
+    Preprocessor('pick_types', ecog=True, misc=True),
+    Preprocessor(lambda x: x / 1e6, picks='ecog'),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size, picks='ecog')
+]
+# Transform the data
+preprocess(train_set, preprocessors)
+preprocess(valid_set, preprocessors)
+preprocess(test_set, preprocessors)
+
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = train_set.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in train_set.datasets])
+# Extract target sampling frequency
+target_sfreq = train_set.datasets[0].raw.info['temp']['target_sfreq']
+
+
+######################################################################
+# Create model
+# ------------
+#
+# In contrast to trialwise decoding, we first have to create the model
+# before we can cut the dataset into windows. This is because we need to
+# know the receptive field of the network to know how large the window
+# stride should be.
+#
+# We first choose the compute/input window size that will be fed to the
+# network during training This has to be larger than the networks
+# receptive field size and can otherwise be chosen for computational
+# efficiency (see explanations in the beginning of this tutorial). Here we
+# choose 1000 samples, which is 1 second for the 1000 Hz sampling rate.
+#
+
+input_window_samples = 1000
+
+
+######################################################################
+# Now we create the deep learning model! Braindecode comes with some
+# predefined convolutional neural network architectures for raw
+# time-domain EEG. Here, we use the shallow ConvNet model from `Deep
+# learning with convolutional neural networks for EEG decoding and
+# visualization <https://arxiv.org/abs/1703.05051>`__. These models are
+# pure `PyTorch <https://pytorch.org>`__ deep learning models, therefore
+# to use your own model, it just has to be a normal PyTorch
+# `nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__.
+#
+
+import torch
+
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 1
+# Extract number of chans and time steps from dataset
+n_chans = train_set[0][0].shape[0] - 5
+
+model = ShallowFBCSPNet(
+    n_chans,
+    n_classes,
+    final_conv_length=2,
+    add_log_softmax=False,
+)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+from braindecode.models import get_output_shape, to_dense_prediction_model
+
+to_dense_prediction_model(model)
+
+######################################################################
+# To know the models’ receptive field, we calculate the shape of model
+# output for a dummy input.
+
+n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
+
+######################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~~~~~~~
+#
+
+from braindecode.preprocessing import create_fixed_length_windows
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+
+train_set = create_fixed_length_windows(
+    train_set,
+    start_offset_samples=0,
+    stop_offset_samples=None,
+    window_size_samples=input_window_samples,
+    window_stride_samples=n_preds_per_input,
+    drop_last_window=False,
+    targets_from='channels',
+    last_target_only=False,
+    preload=False
+)
+
+valid_set = create_fixed_length_windows(
+    valid_set,
+    start_offset_samples=0,
+    stop_offset_samples=None,
+    window_size_samples=input_window_samples,
+    window_stride_samples=n_preds_per_input,
+    drop_last_window=False,
+    targets_from='channels',
+    last_target_only=False,
+    preload=False
+)
+
+test_set = create_fixed_length_windows(
+    test_set,
+    start_offset_samples=0,
+    stop_offset_samples=None,
+    window_size_samples=input_window_samples,
+    window_stride_samples=n_preds_per_input,
+    drop_last_window=False,
+    targets_from='channels',
+    last_target_only=False,
+    preload=False
+)
+######################################################################
+# We select only the thumb's finger flexion to create one model per finger.
+#
+# .. note::
+#    Methods to predict all 5 fingers flexion with the same model may be considered as well.
+#    We encourage you to find your own way to use braindecode models to predict fingers flexions.
+#
+train_set.target_transform = lambda x: x[0: 1]
+valid_set.target_transform = lambda x: x[0: 1]
+test_set.target_transform = lambda x: x[0: 1]
+
+#####################################################################
+# Training
+# --------
+#
+# In difference to trialwise decoding, we now should supply
+# ``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the
+# criterion, as well as ``criterion__loss_function`` as the loss function
+# applied to the meaned predictions.
+#
+# .. note::
+#    In this tutorial, we use some default parameters that we
+#    have found to work well for EEG motor decoding, however we strongly
+#    encourage you to perform your own hyperparameter optimization using
+#    cross validation on your training data.
+#
+
+from skorch.callbacks import LRScheduler
+from skorch.helper import predefined_split
+
+from braindecode import EEGRegressor
+from braindecode.training import CroppedTimeSeriesEpochScoring, TimeSeriesLoss
+
+# These values we found good for shallow network for EEG MI decoding:
+lr = 0.0625 * 0.01
+weight_decay = 0
+batch_size = 27  # only 27 examples in train set, otherwise set to 64
+n_epochs = 8
+
+regressor = EEGRegressor(
+    model,
+    cropped=True,
+    aggregate_predictions=False,
+    criterion=TimeSeriesLoss,
+    criterion__loss_function=torch.nn.functional.mse_loss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(valid_set),
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    iterator_train__shuffle=True,
+    batch_size=batch_size,
+    callbacks=[
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+        ('r2_train', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,
+                                                   lower_is_better=False,
+                                                   on_train=True,
+                                                   name='r2_train')
+         ),
+        ('r2_valid', CroppedTimeSeriesEpochScoring(sklearn.metrics.r2_score,
+                                                   lower_is_better=False,
+                                                   on_train=False,
+                                                   name='r2_valid')
+         )
+    ],
+    device=device,
+)
+set_log_level(verbose='WARNING')
+
+######################################################################
+# Model training for a specified number of epochs. ``y`` is None as it is already supplied
+# in the dataset.
+regressor.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Obtaining predictions and targets for the test, train, and validation dataset
+
+
+def pad_and_select_predictions(preds, y):
+    preds = np.pad(preds,
+                   ((0, 0), (0, 0), (y.shape[2] - preds.shape[2], 0)),
+                   'constant',
+                   constant_values=0)
+
+    mask = ~np.isnan(y[0, 0, :])
+    preds = np.squeeze(preds[..., mask], 0)
+    y = np.squeeze(y[..., mask], 0)
+    return y.T, preds.T
+
+
+preds_train, y_train = regressor.predict_trials(train_set, return_targets=True)
+preds_train, y_train = pad_and_select_predictions(preds_train, y_train)
+
+preds_valid, y_valid = regressor.predict_trials(valid_set, return_targets=True)
+preds_valid, y_valid = pad_and_select_predictions(preds_valid, y_valid)
+
+preds_test, y_test = regressor.predict_trials(test_set, return_targets=True)
+preds_test, y_test = pad_and_select_predictions(preds_test, y_test)
+
+######################################################################
+# Plot Results
+# ------------
+
+
+######################################################################
+# We plot target and predicted finger flexion on training, validation, and test sets.
+#
+# .. note::
+#    The model is trained and validated on limited dataset (to decrease the time needed to run
+#    this example) which does not contain diverse dataset in terms of fingers flexions and may
+#    cause overfitting. To obtain better results use whole dataset as well as improve the decoding
+#    pipeline which may be not optimal for ECoG.
+#
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.lines import Line2D
+
+fig, axes = plt.subplots(3, 1, figsize=(8, 9))
+
+axes[0].set_title('Training dataset')
+axes[0].plot(np.arange(y_train.shape[0]) / target_sfreq, y_train[:, 0], label='Target')
+axes[0].plot(np.arange(preds_train.shape[0]) / target_sfreq, preds_train[:, 0],
+             label='Predicted')
+axes[0].set_ylabel('Finger flexion')
+axes[0].legend()
+
+axes[1].set_title('Validation dataset')
+axes[1].plot(np.arange(y_valid.shape[0]) / target_sfreq, y_valid[:, 0], label='Target')
+axes[1].plot(np.arange(preds_valid.shape[0]) / target_sfreq, preds_valid[:, 0],
+             label='Predicted')
+axes[1].set_ylabel('Finger flexion')
+axes[1].legend()
+
+axes[2].set_title('Test dataset')
+axes[2].plot(np.arange(y_test.shape[0]) / target_sfreq, y_test[:, 0], label='Target')
+axes[2].plot(np.arange(preds_test.shape[0]) / target_sfreq, preds_test[:, 0], label='Predicted')
+axes[2].set_xlabel('Time [s]')
+axes[2].set_ylabel('Finger flexion')
+axes[2].legend()
+plt.tight_layout()
+
+######################################################################
+# We can compute correlation coefficients for each finger
+#
+corr_coeffs = []
+for dim in range(y_test.shape[1]):
+    corr_coeffs.append(
+        np.corrcoef(preds_test[:, dim], y_test[:, dim])[0, 1]
+    )
+print('Correlation coefficient for each dimension: ', np.round(corr_coeffs, 2))
+
+######################################################################
+# Now we use the history stored by Skorch throughout training to plot
+# accuracy and loss curves.
+# Extract loss and accuracy values for plotting from history object
+results_columns = ['train_loss', 'valid_loss', 'r2_train', 'r2_valid']
+df = pd.DataFrame(regressor.history[:, results_columns], columns=results_columns,
+                  index=regressor.history[:, 'epoch'])
+
+fig, ax1 = plt.subplots(figsize=(8, 3))
+df.loc[:, ['train_loss', 'valid_loss']].plot(
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14)
+
+ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
+ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
+
+ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+df.loc[:, ['r2_train', 'r2_valid']].plot(
+    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
+ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
+ax2.set_ylabel("R2 score", color='tab:red', fontsize=14)
+ax1.set_xlabel("Epoch", fontsize=14)
+
+# where some data has already been plotted to ax
+handles = []
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-',
+                      label='Train'))
+handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':',
+                      label='Valid'))
+plt.legend(handles, [h.get_label() for h in handles], fontsize=14, loc='center right')
+plt.tight_layout()
diff --git a/stable/_downloads/c9c12da7d088388ecce38afe9fe69371/plot_dataset_example.py b/stable/_downloads/c9c12da7d088388ecce38afe9fe69371/plot_dataset_example.py
deleted file mode 100644
index 0500e84f..00000000
--- a/stable/_downloads/c9c12da7d088388ecce38afe9fe69371/plot_dataset_example.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""MOABB Dataset Example
-========================
-
-In this example, we show how to fetch and prepare a MOABB dataset for usage
-with Braindecode.
-"""
-
-# Authors: Lukas Gemein <l.gemein@gmail.com>
-#          Hubert Banville <hubert.jbanville@gmail.com>
-#          Simon Brandt <simonbrandt@protonmail.com>
-#
-# License: BSD (3-clause)
-
-import matplotlib.pyplot as plt
-
-from braindecode.datasets import MOABBDataset
-from braindecode.preprocessing import \
-    create_windows_from_events, create_fixed_length_windows
-from braindecode.preprocessing import preprocess, Preprocessor
-
-###############################################################################
-# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
-dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
-
-###############################################################################
-# ds has a pandas DataFrame with additional description of its internal datasets
-dataset.description
-
-##############################################################################
-# We can iterate through ds which yields one time point of a continuous signal x,
-# and a target y (which can be None if targets are not defined for the entire
-# continuous signal).
-for x, y in dataset:
-    print(x.shape, y)
-    break
-
-##############################################################################
-# We can apply preprocessing transforms that are defined in mne and work
-# in-place, such as resampling, bandpass filtering, or electrode selection.
-preprocessors = [
-    Preprocessor('pick_types', eeg=True, meg=False, stim=True),
-    Preprocessor('resample', sfreq=100)
-]
-print(dataset.datasets[0].raw.info["sfreq"])
-preprocess(dataset, preprocessors)
-print(dataset.datasets[0].raw.info["sfreq"])
-
-###############################################################################
-# We can easily split ds based on a criteria applied to the description
-# DataFrame:
-subsets = dataset.split("session")
-print({subset_name: len(subset) for subset_name, subset in subsets.items()})
-
-###############################################################################
-# Next, we use a windower to extract events from the dataset based on events:
-windows_dataset = create_windows_from_events(
-    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=100,
-    window_size_samples=400, window_stride_samples=100,
-    drop_last_window=False)
-
-###############################################################################
-# We can iterate through the windows_ds which yields a window x,
-# a target y, and window_ind (which itself contains ``i_window_in_trial``,
-# ``i_start_in_trial``, and ``i_stop_in_trial``, which are required for
-# combining window predictions in the scorer).
-for x, y, window_ind in windows_dataset:
-    print(x.shape, y, window_ind)
-    break
-
-###############################################################################
-# We visually inspect the windows:
-max_i = 2
-fig, ax_arr = plt.subplots(1, max_i + 1, figsize=(3.5 * (max_i + 1), 3.5),
-                           sharex=True, sharey=True)
-for i, (x, y, window_ind) in enumerate(windows_dataset):
-    ax_arr[i].plot(x.T)
-    ax_arr[i].set_ylim(-4e-5, 4e-5)
-    ax_arr[i].set_title(f"label={y}")
-    if i == max_i:
-        break
-
-fig.tight_layout()
-
-###############################################################################
-# Alternatively, we can create evenly spaced ("sliding") windows using a
-# different windower.
-sliding_windows_dataset = create_fixed_length_windows(
-    dataset, start_offset_samples=0, stop_offset_samples=0,
-    window_size_samples=1200, window_stride_samples=1000,
-    drop_last_window=False)
-
-print(len(sliding_windows_dataset))
-for x, y, window_ind in sliding_windows_dataset:
-    print(x.shape, y, window_ind)
-    break
-
-sliding_windows_dataset.description
-
-###############################################################################
-# Transforms can also be applied on windows in the same way as shown
-# above on continuous data:
-
-
-def crop_windows(windows, start_offset_samples, stop_offset_samples):
-    fs = windows.info["sfreq"]
-    windows.crop(tmin=start_offset_samples / fs, tmax=stop_offset_samples / fs,
-                 include_tmax=False)
-
-
-epochs_preprocessors = [
-    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
-    Preprocessor(crop_windows, apply_on_array=False, start_offset_samples=100,
-                 stop_offset_samples=900)
-]
-
-print(windows_dataset.datasets[0].windows.info["ch_names"],
-      len(windows_dataset.datasets[0].windows.times))
-preprocess(windows_dataset, epochs_preprocessors)
-print(windows_dataset.datasets[0].windows.info["ch_names"],
-      len(windows_dataset.datasets[0].windows.times))
-
-
-max_i = 2
-fig, ax_arr = plt.subplots(1, max_i + 1, figsize=(3.5 * (max_i + 1), 3.5),
-                           sharex=True, sharey=True)
-for i, (x, y, window_ind) in enumerate(windows_dataset):
-    ax_arr[i].plot(x.T)
-    ax_arr[i].set_ylim(-4e-5, 4e-5)
-    ax_arr[i].set_title(f"label={y}")
-    if i == max_i:
-        break
-
-fig.tight_layout()
-plt.show()
-
-###############################################################################
-# Again, we can easily split windows_ds based on some criteria in the
-# description DataFrame:
-subsets = windows_dataset.split("session")
-print({subset_name: len(subset) for subset_name, subset in subsets.items()})
diff --git a/stable/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py b/stable/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py
new file mode 100644
index 00000000..b501d78c
--- /dev/null
+++ b/stable/_downloads/ce2c92ca74396ce848a69d8dcddef743/plot_sleep_staging_usleep.py
@@ -0,0 +1,335 @@
+"""
+Sleep staging on the Sleep Physionet dataset using U-Sleep network
+==================================================================
+
+.. note::
+    Please take a look at the simpler sleep staging example
+    :ref:`here <sphx_glr_auto_examples_plot_sleep_staging.py>`
+    before going through this example. The current example uses a more complex
+    architecture and a sequence-to-sequence (seq2seq) approach.
+
+This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the U-Sleep approach of [1]_ to learn on sequences of EEG
+windows using the openly accessible Sleep Physionet dataset [2]_ [3]_.
+
+.. warning::
+    The example is written to have a very short execution time.
+    This number of epochs is here too small and very few recordings are used.
+    To obtain competitive results you need to use more data and more epochs.
+
+"""
+# Authors: Theo Gnassounou <theo.gnassounou@inria.fr>
+#          Omar Chehab <l-emir-omar.chehab@inria.fr>
+#
+# License: BSD (3-clause)
+
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading
+# ~~~~~~~
+#
+# First, we load the data using the
+# :class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load
+# two recordings from two different individuals: we will use the first one to
+# train our network and the second one to evaluate performance (as in the `MNE`_
+# sleep staging example).
+#
+# .. _MNE: https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html
+#
+
+from braindecode.datasets import SleepPhysionet
+
+subject_ids = [0, 1]
+crop = (0, 30 * 400)  # we only keep 400 windows of 30s to speed example
+dataset = SleepPhysionet(
+    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30,
+    crop=crop)
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# Next, we preprocess the raw data. We scale each channel recording-wise to
+# have zero median and unit interquartile range. We don't upsample to 128 Hz as
+# done in [1]_ so that we keep the example as light as possible. No filtering
+# is described in [1]_.
+
+from braindecode.preprocessing import preprocess, Preprocessor
+from sklearn.preprocessing import robust_scale
+
+preprocessors = [Preprocessor(robust_scale, channel_wise=True)]
+
+# Transform the data
+preprocess(dataset, preprocessors)
+
+######################################################################
+# Extract windows
+# ~~~~~~~~~~~~~~~
+#
+# We extract 30-s windows to be used in the classification task.
+
+from braindecode.preprocessing import create_windows_from_events
+
+mapping = {  # We merge stages 3 and 4 following AASM standards.
+    'Sleep stage W': 0,
+    'Sleep stage 1': 1,
+    'Sleep stage 2': 2,
+    'Sleep stage 3': 3,
+    'Sleep stage 4': 3,
+    'Sleep stage R': 4,
+}
+
+window_size_s = 30
+sfreq = 100
+window_size_samples = window_size_s * sfreq
+
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+    window_size_samples=window_size_samples,
+    window_stride_samples=window_size_samples,
+    preload=True,
+    mapping=mapping,
+)
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We split the dataset into training and validation set taking
+# every other subject as train or valid.
+
+split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])
+splits = windows_dataset.split(split_ids)
+train_set, valid_set = splits["train"], splits["valid"]
+
+######################################################################
+# Create sequence samplers
+# ------------------------
+#
+# Following the sequence-to-sequence approach of [1]_, we need to provide our
+# neural network with sequences of windows. We can achieve this by defining
+# Sampler objects that return sequences of windows.
+# Non-overlapping sequences of 35 windows are used in [1]_, however to limit
+# the memory requirements for this example we use shorter sequences of 3
+# windows.
+
+from braindecode.samplers import SequenceSampler
+
+n_windows = 3  # Sequences of 3 consecutive windows; originally 35 in paper
+n_windows_stride = 3  # Non-overlapping sequences
+
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
+valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
+
+# Print number of examples per class
+print(len(train_sampler))
+print(len(valid_sampler))
+
+######################################################################
+# Finally, since some sleep stages appear a lot more often than others (e.g.
+# most of the night is spent in the N2 stage), the classes are imbalanced. To
+# avoid overfitting to the more frequent classes, we compute weights that we
+# will provide to the loss function when training.
+
+import numpy as np
+from sklearn.utils import compute_class_weight
+
+y_train = [train_set[idx][1][1] for idx in train_sampler]
+class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
+
+######################################################################
+# Create model
+# ------------
+#
+# We can now create the deep learning model. In this tutorial, we use the
+# U-Sleep architecture introduced in [1]_, which is fully convolutional
+# neural network.
+
+import torch
+from braindecode.util import set_random_seeds
+from braindecode.models import USleep
+
+cuda = torch.cuda.is_available()  # check if GPU is available
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+set_random_seeds(seed=31, cuda=cuda)
+
+n_classes = 5
+classes = list(range(n_classes))
+# Extract number of channels and time steps from dataset
+in_chans, input_size_samples = train_set[0][0].shape
+model = USleep(
+    n_chans=in_chans,
+    sfreq=sfreq,
+    depth=12,
+    with_skip_connection=True,
+    n_outputs=n_classes,
+    n_times=input_size_samples
+)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+######################################################################
+# Training
+# --------
+#
+# We can now train our network. :class:`braindecode.EEGClassifier` is a
+# braindecode object that is responsible for managing the training of neural
+# networks. It inherits from :class:`skorch.NeuralNetClassifier`, so the
+# training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+# .. note::
+#    We use different hyperparameters from [1]_, as these hyperparameters were
+#    optimized on different datasets and with a different number of recordings.
+#    Generally speaking, it is recommended to perform hyperparameter
+#    optimization if reusing this code on a different dataset or with more
+#    recordings.
+
+from skorch.helper import predefined_split
+from skorch.callbacks import EpochScoring
+from braindecode import EEGClassifier
+
+lr = 1e-3
+batch_size = 32
+n_epochs = 3  # we use few epochs for speed and but more than one for plotting
+
+from sklearn.metrics import balanced_accuracy_score
+
+
+def balanced_accuracy_multi(model, X, y):
+    y_pred = model.predict(X)
+    return balanced_accuracy_score(y.flatten(), y_pred.flatten())
+
+
+train_bal_acc = EpochScoring(
+    scoring=balanced_accuracy_multi,
+    on_train=True,
+    name='train_bal_acc',
+    lower_is_better=False,
+)
+valid_bal_acc = EpochScoring(
+    scoring=balanced_accuracy_multi,
+    on_train=False,
+    name='valid_bal_acc',
+    lower_is_better=False,
+)
+callbacks = [
+    ('train_bal_acc', train_bal_acc),
+    ('valid_bal_acc', valid_bal_acc)
+]
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.CrossEntropyLoss,
+    criterion__weight=torch.Tensor(class_weights).to(device),
+    optimizer=torch.optim.Adam,
+    iterator_train__shuffle=False,
+    iterator_train__sampler=train_sampler,
+    iterator_valid__sampler=valid_sampler,
+    train_split=predefined_split(valid_set),  # using valid_set for validation
+    optimizer__lr=lr,
+    batch_size=batch_size,
+    callbacks=callbacks,
+    device=device,
+    classes=classes,
+)
+# Deactivate the default valid_acc callback:
+clf.set_params(callbacks__valid_acc=None)
+
+# Model training for a specified number of epochs. `y` is None as it is already
+# supplied in the dataset.
+clf.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Plot results
+# ------------
+#
+# We use the history stored by Skorch during training to plot the performance of
+# the model throughout training. Specifically, we plot the loss and the balanced
+# balanced accuracy for the training and validation sets.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Extract loss and balanced accuracy values for plotting from history object
+df = pd.DataFrame(clf.history.to_list())
+df.index.name = "Epoch"
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)
+df[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)
+df[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)
+ax1.set_ylabel('Loss')
+ax2.set_ylabel('Balanced accuracy')
+ax1.legend(['Train', 'Valid'])
+ax2.legend(['Train', 'Valid'])
+fig.tight_layout()
+plt.show()
+
+######################################################################
+# Finally, we also display the confusion matrix and classification report:
+from braindecode.visualization import plot_confusion_matrix
+from sklearn.metrics import confusion_matrix, classification_report
+
+y_true = np.array([valid_set[i][1] for i in valid_sampler])
+y_pred = clf.predict(valid_set)
+
+confusion_mat = confusion_matrix(y_true.flatten(), y_pred.flatten())
+
+plot_confusion_matrix(confusion_mat=confusion_mat,
+                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])
+
+print(classification_report(y_true.flatten(), y_pred.flatten()))
+
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true.flatten(), color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
+
+######################################################################
+# Our model was able to learn, as shown by the decreasing training and
+# validation loss values, despite the low amount of data that was available
+# (only two recordings in this example). To further improve performance, more
+# recordings should be included in the training set, the model should be
+# trained for more epochs and hyperparameters should be optimized.
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+#        U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+#        https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py
+#
+# .. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py b/stable/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py
new file mode 100644
index 00000000..2916d832
--- /dev/null
+++ b/stable/_downloads/cebe93e1c1784efb2204e0309d1cc9a8/plot_bcic_iv_2a_moabb_cropped.py
@@ -0,0 +1,386 @@
+# -*- coding: utf-8 -*-
+"""
+Cropped Decoding on BCIC IV 2a Dataset
+======================================
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+######################################################################
+# Building on the `Trialwise decoding <./plot_bcic_iv_2a_moabb_trial.html>`__,
+# we now do more data-efficient cropped decoding!
+#
+# In Braindecode, there are two supported configurations created for
+# training models: trialwise decoding and cropped decoding. We will
+# explain this visually by comparing trialwise to cropped decoding.
+#
+# .. image:: https://braindecode.org/stable/_static/trialwise_explanation.png
+#    :alt: Trialwise decoding
+# .. image:: https://braindecode.org/stable/_static/cropped_explanation.png
+#    :alt: Cropped decoding
+# On the left, you see trialwise decoding:
+#
+# 1. A complete trial is pushed through the network.
+# 2. The network produces a prediction.
+# 3. The prediction is compared to the target (label) for that trial to
+#    compute the loss.
+#
+# On the right, you see cropped decoding:
+#
+# 1. Instead of a complete trial, crops are pushed through the network.
+# 2. For computational efficiency, multiple neighbouring crops are pushed
+#    through the network simultaneously (these neighbouring crops are
+#    called compute windows)
+# 3. Therefore, the network produces multiple predictions (one per crop in
+#    the window)
+# 4. The individual crop predictions are *AVERAGED* before computing the
+#    loss function
+#
+# This averaging of predictions of small sub-windows is the key difference
+# between trialwise and cropped decoding. It was introduced in [1]_ and it impact
+# on the parameters of the network.
+#
+# It is important to note that the averaging of predictions is only done
+# during training. During testing, the network is still applied to crops
+# and the predictions are averaged afterwards.
+#
+#
+# .. note::
+#
+#     -  The network architecture implicitly defines the crop size (it is the
+#        receptive field size, i.e., the number of timesteps the network uses
+#        to make a single prediction)
+#     -  The window size is a user-defined hyperparameter, called
+#        ``input_window_samples`` in Braindecode. It mostly affects runtime
+#        (larger window sizes should be faster). As a rule of thumb, you can
+#        set it to two times the crop size.
+#     -  Crop size and window size together define how many predictions the
+#        network makes per window: ``#window − #crop + 1 = #predictions``
+#
+# .. note::
+#
+#     For cropped decoding, the above training setup is mathematically
+#     similar to sampling crops in your dataset, pushing them through the
+#     network and training directly on the individual crops. However, the
+#     if their position would be randomly selected, the crops would be less
+#     correlated in contrast to the neighbourhood crops selected from a window.
+#     At the same time, the above training setup is much faster as it avoids
+#     redundant computations by using dilated convolutions, see [2]_.
+#     However, the two setups are only mathematically related in case (1)
+#     your network does not use any padding or only left padding and
+#     (2) your loss function leads
+#     to the same gradients when using the averaged output. The first is true
+#     for our shallow and deep ConvNet models and the second is true for the
+#     log-softmax outputs and negative log likelihood loss that is typically
+#     used for classification in PyTorch.
+
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading and preprocessing stays the same as in the `Trialwise decoding
+# tutorial <./plot_bcic_iv_2a_moabb_trial.html>`__.
+
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014_001", subject_ids=[subject_id])
+
+from numpy import multiply
+
+from braindecode.preprocessing import (
+    Preprocessor,
+    exponential_moving_standardize,
+    preprocess,
+)
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),
+    # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),
+    # Bandpass filter
+    Preprocessor(exponential_moving_standardize,
+                 # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+# Transform the data
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Create model and compute windowing parameters
+# ---------------------------------------------
+# In contrast to trialwise decoding, we first have to create the model
+# before we can cut the dataset into windows. This is because we need to
+# know the neural network parameters to know how large the sub-window
+# stride should be.
+#
+# We first choose the compute/input window size that will be fed to the
+# network during training. This has to be larger than the networks
+# the number of timesteps size and can otherwise be chosen for computational
+# efficiency (see explanations in the beginning of this tutorial). Here we
+# choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.
+#
+
+input_window_samples = 1000
+
+######################################################################
+# Now we create the model. To enable it to be used in cropped decoding
+# efficiently, we manually set the length of the final convolution layer
+# to some length that makes the number of timesteps of the ConvNet smaller
+# than ``input_window_samples`` (see ``final_conv_length=30`` in the model
+# definition).
+#
+
+import torch
+
+from braindecode.models import ShallowFBCSPNet
+from braindecode.util import set_random_seeds
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+# Extract number of chans from dataset
+n_chans = dataset[0][0].shape[0]
+
+model = ShallowFBCSPNet(
+    n_chans,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length=30,
+)
+
+# Display torchinfo table describing the model
+print(model)
+
+# Send model to GPU
+if cuda:
+    _ = model.cuda()
+
+######################################################################
+# And now we transform model with strides to a model that outputs dense
+# prediction, so we can use it to obtain predictions for all
+# crops.
+#
+model.to_dense_prediction_model()
+
+######################################################################
+# To know the models’ output shape without the last layer, we calculate the
+# shape of model output for a dummy input.
+#
+
+n_preds_per_input = model.get_output_shape()[2]
+
+######################################################################
+# Cut the data into windows
+# -------------------------
+# In contrast to trialwise decoding, we have to supply an explicit
+# window size and window stride to the ``create_windows_from_events``
+# function.
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to define how
+# trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    window_size_samples=input_window_samples,
+    window_stride_samples=n_preds_per_input,
+    drop_last_window=False,
+    preload=True
+)
+
+######################################################################
+# Split the dataset
+# -----------------
+#
+# This code is the same as in trialwise decoding.
+#
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
+
+######################################################################
+# Training
+# --------
+# In difference to trialwise decoding, we now should supply
+# ``cropped=True`` to the EEGClassifier, and ``CroppedLoss`` as the
+# criterion, as well as ``criterion__loss_function`` as the loss function
+# applied to the meaned predictions.
+#
+# .. note::
+#
+#     In this tutorial, we use some default parameters that we
+#     have found to work well for motor decoding, however we strongly
+#     encourage you to perform your own hyperparameter optimization using
+#     cross validation on your training data.
+#
+
+from skorch.callbacks import LRScheduler
+from skorch.helper import predefined_split
+
+from braindecode import EEGClassifier
+from braindecode.training import CroppedLoss
+
+# These values we found good for shallow network:
+lr = 0.0625 * 0.01
+weight_decay = 0
+
+# For deep4 they should be:
+# lr = 1 * 0.01
+# weight_decay = 0.5 * 0.001
+
+batch_size = 64
+n_epochs = 2
+
+clf = EEGClassifier(
+    model,
+    cropped=True,
+    criterion=CroppedLoss,
+    criterion__loss_function=torch.nn.functional.nll_loss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(valid_set),
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    iterator_train__shuffle=True,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+)
+# Model training for a specified number of epochs. `y` is None as it is already supplied
+# in the dataset.
+_ = clf.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Plot Results
+# ----------------
+# .. note::
+#
+#     Note that we drop further in the classification error and
+#     loss as in the trialwise decoding tutorial.
+#
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from matplotlib.lines import Line2D
+
+# Extract loss and accuracy values for plotting from history object
+results_columns = ['train_loss', 'valid_loss', 'train_accuracy',
+                   'valid_accuracy']
+df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,
+                  index=clf.history[:, 'epoch'])
+
+# get percent of misclass for better visual comparison to loss
+df = df.assign(train_misclass=100 - 100 * df.train_accuracy,
+               valid_misclass=100 - 100 * df.valid_accuracy)
+
+fig, ax1 = plt.subplots(figsize=(8, 3))
+df.loc[:, ['train_loss', 'valid_loss']].plot(
+    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False,
+    fontsize=14)
+
+ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)
+ax1.set_ylabel("Loss", color='tab:blue', fontsize=14)
+
+ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
+
+df.loc[:, ['train_misclass', 'valid_misclass']].plot(
+    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False)
+ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)
+ax2.set_ylabel("Misclassification Rate [%]", color='tab:red', fontsize=14)
+ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend
+ax1.set_xlabel("Epoch", fontsize=14)
+
+# where some data has already been plotted to ax
+handles = []
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))
+handles.append(
+    Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))
+plt.legend(handles, [h.get_label() for h in handles], fontsize=14)
+plt.tight_layout()
+
+######################################################################
+# Plot Confusion Matrix
+# ---------------------
+#
+# Generate a confusion matrix as in [2]_
+#
+
+from sklearn.metrics import confusion_matrix
+
+from braindecode.visualization import plot_confusion_matrix
+
+# generate confusion matrices
+# get the targets
+y_true = valid_set.get_metadata().target
+y_pred = clf.predict(valid_set)
+
+# generating confusion matrix
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+# add class labels
+# label_dict is class_name : str -> i_class : int
+label_dict = valid_set.datasets[0].window_kwargs[0][1]['mapping']
+# sort the labels by values (values are integer class labels)
+labels = [k for k, v in sorted(label_dict.items(), key=lambda kv: kv[1])]
+
+# plot the basic conf. matrix
+plot_confusion_matrix(confusion_mat, class_names=labels)
+
+##########################################################################
+#
+#
+# References
+# ----------
+#
+# .. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+#        Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+#        and Nolte, G., 2012. Review of the BCI competition IV.
+#        Frontiers in neuroscience, 6, p.55.
+#
+# .. [2] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+#        Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Hum. Brain Mapping, 38: 5391-5420. https://doi.org/10.1002/hbm.23730.
diff --git a/stable/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py b/stable/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py
new file mode 100644
index 00000000..c7ff530b
--- /dev/null
+++ b/stable/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py
@@ -0,0 +1,365 @@
+"""
+Sleep staging on the Sleep Physionet dataset using Chambon2018 network
+======================================================================
+
+This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the time distributed approach of [1]_ to learn on
+sequences of EEG windows using the openly accessible Sleep Physionet dataset
+[2]_ [3]_.
+
+"""
+# Authors: Hubert Banville <hubert.jbanville@gmail.com>
+#
+# License: BSD (3-clause)
+
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading
+# ~~~~~~~
+#
+# First, we load the data using the
+# :class:`braindecode.datasets.sleep_physionet.SleepPhysionet` class. We load
+# two recordings from two different individuals: we will use the first one to
+# train our network and the second one to evaluate performance (as in the `MNE`_
+# sleep staging example).
+#
+# .. _MNE: https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html
+#
+
+from numbers import Integral
+from braindecode.datasets import SleepPhysionet
+
+subject_ids = [0, 1]
+dataset = SleepPhysionet(
+    subject_ids=subject_ids, recording_ids=[2], crop_wake_mins=30)
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+# Next, we preprocess the raw data. We convert the data to microvolts and apply
+# a lowpass filter. We omit the downsampling step of [1]_ as the Sleep
+# Physionet data is already sampled at a lower 100 Hz.
+
+from braindecode.preprocessing import preprocess, Preprocessor
+from numpy import multiply
+
+high_cut_hz = 30
+factor = 1e6
+
+preprocessors = [
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
+    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
+]
+
+# Transform the data
+preprocess(dataset, preprocessors)
+
+######################################################################
+# Extract windows
+# ~~~~~~~~~~~~~~~
+#
+# We extract 30-s windows to be used in the classification task.
+
+from braindecode.preprocessing import create_windows_from_events
+
+mapping = {  # We merge stages 3 and 4 following AASM standards.
+    'Sleep stage W': 0,
+    'Sleep stage 1': 1,
+    'Sleep stage 2': 2,
+    'Sleep stage 3': 3,
+    'Sleep stage 4': 3,
+    'Sleep stage R': 4
+}
+
+window_size_s = 30
+sfreq = 100
+window_size_samples = window_size_s * sfreq
+
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=0,
+    trial_stop_offset_samples=0,
+    window_size_samples=window_size_samples,
+    window_stride_samples=window_size_samples,
+    preload=True,
+    mapping=mapping
+)
+
+######################################################################
+# Window preprocessing
+# ~~~~~~~~~~~~~~~~~~~~
+#
+# We also preprocess the windows by applying channel-wise z-score normalization
+# in each window.
+
+from sklearn.preprocessing import scale as standard_scale
+
+preprocess(windows_dataset, [Preprocessor(standard_scale, channel_wise=True)])
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# We split the dataset into training and validation set taking
+# every other subject as train or valid.
+
+split_ids = dict(train=subject_ids[::2], valid=subject_ids[1::2])
+splits = windows_dataset.split(split_ids)
+train_set, valid_set = splits["train"], splits["valid"]
+
+######################################################################
+# Create sequence samplers
+# ------------------------
+#
+# Following the time distributed approach of [1]_, we need to provide our
+# neural network with sequences of windows, such that the embeddings of
+# multiple consecutive windows can be concatenated and provided to a final
+# classifier. We can achieve this by defining Sampler objects that return
+# sequences of window indices.
+# To simplify the example, we train the whole model end-to-end on sequences,
+# rather than using the two-step approach of [1]_ (i.e. training the feature
+# extractor on single windows, then freezing its weights and training the
+# classifier).
+#
+
+import numpy as np
+from braindecode.samplers import SequenceSampler
+
+n_windows = 3  # Sequences of 3 consecutive windows
+n_windows_stride = 3  # Maximally overlapping sequences
+
+train_sampler = SequenceSampler(
+    train_set.get_metadata(), n_windows, n_windows_stride, randomize=True
+)
+valid_sampler = SequenceSampler(valid_set.get_metadata(), n_windows, n_windows_stride)
+
+# Print number of examples per class
+print('Training examples: ', len(train_sampler))
+print('Validation examples: ', len(valid_sampler))
+
+
+######################################################################
+# We also implement a transform to extract the label of the center window of a
+# sequence to use it as target.
+
+
+# Use label of center window in the sequence
+def get_center_label(x):
+    if isinstance(x, Integral):
+        return x
+    return x[np.ceil(len(x) / 2).astype(int)] if len(x) > 1 else x
+
+
+train_set.target_transform = get_center_label
+valid_set.target_transform = get_center_label
+
+######################################################################
+# Finally, since some sleep stages appear a lot more often than others (e.g.
+# most of the night is spent in the N2 stage), the classes are imbalanced. To
+# avoid overfitting on the more frequent classes, we compute weights that we
+# will provide to the loss function when training.
+
+from sklearn.utils import compute_class_weight
+
+y_train = [train_set[idx][1] for idx in train_sampler]
+class_weights = compute_class_weight('balanced', classes=np.unique(y_train), y=y_train)
+
+######################################################################
+# Create model
+# ------------
+#
+# We can now create the deep learning model. In this tutorial, we use the sleep
+# staging architecture introduced in [1]_, which is a four-layer convolutional
+# neural network. We use the time distributed version of the model, where the
+# feature vectors of a sequence of windows are concatenated and passed to a
+# linear layer for classification.
+#
+
+import torch
+from torch import nn
+from braindecode.util import set_random_seeds
+from braindecode.models import SleepStagerChambon2018, TimeDistributed
+
+cuda = torch.cuda.is_available()  # check if GPU is available
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+set_random_seeds(seed=31, cuda=cuda)
+
+n_classes = 5
+# Extract number of channels and time steps from dataset
+n_channels, input_size_samples = train_set[0][0].shape
+
+feat_extractor = SleepStagerChambon2018(
+    n_channels,
+    sfreq,
+    n_outputs=n_classes,
+    n_times=input_size_samples,
+    return_feats=True
+)
+
+model = nn.Sequential(
+    TimeDistributed(feat_extractor),  # apply model on each 30-s window
+    nn.Sequential(  # apply linear layer on concatenated feature vectors
+        nn.Flatten(start_dim=1),
+        nn.Dropout(0.5),
+        nn.Linear(feat_extractor.len_last_layer * n_windows, n_classes)
+    )
+)
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# Training
+# --------
+#
+# We can now train our network. :class:`braindecode.EEGClassifier` is a
+# braindecode object that is responsible for managing the training of neural
+# networks. It inherits from :class:`skorch.NeuralNetClassifier`, so the
+# training logic is the same as in
+# `Skorch <https://skorch.readthedocs.io/en/stable/>`__.
+#
+# .. note::
+#    We use different hyperparameters from [1]_, as these hyperparameters were
+#    optimized on a different dataset (MASS SS3) and with a different number of
+#    recordings. Generally speaking, it is recommended to perform
+#    hyperparameter optimization if reusing this code on a different dataset or
+#    with more recordings.
+#
+
+from skorch.helper import predefined_split
+from skorch.callbacks import EpochScoring
+from braindecode import EEGClassifier
+
+lr = 1e-3
+batch_size = 32
+n_epochs = 10
+
+train_bal_acc = EpochScoring(
+    scoring='balanced_accuracy', on_train=True, name='train_bal_acc',
+    lower_is_better=False)
+valid_bal_acc = EpochScoring(
+    scoring='balanced_accuracy', on_train=False, name='valid_bal_acc',
+    lower_is_better=False)
+callbacks = [
+    ('train_bal_acc', train_bal_acc),
+    ('valid_bal_acc', valid_bal_acc)
+]
+
+clf = EEGClassifier(
+    model,
+    criterion=torch.nn.CrossEntropyLoss,
+    criterion__weight=torch.Tensor(class_weights).to(device),
+    optimizer=torch.optim.Adam,
+    iterator_train__shuffle=False,
+    iterator_train__sampler=train_sampler,
+    iterator_valid__sampler=valid_sampler,
+    train_split=predefined_split(valid_set),  # using valid_set for validation
+    optimizer__lr=lr,
+    batch_size=batch_size,
+    callbacks=callbacks,
+    device=device,
+    classes=np.unique(y_train),
+)
+# Model training for a specified number of epochs. `y` is None as it is already
+# supplied in the dataset.
+clf.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Plot results
+# ------------
+#
+# We use the history stored by Skorch during training to plot the performance of
+# the model throughout training. Specifically, we plot the loss and the balanced
+# balanced accuracy for the training and validation sets.
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Extract loss and balanced accuracy values for plotting from history object
+df = pd.DataFrame(clf.history.to_list())
+df.index.name = "Epoch"
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)
+df[['train_loss', 'valid_loss']].plot(color=['r', 'b'], ax=ax1)
+df[['train_bal_acc', 'valid_bal_acc']].plot(color=['r', 'b'], ax=ax2)
+ax1.set_ylabel('Loss')
+ax2.set_ylabel('Balanced accuracy')
+ax1.legend(['Train', 'Valid'])
+ax2.legend(['Train', 'Valid'])
+fig.tight_layout()
+plt.show()
+
+######################################################################
+# Finally, we also display the confusion matrix and classification report:
+#
+
+from sklearn.metrics import confusion_matrix, classification_report
+from braindecode.visualization import plot_confusion_matrix
+
+y_true = [valid_set[[i]][1][0] for i in range(len(valid_sampler))]
+y_pred = clf.predict(valid_set)
+
+confusion_mat = confusion_matrix(y_true, y_pred)
+
+plot_confusion_matrix(confusion_mat=confusion_mat,
+                      class_names=['Wake', 'N1', 'N2', 'N3', 'REM'])
+
+print(classification_report(y_true, y_pred))
+
+######################################################################
+# Finally, we can also visualize the hypnogram of the recording we used for
+# validation, with the predicted sleep stages overlaid on top of the true
+# sleep stages. We can see that the model cannot correctly identify the
+# different sleep stages with this amount of training.
+
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(15, 5))
+ax.plot(y_true, color='b', label='Expert annotations')
+ax.plot(y_pred.flatten(), color='r', label='Predict annotations', alpha=0.5)
+ax.set_xlabel('Time (epochs)')
+ax.set_ylabel('Sleep stage')
+
+######################################################################
+# Our model was able to learn despite the low amount of data that was available
+# (only two recordings in this example) and reached a balanced accuracy of
+# about 36% in a 5-class classification task (chance-level = 20%) on held-out
+# data.
+#
+# .. note::
+#    To further improve performance, more recordings should be included in the
+#    training set, and hyperparameters should be selected accordingly.
+#    Increasing the sequence length was also shown in [1]_ to help improve
+#    performance, especially when few EEG channels are available.
+
+###########################################################################
+# References
+# ----------
+#
+# .. [1] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
+#       Classification Using Multivariate and Multimodal Time Series.
+#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+#       (758-769)
+#
+# .. [2] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+#        a sleep-dependent neuronal feedback loop: the slow-wave
+#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
+#
+# .. [3] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+#        Research Resource for Complex Physiologic Signals.
+#        Circulation 101(23):e215-e220
diff --git a/stable/_downloads/d351841512f246981cc35aadb0422301/plot_tuh_eeg_corpus.ipynb b/stable/_downloads/d351841512f246981cc35aadb0422301/plot_tuh_eeg_corpus.ipynb
deleted file mode 100644
index 09debca7..00000000
--- a/stable/_downloads/d351841512f246981cc35aadb0422301/plot_tuh_eeg_corpus.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Process a big data EEG resource (TUH EEG Corpus)\n\nIn this example, we showcase usage of the Temple University Hospital EEG Corpus\n(https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)\nincluding simple preprocessing steps as well as cutting of compute windows.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "# Author: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows, scale as multiply)\n\n\nplt.style.use('seaborn')\nmne.set_log_level('ERROR')  # avoid messages everytime a window is extracted"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you want to try this code with the actual data, please delete the next\nsection. We are required to mock some dataset functionality, since the data\nis not available at creation time of this example.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We start by creating a TUH dataset. First, the class generates a description\nof the recordings in `TUH_PATH` (which is later accessible as\n`tuh.description`) without actually touching the files. This will parse\ninformation from file paths such as patient id, recording data, etc and should\nbe really fast. Afterwards, the files are sorted chronologically by year,\nmonth, day, patient id, recording session and segment.\nIn the following, a subset of the description corresponding to `recording_ids`\nis used.\nAfterwards, the files will be iterated a second time, slower than before.\nThe files are now actually touched. Additional information about subjects\nlike age and gender are parsed directly from the EDF file header. If existent,\nthe physician report is added to the description. Furthermore, the recordings\nare read with `mne.io.read_raw_edf` with `preload=False`. Finally, we will get\na `BaseConcatDataset` of `BaseDatasets` each holding a single\n`nme.io.Raw` which is fully compatible with other braindecode functionalities.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "TUH_PATH = 'please insert actual path to data here'\nN_JOBS = 2  # specify the number of jobs for loading and windowing\ntuh = TUH(\n    path=TUH_PATH,\n    recording_ids=None,\n    target_name=None,\n    preload=False,\n    add_physician_reports=False,\n    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS,  # Mock dataset can't\n    # be loaded in parallel\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can easily create descriptive statistics using the description `DataFrame`,\nfor example an age histogram split by gender of patients.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "fig, ax = plt.subplots(1, 1, figsize=(15, 5))\ngenders = tuh.description.gender.unique()\nx = [tuh.description.age[tuh.description.gender == g] for g in genders]\nax.hist(\n    x=x,\n    stacked=True,\n    bins=np.arange(100, dtype=int),\n    alpha=.5,\n)\nax.legend(genders)\nax.set_xlabel('Age [years]')\nax.set_ylabel('Count')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we will perform some preprocessing steps. First, we will do some\nselection of available recordings based on the duration. We will select those\nrecordings, that have at least five minutes duration. Data is not loaded here.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def select_by_duration(ds, tmin=0, tmax=None):\n    if tmax is None:\n        tmax = np.inf\n    # determine length of the recordings and select based on tmin and tmax\n    split_ids = []\n    for d_i, d in enumerate(ds.datasets):\n        duration = d.raw.n_times / d.raw.info['sfreq']\n        if tmin <= duration <= tmax:\n            split_ids.append(d_i)\n    splits = ds.split(split_ids)\n    split = splits['0']\n    return split\n\n\ntmin = 5 * 60\ntmax = None\ntuh = select_by_duration(tuh, tmin, tmax)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we will discard all recordings that have an incomplete channel\nconfiguration (wrt the channels that we are interested in, i.e. the 21\nchannels of the international 10-20-placement). The dataset is subdivided into\nrecordings with 'le' and 'ar' reference which we will have to consider. Data\nis not loaded here.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "short_ch_names = sorted([\n    'A1', 'A2',\n    'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',\n    'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])\nar_ch_names = sorted([\n    'EEG A1-REF', 'EEG A2-REF',\n    'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',\n    'EEG C4-REF', 'EEG P3-REF', 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF',\n    'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', 'EEG T5-REF',\n    'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF'])\nle_ch_names = sorted([\n    'EEG A1-LE', 'EEG A2-LE',\n    'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE',\n    'EEG C4-LE', 'EEG P3-LE', 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE',\n    'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', 'EEG T5-LE',\n    'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE'])\nassert len(short_ch_names) == len(ar_ch_names) == len(le_ch_names)\nar_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    ar_ch_names, short_ch_names)}\nle_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(\n    le_ch_names, short_ch_names)}\nch_mapping = {'ar': ar_ch_mapping, 'le': le_ch_mapping}\n\n\ndef select_by_channels(ds, ch_mapping):\n    split_ids = []\n    for i, d in enumerate(ds.datasets):\n        ref = 'ar' if d.raw.ch_names[0].endswith('-REF') else 'le'\n        # these are the channels we are looking for\n        seta = set(ch_mapping[ref].keys())\n        # these are the channels of the recoding\n        setb = set(d.raw.ch_names)\n        # if recording contains all channels we are looking for, include it\n        if seta.issubset(setb):\n            split_ids.append(i)\n    return ds.split(split_ids)['0']\n\n\ntuh = select_by_channels(tuh, ch_mapping)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we will chain several preprocessing steps that are realized through\n`mne`. Data will be loaded by the first preprocessor that has a mention of it\nin brackets:\n\n#. crop the recordings to a region of interest\n#. re-reference all recordings to 'ar' (requires load)\n#. rename channels to short channel names\n#. pick channels of interest\n#. scale signals to micro volts (requires load)\n#. clip outlier values to +/- 800 micro volts (requires load)\n#. resample recordings to a common frequency (requires load)\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "def custom_rename_channels(raw, mapping):\n    # rename channels which are dependent on referencing:\n    # le: EEG 01-LE, ar: EEG 01-REF\n    # mne fails if the mapping contains channels as keys that are not present\n    # in the raw\n    reference = raw.ch_names[0].split('-')[-1].lower()\n    assert reference in ['le', 'ref'], 'unexpected referencing'\n    reference = 'le' if reference == 'le' else 'ar'\n    raw.rename_channels(mapping[reference])\n\n\ndef custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):\n    # crop recordings to tmin \u2013 tmax. can be incomplete if recording\n    # has lower duration than tmax\n    # by default mne fails if tmax is bigger than duration\n    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)\n    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)\n\n\ntmin = 1 * 60\ntmax = 6 * 60\nsfreq = 100\n\npreprocessors = [\n    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,\n                 apply_on_array=False),\n    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),\n    Preprocessor(custom_rename_channels, mapping=ch_mapping,\n                 apply_on_array=False),\n    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),\n    Preprocessor(multiply, factor=1e6, apply_on_array=True),\n    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),\n    Preprocessor('resample', sfreq=sfreq),\n]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, we apply the preprocessors on the selected recordings in parallel.\nWe additionally use the serialization functionality of\n:func:`braindecode.preprocessing.preprocess` to limit memory usage during\npreprocessing (as each file must be loaded into memory for some of the\npreprocessing steps to work). This also makes it possible to use the lazy\nloading capabilities of :class:`braindecode.datasets.BaseConcatDataset`, as\nthe preprocessed data is automatically reloaded with ``preload=False``.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Here we use ``n_jobs=2`` as the machines the documentation is build on\n   only have two cores. This number should be modified based on the machine\n   that is available for preprocessing.</p></div>\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "OUT_PATH = tempfile.mkdtemp()  # please insert actual output directory here\ntuh_preproc = preprocess(\n    concat_ds=tuh,\n    preprocessors=preprocessors,\n    n_jobs=N_JOBS,\n    save_dir=OUT_PATH\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can finally generate compute windows. The resulting dataset is now ready\nto be used for model training.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "window_size_samples = 1000\nwindow_stride_samples = 1000\n# generate compute windows here and store them to disk\ntuh_windows = create_fixed_length_windows(\n    tuh_preproc,\n    window_size_samples=window_size_samples,\n    window_stride_samples=window_stride_samples,\n    drop_last_window=False,\n    n_jobs=N_JOBS,\n)\n\nfor x, y, ind in tuh_windows:\n    break"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/d64670f59a0ee7e125ae228f318fb330/plot_hyperparameter_tuning_with_scikit-learn.ipynb b/stable/_downloads/d64670f59a0ee7e125ae228f318fb330/plot_hyperparameter_tuning_with_scikit-learn.ipynb
deleted file mode 100644
index f93cf8eb..00000000
--- a/stable/_downloads/d64670f59a0ee7e125ae228f318fb330/plot_hyperparameter_tuning_with_scikit-learn.ipynb
+++ /dev/null
@@ -1,225 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Hyperparameter tuning with scikit-learn\n\nThis tutorial shows you how to tune hyperparameters with scikit-learn\n(GridSearchCV) in the setting of trialwise decoding on dataset\nBCIC IV 2a.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Loading and preprocessing the dataset\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Loading\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "First, we load the data. In this tutorial, we use the functionality of\nbraindecode to load datasets through\n[MOABB](https://github.com/NeuroTechX/moabb)_ to load the BCI\nCompetition IV 2a data.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>To load your own datasets either via mne or from\n   preprocessed X/y numpy arrays, see [MNE Dataset\n   Tutorial](./plot_mne_dataset_example.html)_ and [Numpy Dataset\n   Tutorial](./plot_custom_dataset_example.html)_.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.datasets.moabb import MOABBDataset\n\nsubject_id = 3\ndataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[subject_id])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Preprocessing\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we apply preprocessing like bandpass filtering to our dataset. You\ncan either apply functions provided by\n[mne.Raw](https://mne.tools/stable/generated/mne.io.Raw.html)_ or\n[mne.Epochs](https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs)_\nor apply your own functions, either to the MNE object or the underlying\nnumpy array.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These prepocessings are now directly applied to the loaded\n   data, and not on-the-fly applied as transformations in\n   PyTorch-libraries like\n   [torchvision](https://pytorch.org/docs/stable/torchvision/index.html)_.</p></div>\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing.preprocess import (\n    exponential_moving_standardize, preprocess, Preprocessor)\nfrom numpy import multiply\n\nlow_cut_hz = 4.  # low cut frequency for filtering\nhigh_cut_hz = 38.  # high cut frequency for filtering\n# Parameters for exponential moving standardization\nfactor_new = 1e-3\ninit_block_size = 1000\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter\n    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization\n                 factor_new=factor_new, init_block_size=init_block_size)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Cut Compute Windows\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we cut out compute windows, the inputs for the deep networks during\ntraining. In the case of trialwise decoding, we just have to decide if\nwe want to cut out some part before and/or after the trial. For this\ndataset, in our work, it often was beneficial to also cut out 500 ms\nbefore the trial.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from braindecode.preprocessing.windowers import create_windows_from_events\n\ntrial_start_offset_seconds = -0.5\n# Extract sampling frequency, check that they are same in all datasets\nsfreq = dataset.datasets[0].raw.info['sfreq']\nassert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])\n# Calculate the trial start offset in samples.\ntrial_start_offset_samples = int(trial_start_offset_seconds * sfreq)\n\n# Create windows using braindecode function for this. It needs parameters to define how\n# trials should be used.\nwindows_dataset = create_windows_from_events(\n    dataset,\n    trial_start_offset_samples=trial_start_offset_samples,\n    trial_stop_offset_samples=0,\n    preload=True,\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split dataset into train and valid\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We can easily split the dataset using additional info stored in the\ndescription attribute, in this case ``session`` column. We select\n``session_T`` for training and ``session_E`` for evaluation.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "splitted = windows_dataset.split('session')\ntrain_set = splitted['session_T']\neval_set = splitted['session_E']"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create model\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we create the deep learning model! Braindecode comes with some\npredefined convolutional neural network architectures for raw\ntime-domain EEG. Here, we use the shallow ConvNet model from [Deep\nlearning with convolutional neural networks for EEG decoding and\nvisualization](https://arxiv.org/abs/1703.05051)_. These models are\npure [PyTorch](https://pytorch.org)_ deep learning models, therefore\nto use your own model, it just has to be a normal PyTorch\n[nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)_.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import torch\nfrom braindecode.util import set_random_seeds\nfrom braindecode.models import ShallowFBCSPNet\n\ncuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it\ndevice = 'cuda' if cuda else 'cpu'\nif cuda:\n    torch.backends.cudnn.benchmark = True\nseed = 20200220  # random seed to make results reproducible\n# Set random seed to be able to reproduce results\nset_random_seeds(seed=seed, cuda=cuda)\n\nn_classes = 4\n# Extract number of chans and time steps from dataset\nn_chans = train_set[0][0].shape[0]\ninput_window_samples = train_set[0][0].shape[1]\n\nmodel = ShallowFBCSPNet(\n    n_chans,\n    n_classes,\n    input_window_samples=input_window_samples,\n    final_conv_length='auto',\n)\n\n# Send model to GPU\nif cuda:\n    model.cuda()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Training\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now we train the network! EEGClassifier is a Braindecode object\nresponsible for managing the training of neural networks. It inherits\nfrom skorch.NeuralNetClassifier, so the training logic is the same as in\n[Skorch](https://skorch.readthedocs.io/en/stable/)_.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from skorch.callbacks import LRScheduler\n\nfrom braindecode import EEGClassifier\nbatch_size = 16\nn_epochs = 4\n\nclf = EEGClassifier(\n    model,\n    criterion=torch.nn.NLLLoss,\n    optimizer=torch.optim.AdamW,\n    optimizer__lr=[],\n    batch_size=batch_size,\n    train_split=None,  # train /test split is handled by GridSearchCV\n    callbacks=[\n        \"accuracy\",\n        (\"lr_scheduler\", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),\n    ],\n    device=device,\n)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use scikit-learn GridSearchCV to tune hyperparameters. To be able\nto do this, we slice the braindecode datasets that by default return\na 3-tuple to return X and y, respectively.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note**: The KFold object splits the datasets based on their\nlength which corresponds to the number of compute windows. In\nthis (trialwise) example this is fine to do. In a cropped setting\nthis is not advisable since this might split compute windows\nof a single trial into both train and valid set.\n\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import GridSearchCV, KFold\nfrom skorch.helper import SliceDataset\nfrom numpy import array\nimport pandas as pd\n\ntrain_X = SliceDataset(train_set, idx=0)\ntrain_y = array([y for y in SliceDataset(train_set, idx=1)])\ncv = KFold(n_splits=2, shuffle=True, random_state=42)\n\nfit_params = {'epochs': n_epochs}\nparam_grid = {\n    'optimizer__lr': [0.00625, 0.000625, 0.0000625],\n}\nsearch = GridSearchCV(\n    estimator=clf,\n    param_grid=param_grid,\n    cv=cv,\n    return_train_score=True,\n    scoring='accuracy',\n    refit=True,\n    verbose=1,\n    error_score='raise'\n)\n\nsearch.fit(train_X, train_y, **fit_params)\n\nsearch_results = pd.DataFrame(search.cv_results_)\n\nbest_run = search_results[search_results['rank_test_score'] == 1].squeeze()\nprint(f\"Best hyperparameters were {best_run['params']} which gave a validation \"\n      f\"accuracy of {best_run['mean_test_score']*100:.2f}% (training \"\n      f\"accuracy of {best_run['mean_train_score']*100:.2f}%).\")\n\neval_X = SliceDataset(eval_set, idx=0)\neval_y = SliceDataset(eval_set, idx=1)\nscore = search.score(eval_X, eval_y)\nprint(f\"Eval accuracy is {score*100:.2f}%.\")"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.12"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/stable/_downloads/d8042a52b5ef94f5d88f95acaeb05755/plot_regression.py b/stable/_downloads/d8042a52b5ef94f5d88f95acaeb05755/plot_regression.py
deleted file mode 100644
index e5759a59..00000000
--- a/stable/_downloads/d8042a52b5ef94f5d88f95acaeb05755/plot_regression.py
+++ /dev/null
@@ -1,140 +0,0 @@
-"""
-Regression example on fake data
-===============================
-"""
-
-# Authors: Lukas Gemein <l.gemein@gmail.com>
-#
-# License: BSD-3
-import numpy as np
-import pandas as pd
-import torch
-from skorch.callbacks import LRScheduler
-from skorch.helper import predefined_split
-
-from braindecode import EEGRegressor
-from braindecode.preprocessing import create_fixed_length_windows
-from braindecode.datasets import BaseDataset, BaseConcatDataset
-from braindecode.training.losses import CroppedLoss
-from braindecode.models import Deep4Net
-from braindecode.models import ShallowFBCSPNet
-from braindecode.models.util import to_dense_prediction_model, get_output_shape
-from braindecode.util import set_random_seeds, create_mne_dummy_raw
-
-model_name = "shallow"  # 'shallow' or 'deep'
-n_epochs = 3
-seed = 20200220
-
-input_window_samples = 6000
-batch_size = 64
-cuda = torch.cuda.is_available()
-device = 'cuda' if cuda else 'cpu'
-if cuda:
-    torch.backends.cudnn.benchmark = True
-
-n_chans = 21
-# set to how many targets you want to regress (age -> 1, [x, y, z] -> 3)
-n_classes = 1
-
-set_random_seeds(seed=seed, cuda=cuda)
-
-# initialize a model, transform to dense and move to gpu
-if model_name == "shallow":
-    model = ShallowFBCSPNet(
-        in_chans=n_chans,
-        n_classes=n_classes,
-        input_window_samples=input_window_samples,
-        n_filters_time=40,
-        n_filters_spat=40,
-        final_conv_length=35,
-    )
-    optimizer_lr = 0.000625
-    optimizer_weight_decay = 0
-elif model_name == "deep":
-    model = Deep4Net(
-        in_chans=n_chans,
-        n_classes=n_classes,
-        input_window_samples=input_window_samples,
-        n_filters_time=25,
-        n_filters_spat=25,
-        stride_before_pool=True,
-        n_filters_2=int(n_chans * 2),
-        n_filters_3=int(n_chans * (2 ** 2.0)),
-        n_filters_4=int(n_chans * (2 ** 3.0)),
-        final_conv_length=1,
-    )
-    optimizer_lr = 0.01
-    optimizer_weight_decay = 0.0005
-else:
-    raise ValueError(f'{model_name} unknown')
-
-new_model = torch.nn.Sequential()
-for name, module_ in model.named_children():
-    if "softmax" in name:
-        continue
-    new_model.add_module(name, module_)
-model = new_model
-
-if cuda:
-    model.cuda()
-
-to_dense_prediction_model(model)
-n_preds_per_input = get_output_shape(model, n_chans, input_window_samples)[2]
-
-
-def fake_regression_dataset(n_fake_recs, n_fake_chs, fake_sfreq, fake_duration_s):
-    datasets = []
-    for i in range(n_fake_recs):
-        train_or_eval = "eval" if i == 0 else "train"
-        raw, save_fname = create_mne_dummy_raw(
-            n_channels=n_fake_chs, n_times=fake_duration_s * fake_sfreq,
-            sfreq=fake_sfreq, savedir=None)
-        target = np.random.randint(0, 100, n_classes)
-        if n_classes == 1:
-            target = target[0]
-        fake_descrition = pd.Series(
-            data=[target, train_or_eval],
-            index=["target", "session"])
-        base_ds = BaseDataset(raw, fake_descrition, target_name="target")
-        datasets.append(base_ds)
-    dataset = BaseConcatDataset(datasets)
-    return dataset
-
-
-dataset = fake_regression_dataset(
-    n_fake_recs=5, n_fake_chs=21, fake_sfreq=100, fake_duration_s=60)
-
-windows_dataset = create_fixed_length_windows(
-    dataset,
-    start_offset_samples=0,
-    stop_offset_samples=0,
-    window_size_samples=input_window_samples,
-    window_stride_samples=n_preds_per_input,
-    drop_last_window=False,
-    drop_bad_windows=True,
-)
-
-splits = windows_dataset.split("session")
-train_set = splits["train"]
-valid_set = splits["eval"]
-
-regressor = EEGRegressor(
-    model,
-    cropped=True,
-    criterion=CroppedLoss,
-    criterion__loss_function=torch.nn.functional.mse_loss,
-    optimizer=torch.optim.AdamW,
-    train_split=predefined_split(valid_set),
-    optimizer__lr=optimizer_lr,
-    optimizer__weight_decay=optimizer_weight_decay,
-    iterator_train__shuffle=True,
-    batch_size=batch_size,
-    callbacks=[
-        "neg_root_mean_squared_error",
-        # seems n_epochs -1 leads to desired behavior of lr=0 after end of training?
-        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
-    ],
-    device=device,
-)
-
-regressor.fit(train_set, y=None, epochs=n_epochs)
diff --git a/stable/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py b/stable/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py
new file mode 100644
index 00000000..36da3656
--- /dev/null
+++ b/stable/_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py
@@ -0,0 +1,278 @@
+"""
+Data Augmentation on BCIC IV 2a Dataset
+=======================================
+
+This tutorial shows how to train EEG deep models with data augmentation. It
+follows the trial-wise decoding example and also illustrates the effect of a
+transform on the input signals.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Simon Brandt <simonbrandt@protonmail.com>
+#          Cédric Rommel <cedric.rommel@inria.fr>
+#
+# License: BSD (3-clause)
+
+######################################################################
+# Loading and preprocessing the dataset
+# -------------------------------------
+#
+# Loading
+# ~~~~~~~
+
+from skorch.helper import predefined_split
+from skorch.callbacks import LRScheduler
+
+from braindecode import EEGClassifier
+from braindecode.datasets import MOABBDataset
+
+subject_id = 3
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[subject_id])
+
+######################################################################
+# Preprocessing
+# ~~~~~~~~~~~~~
+#
+
+from braindecode.preprocessing import (
+    exponential_moving_standardize, preprocess, Preprocessor)
+from numpy import multiply
+
+low_cut_hz = 4.  # low cut frequency for filtering
+high_cut_hz = 38.  # high cut frequency for filtering
+# Parameters for exponential moving standardization
+factor_new = 1e-3
+init_block_size = 1000
+# Factor to convert from V to uV
+factor = 1e6
+
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=False),  # Keep EEG sensors
+    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor('filter', l_freq=low_cut_hz, h_freq=high_cut_hz),  # Bandpass filter
+    Preprocessor(exponential_moving_standardize,  # Exponential moving standardization
+                 factor_new=factor_new, init_block_size=init_block_size)
+]
+
+preprocess(dataset, preprocessors, n_jobs=-1)
+
+######################################################################
+# Extracting windows
+# ~~~~~~~~~~~~~~~~~~
+#
+
+from braindecode.preprocessing import create_windows_from_events
+
+trial_start_offset_seconds = -0.5
+# Extract sampling frequency, check that they are same in all datasets
+sfreq = dataset.datasets[0].raw.info['sfreq']
+assert all([ds.raw.info['sfreq'] == sfreq for ds in dataset.datasets])
+# Calculate the trial start offset in samples.
+trial_start_offset_samples = int(trial_start_offset_seconds * sfreq)
+
+# Create windows using braindecode function for this. It needs parameters to
+# define how trials should be used.
+windows_dataset = create_windows_from_events(
+    dataset,
+    trial_start_offset_samples=trial_start_offset_samples,
+    trial_stop_offset_samples=0,
+    preload=True,
+)
+
+######################################################################
+# Split dataset into train and valid
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+
+splitted = windows_dataset.split('session')
+train_set = splitted['0train']  # Session train
+valid_set = splitted['1test']  # Session evaluation
+######################################################################
+# Defining a Transform
+# --------------------
+#
+# Data can be manipulated by transforms, which are callable objects. A
+# transform is usually handled by a custom data loader, but can also be called
+# directly on input data, as demonstrated below for illutrative purposes.
+#
+# First, we need to define a Transform. Here we chose the FrequencyShift, which
+# randomly translates all frequencies within a given range.
+
+from braindecode.augmentation import FrequencyShift
+
+transform = FrequencyShift(
+    probability=1.,  # defines the probability of actually modifying the input
+    sfreq=sfreq,
+    max_delta_freq=2.  # the frequency shifts are sampled now between -2 and 2 Hz
+)
+
+######################################################################
+# Manipulating one session and visualizing the transformed data
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+#
+# Next, let us augment one session to show the resulting frequency shift. The
+# data of an mne Epoch is used here to make usage of mne functions.
+
+import torch
+import numpy as np
+
+X = np.stack([X for X, y, i in train_set.datasets[0]])
+# This allows to apply the transform with a fixed shift (10 Hz) for
+# visualization instead of sampling the shift randomly between -2 and 2 Hz
+X_tr, _ = transform.operation(torch.as_tensor(X).float(), None, 10., sfreq)
+
+######################################################################
+# The psd of the transformed session has now been shifted by 10 Hz, as one can
+# see on the psd plot.
+
+import mne
+import matplotlib.pyplot as plt
+
+
+def plot_psd(data, axis, label, color):
+    psds, freqs = mne.time_frequency.psd_array_multitaper(data, sfreq=sfreq,
+                                                          fmin=0.1, fmax=100)
+    psds = 10. * np.log10(psds)
+    psds_mean = psds.mean(0).mean(0)
+    axis.plot(freqs, psds_mean, color=color, label=label)
+
+
+_, ax = plt.subplots()
+plot_psd(X, ax, 'original', 'k')
+plot_psd(X_tr.numpy(), ax, 'shifted', 'r')
+
+ax.set(title='Multitaper PSD (gradiometers)', xlabel='Frequency (Hz)',
+       ylabel='Power Spectral Density (dB)')
+ax.legend()
+plt.show()
+
+######################################################################
+# Training a model with data augmentation
+# ---------------------------------------
+#
+# Now that we know how to instantiate ``Transforms``, it is time to learn how
+# to use them to train a model and try to improve its generalization power.
+# Let's first create a model.
+#
+# Create model
+# ~~~~~~~~~~~~
+#
+# The model to be trained is defined as usual.
+
+from braindecode.util import set_random_seeds
+from braindecode.models import ShallowFBCSPNet
+
+cuda = torch.cuda.is_available()  # check if GPU is available, if True chooses to use it
+device = 'cuda' if cuda else 'cpu'
+if cuda:
+    torch.backends.cudnn.benchmark = True
+
+# Set random seed to be able to roughly reproduce results
+# Note that with cudnn benchmark set to True, GPU indeterminism
+# may still make results substantially different between runs.
+# To obtain more consistent results at the cost of increased computation time,
+# you can set `cudnn_benchmark=False` in `set_random_seeds`
+# or remove `torch.backends.cudnn.benchmark = True`
+seed = 20200220
+set_random_seeds(seed=seed, cuda=cuda)
+
+n_classes = 4
+classes = list(range(n_classes))
+
+# Extract number of chans and time steps from dataset
+n_channels = train_set[0][0].shape[0]
+input_window_samples = train_set[0][0].shape[1]
+
+model = ShallowFBCSPNet(
+    n_channels,
+    n_classes,
+    input_window_samples=input_window_samples,
+    final_conv_length='auto',
+)
+
+######################################################################
+# Create an EEGClassifier with the desired augmentation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# In order to train with data augmentation, a custom data loader can be
+# for the training. Multiple transforms can be passed to it and will be applied
+# sequentially to the batched data within the ``AugmentedDataLoader`` object.
+
+from braindecode.augmentation import AugmentedDataLoader, SignFlip
+
+freq_shift = FrequencyShift(
+    probability=.5,
+    sfreq=sfreq,
+    max_delta_freq=2.  # the frequency shifts are sampled now between -2 and 2 Hz
+)
+
+sign_flip = SignFlip(probability=.1)
+
+transforms = [
+    freq_shift,
+    sign_flip
+]
+
+# Send model to GPU
+if cuda:
+    model.cuda()
+
+######################################################################
+# The model is now trained as in the trial-wise example. The
+# ``AugmentedDataLoader`` is used as the train iterator and the list of
+# transforms are passed as arguments.
+
+lr = 0.0625 * 0.01
+weight_decay = 0
+
+batch_size = 64
+n_epochs = 4
+
+clf = EEGClassifier(
+    model,
+    iterator_train=AugmentedDataLoader,  # This tells EEGClassifier to use a custom DataLoader
+    iterator_train__transforms=transforms,  # This sets the augmentations to use
+    criterion=torch.nn.NLLLoss,
+    optimizer=torch.optim.AdamW,
+    train_split=predefined_split(valid_set),  # using valid_set for validation
+    optimizer__lr=lr,
+    optimizer__weight_decay=weight_decay,
+    batch_size=batch_size,
+    callbacks=[
+        "accuracy",
+        ("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
+    ],
+    device=device,
+    classes=classes,
+)
+# Model training for a specified number of epochs. `y` is None as it is already
+# supplied in the dataset.
+clf.fit(train_set, y=None, epochs=n_epochs)
+
+######################################################################
+# Manually composing Transforms
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# It would be equivalent (although more verbose) to pass to ``EEGClassifier`` a
+# composition of the same transforms:
+
+from braindecode.augmentation import Compose
+
+composed_transforms = Compose(transforms=transforms)
+
+######################################################################
+# Setting the data augmentation at the Dataset level
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Also note that it is also possible for most of the transforms to pass them
+# directly to the WindowsDataset object through the `transform` argument, as
+# most commonly done in other libraries. However, it is advised to use the
+# ``AugmentedDataLoader`` as above, as it is compatible with all transforms and
+# can be more efficient.
+
+train_set.transform = composed_transforms
diff --git a/stable/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py b/stable/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py
new file mode 100644
index 00000000..9f53c631
--- /dev/null
+++ b/stable/_downloads/e619a6cc8a167ef21aec010c22e3d8c3/plot_basic_training_epochs.py
@@ -0,0 +1,177 @@
+"""
+Simple training on MNE epochs
+=============================
+
+The braindecode library gives you access to a large number of neural network
+architectures that were developed for EEG data decoding. This tutorial will
+show you how you can easily use any of these models to decode your own data.
+In particular, we assume that have your data in an MNE format and want to
+train one of the Braindecode models on it.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+"""
+
+# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>
+#
+# License: BSD (3-clause)
+
+######################################################################
+# Finding the model you want
+# --------------------------
+#
+# Exploring the braindecode online documentation
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Let's suppose you recently stumbled upon the Schirrmeister 2017 article [1]_.
+# In this article, the authors mention that their novel architecture ShallowConvNet
+# is performing well on the BCI Competition IV 2a dataset and you would like to use
+# it on your own data. Fortunately, the authors also mentioned they published their
+# architecture on Braindecode!
+#
+# In order to use this architecture, you first need to find what is its exact
+# name in Braindecode. To do so, you can visit the Braindecode online documentation
+# which lists all the available models.
+#
+# Models list: https://braindecode.org/stable/api.html#models
+#
+# Alternatively, the API also provide a dictionary with all available models:
+
+from braindecode.models.util import models_dict
+
+print(f'All the Braindecode models:\n{list(models_dict.keys())}')
+
+######################################################################
+# After your investigation, you found out that the model you are looking for is
+# ``ShallowFBCSPNet``. You can now import it from Braindecode:
+
+from braindecode.models import ShallowFBCSPNet
+
+######################################################################
+# Examining the model
+# ~~~~~~~~~~~~~~~~~~~
+#
+# Now that you found your model, you must check which parameters it expects.
+# You can find this information either in the online documentation here:
+# :class:`braindecode.models.ShallowFBCSPNet` or directly in the module's docstring:
+
+print(ShallowFBCSPNet.__doc__)
+
+######################################################################
+# Additionally, you might be interested in visualizing the model's architecture.
+# This can be done by initializing the model and calling its ``__str__()`` method.
+# To initialize it, we need to specify some parameters that we set at random
+# values for now:
+
+model = ShallowFBCSPNet(
+    n_chans=32,
+    n_times=1000,
+    n_outputs=2,
+    final_conv_length='auto',
+)
+print(model)
+
+######################################################################
+# Loading your own data with MNE
+# ------------------------------
+#
+# In this tutorial, we demonstrate how to train the model on MNE data.
+# MNE is quite a popular library for EEG data analysis as it provides methods
+# to load data from many different file formats and a large collection of algorithms
+# to preprocess it.
+# However, Braindecode is not limited to MNE and can be used with numpy arrays or
+# PyTorch tensors/datasets.
+#
+# For this example, we generate some random data containing 100 examples with each
+# 3 channels and 1024 time points. We also generate some random labels for our data
+# that simulate a 4-class classification problem.
+
+import mne
+import numpy as np
+
+info = mne.create_info(ch_names=['C3', 'C4', 'Cz'], sfreq=256., ch_types='eeg')
+X = np.random.randn(100, 3, 1024)  # 100 epochs, 3 channels, 4 seconds (@256Hz)
+epochs = mne.EpochsArray(X, info=info)
+y = np.random.randint(0, 4, size=100)  # 4 classes
+print(epochs)
+
+######################################################################
+# Training your model (scikit-learn compatible)
+# ---------------------------------------------
+#
+# Now that you know which model you want to use, you know how to instantiate it,
+# and that we have some fake data, it is time to train the model!
+#
+# .. note::
+#    `Skorch <https://skorch.readthedocs.io>`_  is a library that allows you to wrap
+#    any PyTorch module into a scikit-learn-compatible classifier or regressor.
+#    Braindecode provides wrappers that inherit form the original Skorch ones and simply
+#    implement a few additional features that facilitate the use of Braindecode models.
+#
+# To train a Braindecode model, the easiest way is by using braindecode's
+# Skorch wrappers. These wrappers are :class:`braindecode.EEGClassifier` and
+# :class:`braindecode.EEGRegressor`. As our fake data is a classification task,
+# we will use the former.
+#
+# The wrapper :class:`braindecode.EEGClassifier` expects a model class as its first argument but
+# to facilitate the usage, you can also simply pass the name of any braindecode model as a string.
+# The wrapper automatically finds and instantiates the model for you.
+# If you want to pass parameters to your model, you can give them to the wrapper
+# with the prefix ``module__``.
+#
+from skorch.dataset import ValidSplit
+from braindecode import EEGClassifier
+
+net = EEGClassifier(
+    'ShallowFBCSPNet',
+    module__final_conv_length='auto',
+    train_split=ValidSplit(0.2),
+    # To train a neural network you need validation split, here, we use 20%.
+)
+
+######################################################################
+# In this example, we passed one additional parameter to the wrapper: ``module__final_conv_length``
+# that will be forwarded to the model (without the prefix ``module__``).
+#
+# We also note that the parameters ``n_chans``, ``n_times`` and ``n_outputs`` were not specified
+# even if :class:`braindecode.ShallowFBCSPNet` needs them to be initialized. This is because the
+# wrapper will automatically infer them, along with some other signal-related parameters,
+# from the input data at training time.
+#
+# Now that we have our model wrapped in a scikit-learn-compatible classifier,
+# we can train it by simply calling the ``fit`` method:
+
+net.fit(epochs, y)
+
+######################################################################
+# The pre-trained model is accessible via the ``module_`` attribute:
+
+print(net.module_)
+
+######################################################################
+# And we can see that all the following parameters were automatically inferred
+# from the training data:
+
+print(f'{net.module_.n_chans=}\n{net.module_.n_times=}\n{net.module_.n_outputs=}'
+      f'\n{net.module_.input_window_seconds=}\n{net.module_.sfreq=}\n{net.module_.chs_info=}')
+
+######################################################################
+# Depending on the type of data used for training, some parameters might not be
+# possible to infer. For example if you pass a numpy array or a
+# :class:`braindecode.dataset.WindowsDataset` with ``target_from="metadata"``,
+#  then only ``n_chans``, ``n_times`` and ``n_outputs`` will be inferred.
+# And if you pass other types of datasets, only ``n_chans`` and ``n_times`` will be inferred.
+# In these case, you will have to pass the missing parameters manually
+# (with the prefix ``module__``).
+
+######################################################################
+# References
+# ----------
+#
+# .. [1] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter,
+#        M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T.(2017).
+#        Deep learning with convolutional neural networks for EEG decoding and visualization.
+#        Human Brain Mapping, Aug. 2017.
+#        Online: http://dx.doi.org/10.1002/hbm.23730
diff --git a/stable/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py b/stable/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py
new file mode 100644
index 00000000..ad6daf06
--- /dev/null
+++ b/stable/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py
@@ -0,0 +1,299 @@
+"""
+Process a big data EEG resource (TUH EEG Corpus)
+================================================
+
+In this example, we showcase usage of the Temple University Hospital EEG Corpus
+(https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg)
+including simple preprocessing steps as well as cutting of compute windows.
+
+.. contents:: This example covers:
+   :local:
+   :depth: 2
+
+
+"""
+
+# Author: Lukas Gemein <l.gemein@gmail.com>
+#
+# License: BSD (3-clause)
+
+import tempfile
+
+import numpy as np
+import matplotlib.pyplot as plt
+import mne
+
+from braindecode.datasets import TUH
+from braindecode.preprocessing import (
+    preprocess, Preprocessor, create_fixed_length_windows)
+from numpy import multiply
+
+mne.set_log_level('ERROR')  # avoid messages every time a window is extracted
+
+###############################################################################
+# Creating the dataset using TUHMock
+# -------------------------------------
+#
+# Since the data is not available at the time of the creation of this example,
+# we are required to mock some of the dataset functionality. Therefore, if you
+# want to try this code with the actual data, please disconsider this section.
+
+from braindecode.datasets.tuh import _TUHMock as TUH  # noqa F811
+
+###############################################################################
+# Firstly, we start by creating a TUH mock dataset using braindecode's _TUHMock class.
+# The complete code can be found at :func:`braindecode.datasets.TUH`, but we will give
+# a small description of how it works.
+# This class is able to read the recordings from TUH_PATH and generate a description
+# by parsing information from file paths, such as patient id and recording data.
+# THis description can later be accessed by the object's .description method.
+# After that, the files are sorted chronologically by year, month, day,
+# patient id, recording session and segment, and then use the description corresponding
+# to the specified by recording ids.
+# FInally, additional information regarding age and gender of the subjects are parsed
+# directly to the description.
+
+TUH_PATH = 'please insert actual path to data here'
+# specify the number of jobs for loading and windowing
+N_JOBS = 2
+tuh = TUH(
+    path=TUH_PATH,
+    recording_ids=None,
+    target_name=None,
+    preload=False,
+    add_physician_reports=False,
+    n_jobs=1 if TUH.__name__ == '_TUHMock' else N_JOBS
+)
+
+
+###############################################################################
+# We can visualize our data's statistics using the class' "description" method
+
+def plt_histogram(df_of_ages_genders, alpha=0.5, fs=24, ylim=1.5, show_title=True):
+    # Dafarame containing info about gender and age of subjects
+    df = df_of_ages_genders
+    male_df = df[df["gender"] == 'M']
+    female_df = df[df["gender"] == 'F']
+
+    plt.figure(figsize=(15, 18))
+    if show_title:
+        plt.suptitle("Age information", y=0.95, fontsize=fs + 5)
+
+    # First plot: Male individuals
+    plt.subplot(121)
+    plt.hist(male_df["age"], bins=np.linspace(0, 100, 101),
+             alpha=alpha, color="green", orientation="horizontal")
+    plt.axhline(np.mean(male_df["age"]), color="black",
+                label=f"mean age {np.mean(male_df['age']):.1f} "
+                      f"(±{np.std(male_df['age']):.1f})")
+    plt.barh(np.mean(male_df["age"]), height=2 * np.std(male_df["age"]),
+             width=ylim, color="black", alpha=0.25)
+
+    # Legend
+    plt.xlim(0, ylim)
+    plt.legend(fontsize=fs, loc="upper left")
+    plt.title(f"male ({100 * len(male_df) / len(df):.1f}%)",
+              fontsize=fs, loc="left", y=1, x=0.05)
+    plt.yticks(color='w')
+    plt.gca().invert_xaxis()
+    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)
+    plt.tick_params(labelsize=fs - 5)
+
+    # First plot: Female individuals
+    plt.subplot(122)
+    plt.hist(female_df["age"], bins=np.linspace(0, 100, 101),
+             alpha=alpha, color="orange", orientation="horizontal")
+    plt.axhline(np.mean(female_df["age"]), color="black", linestyle="--",
+                label=f"mean age {np.mean(female_df['age']):.1f} ("
+                      f"±{np.std(female_df['age']):.1f})")
+    plt.barh(np.mean(female_df["age"]), height=2 * np.std(female_df["age"]),
+             width=ylim, color="black", alpha=0.25)
+
+    # Label
+    plt.legend(fontsize=fs, loc="upper right")
+    plt.xlim(0, ylim)
+    plt.title(f"female ({100 * len(female_df) / len(df):.1f}%)",
+              fontsize=fs, loc="right", y=1, x=0.95)
+    plt.ylim(0, 100)
+    plt.subplots_adjust(wspace=0, hspace=0)
+    plt.ylabel("age [years]", fontsize=fs)
+    plt.xlabel("count", fontsize=fs, x=1, labelpad=20)
+    plt.yticks(np.linspace(0, 100, 11), fontsize=fs - 5)
+    plt.tick_params(labelsize=fs - 5)
+
+    plt.show()
+
+
+df = tuh.description
+plt_histogram(df)
+
+
+###############################################################################
+# Preprocessing
+# -------------------------------------
+#
+# Selecting recordings
+# ~~~~~~~~~~~~~
+#
+# First, we will do some selection of available recordings based on the duration.
+# We will select those recordings that have at least five minutes duration.
+#
+
+def select_by_duration(ds, tmin=0, tmax=None):
+    if tmax is None:
+        tmax = np.inf
+    # determine length of the recordings and select based on tmin and tmax
+    split_ids = []
+    for d_i, d in enumerate(ds.datasets):
+        duration = d.raw.n_times / d.raw.info['sfreq']
+        # select the ones in the required duration range
+        if tmin <= duration <= tmax:
+            split_ids.append(d_i)
+    splits = ds.split(split_ids)
+    split = splits['0']
+    return split
+
+
+tmin = 5 * 60
+tmax = None
+tuh = select_by_duration(tuh, tmin, tmax)
+
+###############################################################################
+# Next, we will discard all recordings that have an incomplete channel
+# configuration on the channels that we are interested. The subdivisions of the
+# recordings into 'le' and 'ar' labels represents the channels for
+# the re-referencing of the signals.
+
+short_ch_names = sorted([
+    'A1', 'A2',
+    'FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2',
+    'F7', 'F8', 'T3', 'T4', 'T5', 'T6', 'FZ', 'CZ', 'PZ'])
+
+# TUH data is subdivided into 'le' and 'ar' recordings references
+ar_ch_names = sorted([
+    'EEG A1-REF', 'EEG A2-REF',
+    'EEG FP1-REF', 'EEG FP2-REF', 'EEG F3-REF', 'EEG F4-REF', 'EEG C3-REF',
+    'EEG C4-REF', 'EEG P3-REF', 'EEG P4-REF', 'EEG O1-REF', 'EEG O2-REF',
+    'EEG F7-REF', 'EEG F8-REF', 'EEG T3-REF', 'EEG T4-REF', 'EEG T5-REF',
+    'EEG T6-REF', 'EEG FZ-REF', 'EEG CZ-REF', 'EEG PZ-REF'])
+le_ch_names = sorted([
+    'EEG A1-LE', 'EEG A2-LE',
+    'EEG FP1-LE', 'EEG FP2-LE', 'EEG F3-LE', 'EEG F4-LE', 'EEG C3-LE',
+    'EEG C4-LE', 'EEG P3-LE', 'EEG P4-LE', 'EEG O1-LE', 'EEG O2-LE',
+    'EEG F7-LE', 'EEG F8-LE', 'EEG T3-LE', 'EEG T4-LE', 'EEG T5-LE',
+    'EEG T6-LE', 'EEG FZ-LE', 'EEG CZ-LE', 'EEG PZ-LE'])
+assert len(short_ch_names) == len(ar_ch_names) == len(le_ch_names)
+ar_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(
+    ar_ch_names, short_ch_names)}
+le_ch_mapping = {ch_name: short_ch_name for ch_name, short_ch_name in zip(
+    le_ch_names, short_ch_names)}
+ch_mapping = {'ar': ar_ch_mapping, 'le': le_ch_mapping}
+
+
+def select_by_channels(ds, ch_mapping):
+    split_ids = []
+    for i, d in enumerate(ds.datasets):
+        ref = 'ar' if d.raw.ch_names[0].endswith('-REF') else 'le'
+        # these are the channels we are looking for
+        seta = set(ch_mapping[ref].keys())
+        # these are the channels of the recoding
+        setb = set(d.raw.ch_names)
+        # if recording contains all channels we are looking for, include it
+        if seta.issubset(setb):
+            split_ids.append(i)
+    return ds.split(split_ids)['0']
+
+
+tuh = select_by_channels(tuh, ch_mapping)
+
+
+###############################################################################
+# Combining preprocessing steps
+# ~~~~~~~~~~~~~
+#
+# Next, we use braindecode's preprocess to combine and execute several preprocessing
+# steps that are executed through 'mne':
+#
+# - Crop the recordings to a region of interest
+# - Re-reference all recordings to 'ar' (requires load)
+# - Rename channels to short channel names
+# - Pick channels of interest
+# - Scale signals to micro volts (requires load)
+# - Clip outlier values to +/- 800 micro volts (requires load)
+# - Resample recordings to a common frequency (requires load)
+
+def custom_rename_channels(raw, mapping):
+    # rename channels which are dependent on referencing:
+    # le: EEG 01-LE, ar: EEG 01-REF
+    # mne fails if the mapping contains channels as keys that are not present
+    # in the raw
+    reference = raw.ch_names[0].split('-')[-1].lower()
+    assert reference in ['le', 'ref'], 'unexpected referencing'
+    reference = 'le' if reference == 'le' else 'ar'
+    raw.rename_channels(mapping[reference])
+
+
+def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
+    # crop recordings to tmin – tmax. can be incomplete if recording
+    # has lower duration than tmax
+    # by default mne fails if tmax is bigger than duration
+    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)
+    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)
+
+
+tmin = 1 * 60
+tmax = 6 * 60
+sfreq = 100
+factor = 1e6
+
+preprocessors = [
+    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,
+                 apply_on_array=False),
+    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),
+    Preprocessor(custom_rename_channels, mapping=ch_mapping,
+                 apply_on_array=False),
+    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
+    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),
+    Preprocessor('resample', sfreq=sfreq),
+]
+
+###############################################################################
+# Next, we can apply the defined preprocessors on the selected recordings in parallel.
+# We additionally use the serialization functionality of
+# :func:`braindecode.preprocessing.preprocess` to limit memory usage during
+# preprocessing, as each file must be loaded into memory for some of the
+# preprocessing steps to work.
+# This also makes it possible to use the lazy
+# loading capabilities of :class:`braindecode.datasets.BaseConcatDataset`, as
+# the preprocessed data is automatically reloaded with ``preload=False``.
+#
+# .. note::
+#    Here we use ``n_jobs=2`` as the machines the documentation is build on
+#    only have two cores. This number should be modified based on the machine
+#    that is available for preprocessing.
+
+OUT_PATH = tempfile.mkdtemp()  # please insert actual output directory here
+tuh_preproc = preprocess(
+    concat_ds=tuh,
+    preprocessors=preprocessors,
+    n_jobs=N_JOBS,
+    save_dir=OUT_PATH
+)
+
+###############################################################################
+# Cut Compute Windows
+# ~~~~~~~~~~~~~
+# We can finally generate compute windows. The resulting dataset is now ready
+# to be used for model training.
+
+window_size_samples = 1000
+window_stride_samples = 1000
+# Generate compute windows here and store them to disk
+tuh_windows = create_fixed_length_windows(
+    tuh_preproc,
+    window_size_samples=window_size_samples,
+    window_stride_samples=window_stride_samples,
+    drop_last_window=False,
+    n_jobs=N_JOBS,
+)
diff --git a/stable/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb b/stable/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb
new file mode 100644
index 00000000..86fb1fbb
--- /dev/null
+++ b/stable/_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb
@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Split Dataset Example\n\nIn this example, we aim to show multiple ways of how you can split your datasets for\ntraining, testing, and evaluating your models.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom braindecode.datasets import MOABBDataset\nfrom braindecode.preprocessing import create_windows_from_events"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading the dataset\n\nFirstly, we create a dataset using the braindecode class <MOABBDataset> to load\nit fetched from MOABB. In this example, we're using Dataset 2a from BCI\nCompetition IV.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset = MOABBDataset(dataset_name=\"BNCI2014001\", subject_ids=[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Splitting\n\n### By description information\n\nThe class <MOABBDataset> has a pandas DataFrame containing additional\ndescription of its internal datasets, which can be used to help splitting the data\nbased on recording information, such as subject, session, and run of each trial.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "dataset.description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here, we're splitting the data based on different runs. The method split returns\na dictionary with string keys corresponding to unique entries in the description\nDataFrame column.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splits = dataset.split(\"run\")\nprint(splits)\nsplits[\"4\"].description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### By row index\n\nAnother way we can split the dataset is based on a list of integers corresponding to\nrows in the description. In this case, the returned dictionary will have\n'0' as the only key.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splits = dataset.split([0, 1, 5])\nprint(splits)\nsplits[\"0\"].description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "However, if we want multiple splits based on indices, we can also define a list\ncontaining lists of integers. In this case, the dictionary will have string keys\nrepresenting the index of the dataset split in the order of the given list of\nintegers.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splits = dataset.split([[0, 1, 5], [2, 3, 4], [6, 7, 8, 9, 10, 11]])\nprint(splits)\nsplits[\"2\"].description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can also name each split in the output dictionary by specifying the keys\nof each list of indexes in the input dictionary:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "splits = dataset.split(\n    {\"train\": [0, 1, 5], \"valid\": [2, 3, 4], \"test\": [6, 7, 8, 9, 10, 11]}\n)\nprint(splits)\nsplits[\"test\"].description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Observation\n\nSimilarly, we can split datasets after creating windows using the same methods.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows = create_windows_from_events(\n    dataset, trial_start_offset_samples=0, trial_stop_offset_samples=0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Splitting by different runs\nprint(\"Using description info\")\nsplits = windows.split(\"run\")\nprint(splits)\nprint()\n\n# Splitting by row index\nprint(\"Splitting by row index\")\nsplits = windows.split([4, 8])\nprint(splits)\nprint()\n\nprint(\"Multiple row index split\")\nsplits = windows.split([[4, 8], [5, 9, 11]])\nprint(splits)\nprint()\n\n# Specifying output's keys\nprint(\"Specifying keys\")\nsplits = windows.split(dict(train=[4, 8], test=[5, 9, 11]))\nprint(splits)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb b/stable/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb
new file mode 100644
index 00000000..1176a69e
--- /dev/null
+++ b/stable/_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb
@@ -0,0 +1,133 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Custom Dataset Example\n\nThis example shows how to convert data X and y as numpy arrays to a braindecode\ncompatible data format.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Lukas Gemein <l.gemein@gmail.com>\n#\n# License: BSD (3-clause)\n\nimport mne\n\nfrom braindecode.datasets import create_from_X_y"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "To set up the example, we first fetch some data using mne:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet\nsubject_id = 22\nevent_codes = [5, 6, 9, 10, 13, 14]\n# event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n\n# This will download the files if you don't have them yet,\n# and then return the paths to the files.\nphysionet_paths = mne.datasets.eegbci.load_data(\n    subject_id, event_codes, update_path=False)\n\n# Load each of the files\nparts = [mne.io.read_raw_edf(path, preload=True, stim_channel='auto')\n         for path in physionet_paths]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "We take the required data, targets and additional information sampling\nfrequency and channel names from the loaded data. Note that this data and\ninformation can originate from any source.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X = [raw.get_data() for raw in parts]\ny = event_codes\nsfreq = parts[0].info[\"sfreq\"]\nch_names = parts[0].info[\"ch_names\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Convert to data format compatible with skorch and braindecode:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "windows_dataset = create_from_X_y(\n    X, y, drop_last_window=False, sfreq=sfreq, ch_names=ch_names,\n    window_stride_samples=500,\n    window_size_samples=500,\n)\n\nwindows_dataset.description  # look as dataset description"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can manipulate the dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(len(windows_dataset))  # get the number of samples"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now index the data\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "i = 0\nx_i, y_i, window_ind = windows_dataset[0]\nn_channels, n_times = x_i.shape  # the EEG data\n_, start_ind, stop_ind = window_ind\nprint(f\"n_channels={n_channels}  -- n_times={n_times} -- y_i={y_i}\")\nprint(f\"start_ind={start_ind} -- stop_ind={stop_ind}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py b/stable/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py
new file mode 100644
index 00000000..3d148717
--- /dev/null
+++ b/stable/_downloads/fea70b19933793ddf91e286b398628af/plot_moabb_dataset_example.py
@@ -0,0 +1,54 @@
+"""MOABB Dataset Example
+========================
+
+In this example, we show how to fetch and prepare a MOABB dataset for usage
+with Braindecode.
+"""
+
+# Authors: Lukas Gemein <l.gemein@gmail.com>
+#          Hubert Banville <hubert.jbanville@gmail.com>
+#          Simon Brandt <simonbrandt@protonmail.com>
+#          Daniel Wilson <dan.c.wil@gmail.com>
+#
+# License: BSD (3-clause)
+
+from braindecode.datasets import MOABBDataset
+from braindecode.preprocessing import preprocess, Preprocessor
+
+###############################################################################
+# First, we create a dataset based on BCIC IV 2a fetched with MOABB,
+dataset = MOABBDataset(dataset_name="BNCI2014001", subject_ids=[1])
+
+###############################################################################
+# The dataset has a pandas DataFrame with additional description of its internal datasets
+dataset.description
+
+##############################################################################
+# We can iterate through dataset which yields one time point of a continuous signal x,
+# and a target y (which can be None if targets are not defined for the entire
+# continuous signal).
+for x, y in dataset:
+    print(x.shape, y)
+    break
+
+##############################################################################
+# We can apply preprocessing transforms that are defined in mne and work
+# in-place, such as resampling, bandpass filtering, or electrode selection.
+preprocessors = [
+    Preprocessor('pick_types', eeg=True, meg=False, stim=True),
+    Preprocessor('resample', sfreq=100)
+]
+print(dataset.datasets[0].raw.info["sfreq"])
+preprocess(dataset, preprocessors)
+print(dataset.datasets[0].raw.info["sfreq"])
+
+###############################################################################
+# We can easily split the dataset based on a criteria applied to the description
+# DataFrame:
+subsets = dataset.split("session")
+print({subset_name: len(subset) for subset_name, subset in subsets.items()})
+
+##############################################################################
+# See our `Trialwise Decoding <../model_building/plot_bcic_iv_2a_moabb_trial.html>`__ and
+# `Cropped Decoding <../model_building/plot_bcic_iv_2a_moabb_cropped.html>`__ examples for
+# training with this dataset.
diff --git a/stable/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb b/stable/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb
new file mode 100644
index 00000000..9ab30f6d
--- /dev/null
+++ b/stable/_downloads/fff46913db5173d3ae22c1113acffb45/plot_basic_training_epochs.ipynb
@@ -0,0 +1,219 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Simple training on MNE epochs\n\nThe braindecode library gives you access to a large number of neural network\narchitectures that were developed for EEG data decoding. This tutorial will\nshow you how you can easily use any of these models to decode your own data.\nIn particular, we assume that have your data in an MNE format and want to\ntrain one of the Braindecode models on it.\n   :depth: 2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Authors: Pierre Guetschel <pierre.guetschel@gmail.com>\n#\n# License: BSD (3-clause)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Finding the model you want\n\n### Exploring the braindecode online documentation\n\nLet's suppose you recently stumbled upon the Schirrmeister 2017 article [1]_.\nIn this article, the authors mention that their novel architecture ShallowConvNet\nis performing well on the BCI Competition IV 2a dataset and you would like to use\nit on your own data. Fortunately, the authors also mentioned they published their\narchitecture on Braindecode!\n\nIn order to use this architecture, you first need to find what is its exact\nname in Braindecode. To do so, you can visit the Braindecode online documentation\nwhich lists all the available models.\n\nModels list: https://braindecode.org/stable/api.html#models\n\nAlternatively, the API also provide a dictionary with all available models:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models.util import models_dict\n\nprint(f'All the Braindecode models:\\n{list(models_dict.keys())}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After your investigation, you found out that the model you are looking for is\n``ShallowFBCSPNet``. You can now import it from Braindecode:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from braindecode.models import ShallowFBCSPNet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Examining the model\n\nNow that you found your model, you must check which parameters it expects.\nYou can find this information either in the online documentation here:\n:class:`braindecode.models.ShallowFBCSPNet` or directly in the module's docstring:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(ShallowFBCSPNet.__doc__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Additionally, you might be interested in visualizing the model's architecture.\nThis can be done by initializing the model and calling its ``__str__()`` method.\nTo initialize it, we need to specify some parameters that we set at random\nvalues for now:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model = ShallowFBCSPNet(\n    n_chans=32,\n    n_times=1000,\n    n_outputs=2,\n    final_conv_length='auto',\n)\nprint(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Loading your own data with MNE\n\nIn this tutorial, we demonstrate how to train the model on MNE data.\nMNE is quite a popular library for EEG data analysis as it provides methods\nto load data from many different file formats and a large collection of algorithms\nto preprocess it.\nHowever, Braindecode is not limited to MNE and can be used with numpy arrays or\nPyTorch tensors/datasets.\n\nFor this example, we generate some random data containing 100 examples with each\n3 channels and 1024 time points. We also generate some random labels for our data\nthat simulate a 4-class classification problem.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import mne\nimport numpy as np\n\ninfo = mne.create_info(ch_names=['C3', 'C4', 'Cz'], sfreq=256., ch_types='eeg')\nX = np.random.randn(100, 3, 1024)  # 100 epochs, 3 channels, 4 seconds (@256Hz)\nepochs = mne.EpochsArray(X, info=info)\ny = np.random.randint(0, 4, size=100)  # 4 classes\nprint(epochs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Training your model (scikit-learn compatible)\n\nNow that you know which model you want to use, you know how to instantiate it,\nand that we have some fake data, it is time to train the model!\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>[Skorch](https://skorch.readthedocs.io)  is a library that allows you to wrap\n   any PyTorch module into a scikit-learn-compatible classifier or regressor.\n   Braindecode provides wrappers that inherit form the original Skorch ones and simply\n   implement a few additional features that facilitate the use of Braindecode models.</p></div>\n\nTo train a Braindecode model, the easiest way is by using braindecode's\nSkorch wrappers. These wrappers are :class:`braindecode.EEGClassifier` and\n:class:`braindecode.EEGRegressor`. As our fake data is a classification task,\nwe will use the former.\n\nThe wrapper :class:`braindecode.EEGClassifier` expects a model class as its first argument but\nto facilitate the usage, you can also simply pass the name of any braindecode model as a string.\nThe wrapper automatically finds and instantiates the model for you.\nIf you want to pass parameters to your model, you can give them to the wrapper\nwith the prefix ``module__``.\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from skorch.dataset import ValidSplit\nfrom braindecode import EEGClassifier\n\nnet = EEGClassifier(\n    'ShallowFBCSPNet',\n    module__final_conv_length='auto',\n    train_split=ValidSplit(0.2),\n    # To train a neural network you need validation split, here, we use 20%.\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "In this example, we passed one additional parameter to the wrapper: ``module__final_conv_length``\nthat will be forwarded to the model (without the prefix ``module__``).\n\nWe also note that the parameters ``n_chans``, ``n_times`` and ``n_outputs`` were not specified\neven if :class:`braindecode.ShallowFBCSPNet` needs them to be initialized. This is because the\nwrapper will automatically infer them, along with some other signal-related parameters,\nfrom the input data at training time.\n\nNow that we have our model wrapped in a scikit-learn-compatible classifier,\nwe can train it by simply calling the ``fit`` method:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "net.fit(epochs, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The pre-trained model is accessible via the ``module_`` attribute:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(net.module_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "And we can see that all the following parameters were automatically inferred\nfrom the training data:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(f'{net.module_.n_chans=}\\n{net.module_.n_times=}\\n{net.module_.n_outputs=}'\n      f'\\n{net.module_.input_window_seconds=}\\n{net.module_.sfreq=}\\n{net.module_.chs_info=}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Depending on the type of data used for training, some parameters might not be\npossible to infer. For example if you pass a numpy array or a\n:class:`braindecode.dataset.WindowsDataset` with ``target_from=\"metadata\"``,\n then only ``n_chans``, ``n_times`` and ``n_outputs`` will be inferred.\nAnd if you pass other types of datasets, only ``n_chans`` and ``n_times`` will be inferred.\nIn these case, you will have to pass the missing parameters manually\n(with the prefix ``module__``).\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## References\n\n.. [1] Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter,\n       M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T.(2017).\n       Deep learning with convolutional neural networks for EEG decoding and visualization.\n       Human Brain Mapping, Aug. 2017.\n       Online: http://dx.doi.org/10.1002/hbm.23730\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/stable/_images/sphx_glr_plot_basic_training_epochs_thumb.png b/stable/_images/sphx_glr_plot_basic_training_epochs_thumb.png
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-              
-  <h1>Source code for braindecode.augmentation.base</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1">#          Alexandre Gramfort &lt;alexandre.gramfort@inria.fr&gt;</span>
-<span class="c1">#          Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
-<span class="c1">#          Martin Wimpff &lt;martin.wimpff@iss.uni-stuttgart.de&gt;</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Any</span>
-<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Real</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_random_state</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data._utils.collate</span> <span class="kn">import</span> <span class="n">default_collate</span>
-
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">identity</span>
-
-<span class="n">Batch</span> <span class="o">=</span> <span class="n">List</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span>
-<span class="n">Output</span> <span class="o">=</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="Transform"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform">[docs]</a><span class="k">class</span> <span class="nc">Transform</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Basic transform class used for implementing data augmentation</span>
-<span class="sd">    operations.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    operation : callable</span>
-<span class="sd">        A function taking arrays X, y (inputs and targets resp.) and</span>
-<span class="sd">        other required arguments, and returning the transformed X and y.</span>
-<span class="sd">    probability : float, optional</span>
-<span class="sd">        Float between 0 and 1 defining the uniform probability of applying the</span>
-<span class="sd">        operation. Set to 1.0 by default (e.g always apply the operation).</span>
-<span class="sd">    random_state: int, optional</span>
-<span class="sd">        Seed to be used to instatiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument. Defaults to None.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">probability</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">forward</span><span class="o">.</span><span class="vm">__func__</span> <span class="ow">is</span> <span class="n">Transform</span><span class="o">.</span><span class="n">forward</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">callable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">operation</span><span class="p">),</span>\
-                <span class="s2">&quot;operation should be a ``callable``.&quot;</span>
-
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">probability</span><span class="p">,</span> <span class="n">Real</span><span class="p">),</span> <span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;probability should be a ``real``. Got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">probability</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="n">probability</span> <span class="o">&lt;=</span> <span class="mf">1.</span> <span class="ow">and</span> <span class="n">probability</span> <span class="o">&gt;=</span> <span class="mf">0.</span><span class="p">,</span> \
-            <span class="s2">&quot;probability should be between 0 and 1.&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_probability</span> <span class="o">=</span> <span class="n">probability</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-
-<div class="viewcode-block" id="Transform.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">dict</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="Transform.forward"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Output</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;General forward pass for an augmentation transform.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : torch.Tensor</span>
-<span class="sd">            EEG input example or batch.</span>
-<span class="sd">        y : torch.Tensor | None</span>
-<span class="sd">            EEG labels for the example or batch. Defaults to None.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        torch.Tensor</span>
-<span class="sd">            Transformed inputs.</span>
-<span class="sd">        torch.Tensor, optional</span>
-<span class="sd">            Transformed labels. Only returned when y is not None.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-        <span class="n">out_X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-            <span class="n">out_y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">out_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-        <span class="c1"># Samples a mask setting for each example whether they should stay</span>
-        <span class="c1"># inchanged or not</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_mask</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">num_valid</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="n">num_valid</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># Uses the mask to define the output</span>
-            <span class="n">out_X</span><span class="p">[</span><span class="n">mask</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">tr_y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operation</span><span class="p">(</span>
-                <span class="n">out_X</span><span class="p">[</span><span class="n">mask</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">out_y</span><span class="p">[</span><span class="n">mask</span><span class="p">],</span>
-                <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">get_augmentation_params</span><span class="p">(</span><span class="n">out_X</span><span class="p">[</span><span class="n">mask</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">out_y</span><span class="p">[</span><span class="n">mask</span><span class="p">])</span>
-            <span class="p">)</span>
-            <span class="c1"># Apply the operation defining the Transform to the whole batch</span>
-            <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">tr_y</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span><span class="p">:</span>
-                <span class="n">out_y</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">tmp_y</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="k">for</span> <span class="n">tmp_y</span> <span class="ow">in</span> <span class="n">tr_y</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">out_y</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="n">tr_y</span>
-
-        <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">out_X</span><span class="p">,</span> <span class="n">out_y</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">out_X</span></div>
-
-    <span class="k">def</span> <span class="nf">_get_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
-        <span class="sd">&quot;&quot;&quot;Samples whether to apply operation or not over the whole batch</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">probability</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
-        <span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">probability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_probability</span></div>
-
-
-<div class="viewcode-block" id="IdentityTransform"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.IdentityTransform.html#braindecode.augmentation.IdentityTransform">[docs]</a><span class="k">class</span> <span class="nc">IdentityTransform</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Identity transform.</span>
-
-<span class="sd">    Transform that does not change the input.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">identity</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="Compose"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose">[docs]</a><span class="k">class</span> <span class="nc">Compose</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Transform composition.</span>
-
-<span class="sd">    Callable class allowing to cast a sequence of Transform objects into a</span>
-<span class="sd">    single one.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    transforms: list</span>
-<span class="sd">        Sequence of Transforms to be composed.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transforms</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-<div class="viewcode-block" id="Compose.forward"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">transform</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span><span class="p">:</span>
-            <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div></div>
-
-
-<span class="k">def</span> <span class="nf">_make_collateable</span><span class="p">(</span><span class="n">transform</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Wraps a transform to make it collateable.</span>
-<span class="sd">        with device control. &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">_collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
-        <span class="n">collated_batch</span> <span class="o">=</span> <span class="n">default_collate</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">collated_batch</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="p">(</span><span class="o">*</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="o">*</span><span class="n">collated_batch</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
-
-    <span class="k">return</span> <span class="n">_collate_fn</span>
-
-
-<div class="viewcode-block" id="AugmentedDataLoader"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader">[docs]</a><span class="k">class</span> <span class="nc">AugmentedDataLoader</span><span class="p">(</span><span class="n">DataLoader</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A base dataloader class customized to applying augmentation Transforms.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataset : BaseDataset</span>
-<span class="sd">        The dataset containing the signals.</span>
-<span class="sd">    transforms : list | Transform, optional</span>
-<span class="sd">        Transform or sequence of Transform to be applied to each batch.</span>
-<span class="sd">    device : str | torch.device | None, optional</span>
-<span class="sd">        Device on which to transform the data. Defaults to None.</span>
-<span class="sd">    **kwargs : dict, optional</span>
-<span class="sd">        keyword arguments to pass to standard DataLoader class.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">transforms</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="s2">&quot;collate_fn&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s2">&quot;collate_fn cannot be used in this context because it is used &quot;</span>
-                <span class="s2">&quot;to pass transform&quot;</span>
-            <span class="p">)</span>
-        <span class="k">if</span> <span class="n">transforms</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span>
-                <span class="nb">isinstance</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">transforms</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span>
-        <span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">collated_tr</span> <span class="o">=</span> <span class="n">_make_collateable</span><span class="p">(</span><span class="n">IdentityTransform</span><span class="p">(),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="p">(</span><span class="n">Transform</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">)):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">collated_tr</span> <span class="o">=</span> <span class="n">_make_collateable</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transforms</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">collated_tr</span> <span class="o">=</span> <span class="n">_make_collateable</span><span class="p">(</span><span class="n">Compose</span><span class="p">(</span><span class="n">transforms</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;transforms can be either a Transform object&quot;</span> <span class="o">+</span>
-                            <span class="s2">&quot; or a list of Transform objects.&quot;</span><span class="p">)</span>
-
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">dataset</span><span class="p">,</span>
-            <span class="n">collate_fn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">collated_tr</span><span class="p">,</span>
-            <span class="o">**</span><span class="n">kwargs</span>
-        <span class="p">)</span></div>
-</pre></div>
-
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-  <h1>Source code for braindecode.augmentation.functional</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1">#          Alexandre Gramfort &lt;alexandre.gramfort@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Real</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">Rbf</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_random_state</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.fft</span> <span class="kn">import</span> <span class="n">fft</span><span class="p">,</span> <span class="n">ifft</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">pad</span><span class="p">,</span> <span class="n">one_hot</span>
-<span class="kn">from</span> <span class="nn">mne.filter</span> <span class="kn">import</span> <span class="n">notch_filter</span>
-
-
-<div class="viewcode-block" id="identity"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.identity.html#braindecode.augmentation.functional.identity">[docs]</a><span class="k">def</span> <span class="nf">identity</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Identity operation.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="time_reverse"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.time_reverse.html#braindecode.augmentation.functional.time_reverse">[docs]</a><span class="k">def</span> <span class="nf">time_reverse</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Flip the time axis of each input.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="sign_flip"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.sign_flip.html#braindecode.augmentation.functional.sign_flip">[docs]</a><span class="k">def</span> <span class="nf">sign_flip</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Flip the sign axis of each input.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="o">-</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<span class="k">def</span> <span class="nf">_new_random_fft_phase_odd</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
-    <span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="n">random_phase</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
-        <span class="mi">2</span><span class="n">j</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span>
-        <span class="n">random_phase</span><span class="p">,</span>
-        <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">random_phase</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_new_random_fft_phase_even</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
-    <span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="n">random_phase</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
-        <span class="mi">2</span><span class="n">j</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">n</span> <span class="o">//</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span>
-        <span class="n">random_phase</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span>
-        <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">random_phase</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="p">],</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="n">_new_random_fft_phase</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="mi">0</span><span class="p">:</span> <span class="n">_new_random_fft_phase_even</span><span class="p">,</span>
-    <span class="mi">1</span><span class="p">:</span> <span class="n">_new_random_fft_phase_odd</span>
-<span class="p">}</span>
-
-
-<div class="viewcode-block" id="ft_surrogate"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.ft_surrogate.html#braindecode.augmentation.functional.ft_surrogate">[docs]</a><span class="k">def</span> <span class="nf">ft_surrogate</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span>
-    <span class="n">y</span><span class="p">,</span>
-    <span class="n">phase_noise_magnitude</span><span class="p">,</span>
-    <span class="n">channel_indep</span><span class="p">,</span>
-    <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;FT surrogate augmentation of a single EEG channel, as proposed in [1]_.</span>
-
-<span class="sd">    Function copied from https://github.com/cliffordlab/sleep-convolutions-tf</span>
-<span class="sd">    and modified.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    phase_noise_magnitude: float</span>
-<span class="sd">        Float between 0 and 1 setting the range over which the phase</span>
-<span class="sd">        pertubation is uniformly sampled:</span>
-<span class="sd">        [0, `phase_noise_magnitude` * 2 * `pi`].</span>
-<span class="sd">    channel_indep : bool</span>
-<span class="sd">        Whether to sample phase perturbations independently for each channel or</span>
-<span class="sd">        not. It is advised to set it to False when spatial information is</span>
-<span class="sd">        important for the task, like in BCI.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Used to draw the phase perturbation. Defaults to None.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;</span>
-<span class="sd">       Clifford, G. D. (2018). Addressing Class Imbalance in Classification</span>
-<span class="sd">       Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv</span>
-<span class="sd">       preprint arXiv:1806.08675.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span>
-        <span class="n">phase_noise_magnitude</span><span class="p">,</span>
-        <span class="p">(</span><span class="n">Real</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">)</span>
-    <span class="p">)</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">phase_noise_magnitude</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span>
-        <span class="sa">f</span><span class="s2">&quot;eps must be a float beween 0 and 1. Got </span><span class="si">{</span><span class="n">phase_noise_magnitude</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
-
-    <span class="n">f</span> <span class="o">=</span> <span class="n">fft</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">double</span><span class="p">(),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">device</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">device</span>
-
-    <span class="n">n</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">random_phase</span> <span class="o">=</span> <span class="n">_new_random_fft_phase</span><span class="p">[</span><span class="n">n</span> <span class="o">%</span> <span class="mi">2</span><span class="p">](</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="n">f</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">channel_indep</span> <span class="k">else</span> <span class="mi">1</span><span class="p">,</span>
-        <span class="n">n</span><span class="p">,</span>
-        <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">channel_indep</span><span class="p">:</span>
-        <span class="n">random_phase</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">random_phase</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">f</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">phase_noise_magnitude</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-        <span class="n">phase_noise_magnitude</span> <span class="o">=</span> <span class="n">phase_noise_magnitude</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">f_shifted</span> <span class="o">=</span> <span class="n">f</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">phase_noise_magnitude</span> <span class="o">*</span> <span class="n">random_phase</span><span class="p">)</span>
-    <span class="n">shifted</span> <span class="o">=</span> <span class="n">ifft</span><span class="p">(</span><span class="n">f_shifted</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">transformed_X</span> <span class="o">=</span> <span class="n">shifted</span><span class="o">.</span><span class="n">real</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">transformed_X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<span class="k">def</span> <span class="nf">_pick_channels_randomly</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">p_pick</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
-    <span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-    <span class="c1"># allows to use the same RNG</span>
-    <span class="n">unif_samples</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-        <span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">)),</span>
-        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
-        <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="c1"># equivalent to a 0s and 1s mask</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="mi">1000</span><span class="o">*</span><span class="p">(</span><span class="n">unif_samples</span> <span class="o">-</span> <span class="n">p_pick</span><span class="p">))</span>
-
-
-<div class="viewcode-block" id="channels_dropout"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.channels_dropout.html#braindecode.augmentation.functional.channels_dropout">[docs]</a><span class="k">def</span> <span class="nf">channels_dropout</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">p_drop</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly set channels to flat signal.</span>
-
-<span class="sd">    Part of the CMSAugment policy proposed in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    p_drop : float</span>
-<span class="sd">        Float between 0 and 1 setting the probability of dropping each channel.</span>
-<span class="sd">    random_state : int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).</span>
-<span class="sd">       Learning from Heterogeneous EEG Signals with Differentiable Channel</span>
-<span class="sd">       Reordering. arXiv preprint arXiv:2010.13694.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">_pick_channels_randomly</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">p_drop</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">X</span> <span class="o">*</span> <span class="n">mask</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">y</span></div>
-
-
-<span class="k">def</span> <span class="nf">_make_permutation_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
-    <span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">hard_mask</span> <span class="o">=</span> <span class="n">mask</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
-    <span class="n">batch_permutations</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>
-        <span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span>
-    <span class="p">)</span>
-    <span class="k">for</span> <span class="n">b</span><span class="p">,</span> <span class="n">mask</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">hard_mask</span><span class="p">):</span>
-        <span class="n">channels_to_shuffle</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_channels</span><span class="p">)</span>
-        <span class="n">channels_to_shuffle</span> <span class="o">=</span> <span class="n">channels_to_shuffle</span><span class="p">[</span><span class="n">mask</span><span class="o">.</span><span class="n">bool</span><span class="p">()]</span>
-        <span class="n">channels_permutation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_channels</span><span class="p">)</span>
-        <span class="n">channels_permutation</span><span class="p">[</span><span class="n">channels_to_shuffle</span><span class="p">]</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span>
-            <span class="n">channels_to_shuffle</span>
-        <span class="p">)</span>
-        <span class="n">channels_permutation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="n">channels_permutation</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span>
-        <span class="p">)</span>
-        <span class="n">batch_permutations</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">one_hot</span><span class="p">(</span><span class="n">channels_permutation</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">batch_permutations</span>
-
-
-<div class="viewcode-block" id="channels_shuffle"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.channels_shuffle.html#braindecode.augmentation.functional.channels_shuffle">[docs]</a><span class="k">def</span> <span class="nf">channels_shuffle</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">p_shuffle</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly shuffle channels in EEG data matrix.</span>
-
-<span class="sd">    Part of the CMSAugment policy proposed in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    p_shuffle: float | None</span>
-<span class="sd">        Float between 0 and 1 setting the probability of including the channel</span>
-<span class="sd">        in the set of permutted channels.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to sample which channels to shuffle and to carry the shuffle.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).</span>
-<span class="sd">       Learning from Heterogeneous EEG Signals with Differentiable Channel</span>
-<span class="sd">       Reordering. arXiv preprint arXiv:2010.13694.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">p_shuffle</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="n">_pick_channels_randomly</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">p_shuffle</span><span class="p">,</span> <span class="n">random_state</span><span class="p">)</span>
-    <span class="n">batch_permutations</span> <span class="o">=</span> <span class="n">_make_permutation_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">mask</span><span class="p">,</span> <span class="n">random_state</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">batch_permutations</span><span class="p">,</span> <span class="n">X</span><span class="p">),</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="gaussian_noise"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.gaussian_noise.html#braindecode.augmentation.functional.gaussian_noise">[docs]</a><span class="k">def</span> <span class="nf">gaussian_noise</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly add white Gaussian noise to all channels.</span>
-
-<span class="sd">    Suggested e.g. in [1]_, [2]_ and [3]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    std : float</span>
-<span class="sd">        Standard deviation to use for the additive noise.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data</span>
-<span class="sd">       augmentation for eeg-based emotion recognition with deep convolutional</span>
-<span class="sd">       neural networks. In International Conference on Multimedia Modeling</span>
-<span class="sd">       (pp. 82-93).</span>
-<span class="sd">    .. [2] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [3] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">std</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-        <span class="n">std</span> <span class="o">=</span> <span class="n">std</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">noise</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
-        <span class="n">rng</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span>
-            <span class="n">loc</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
-            <span class="n">scale</span><span class="o">=</span><span class="mi">1</span>
-        <span class="p">),</span>
-    <span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span>
-    <span class="n">transformed_X</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">noise</span>
-    <span class="k">return</span> <span class="n">transformed_X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="channels_permute"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.channels_permute.html#braindecode.augmentation.functional.channels_permute">[docs]</a><span class="k">def</span> <span class="nf">channels_permute</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">permutation</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Permute EEG channels according to fixed permutation matrix.</span>
-
-<span class="sd">    Suggested e.g. in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    permutation : list</span>
-<span class="sd">        List of integers defining the new channels order.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.</span>
-<span class="sd">       (2018). HAMLET: interpretable human and machine co-learning technique.</span>
-<span class="sd">       arXiv preprint arXiv:1803.09702.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">X</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">permutation</span><span class="p">,</span> <span class="p">:],</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="smooth_time_mask"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.smooth_time_mask.html#braindecode.augmentation.functional.smooth_time_mask">[docs]</a><span class="k">def</span> <span class="nf">smooth_time_mask</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">mask_start_per_sample</span><span class="p">,</span> <span class="n">mask_len_samples</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Smoothly replace a contiguous part of all channels by zeros.</span>
-
-<span class="sd">    Originally proposed in [1]_ and [2]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    mask_start_per_sample : torch.tensor</span>
-<span class="sd">        Tensor of integers containing the position (in last dimension) where to</span>
-<span class="sd">        start masking the signal. Should have the same size as the first</span>
-<span class="sd">        dimension of X (i.e. one start position per example in the batch).</span>
-<span class="sd">    mask_len_samples : int</span>
-<span class="sd">        Number of consecutive samples to zero out.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [2] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">seq_len</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-    <span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">seq_len</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-    <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">mask_start_per_sample</span> <span class="o">=</span> <span class="n">mask_start_per_sample</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="n">s</span> <span class="o">=</span> <span class="mi">1000</span> <span class="o">/</span> <span class="n">seq_len</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">s</span> <span class="o">*</span> <span class="o">-</span><span class="p">(</span><span class="n">t</span> <span class="o">-</span> <span class="n">mask_start_per_sample</span><span class="p">))</span> <span class="o">+</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">s</span> <span class="o">*</span> <span class="p">(</span><span class="n">t</span> <span class="o">-</span> <span class="n">mask_start_per_sample</span> <span class="o">-</span> <span class="n">mask_len_samples</span><span class="p">))</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">X</span> <span class="o">*</span> <span class="n">mask</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="bandstop_filter"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.bandstop_filter.html#braindecode.augmentation.functional.bandstop_filter">[docs]</a><span class="k">def</span> <span class="nf">bandstop_filter</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">freqs_to_notch</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Apply a band-stop filter with desired bandwidth at the desired frequency</span>
-<span class="sd">    position.</span>
-
-<span class="sd">    Suggested e.g. in [1]_ and [2]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        Sampling frequency of the signals to be filtered.</span>
-<span class="sd">    bandwidth : float</span>
-<span class="sd">        Bandwidth of the filter, i.e. distance between the low and high cut</span>
-<span class="sd">        frequencies.</span>
-<span class="sd">    freqs_to_notch : array-like | None</span>
-<span class="sd">        Array of floats of size ``(batch_size,)`` containing the center of the</span>
-<span class="sd">        frequency band to filter out for each sample in the batch. Frequencies</span>
-<span class="sd">        should be greater than ``bandwidth/2 + transition`` and lower than</span>
-<span class="sd">        ``sfreq/2 - bandwidth/2 - transition`` (where ``transition = 1 Hz``).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [2] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">bandwidth</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
-    <span class="n">transformed_X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">notched_freq</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span>
-            <span class="nb">zip</span><span class="p">(</span><span class="n">transformed_X</span><span class="p">,</span> <span class="n">freqs_to_notch</span><span class="p">)):</span>
-        <span class="n">sample</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="n">transformed_X</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">notch_filter</span><span class="p">(</span>
-            <span class="n">sample</span><span class="p">,</span>
-            <span class="n">Fs</span><span class="o">=</span><span class="n">sfreq</span><span class="p">,</span>
-            <span class="n">freqs</span><span class="o">=</span><span class="n">notched_freq</span><span class="p">,</span>
-            <span class="n">method</span><span class="o">=</span><span class="s1">&#39;fir&#39;</span><span class="p">,</span>
-            <span class="n">notch_widths</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">,</span>
-            <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
-        <span class="p">))</span>
-    <span class="k">return</span> <span class="n">transformed_X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<span class="k">def</span> <span class="nf">_analytic_transform</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_complex</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;x must be real.&quot;</span><span class="p">)</span>
-
-    <span class="n">N</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">f</span> <span class="o">=</span> <span class="n">fft</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">h</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">N</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">h</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">h</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">N</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">h</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">:</span><span class="n">N</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">h</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">h</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">:(</span><span class="n">N</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
-
-    <span class="k">return</span> <span class="n">ifft</span><span class="p">(</span><span class="n">f</span> <span class="o">*</span> <span class="n">h</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_nextpow2</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Return the first integer N such that 2**N &gt;= abs(n).&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log2</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">n</span><span class="p">))))</span>
-
-
-<span class="k">def</span> <span class="nf">_frequency_shift</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">fs</span><span class="p">,</span> <span class="n">f_shift</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Shift the specified signal by the specified frequency.</span>
-
-<span class="sd">    See https://gist.github.com/lebedov/4428122</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Pad the signal with zeros to prevent the FFT invoked by the transform</span>
-    <span class="c1"># from slowing down the computation:</span>
-    <span class="n">n_channels</span><span class="p">,</span> <span class="n">N_orig</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
-    <span class="n">N_padded</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="n">_nextpow2</span><span class="p">(</span><span class="n">N_orig</span><span class="p">)</span>
-    <span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">N_padded</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">/</span> <span class="n">fs</span>
-    <span class="n">padded</span> <span class="o">=</span> <span class="n">pad</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N_padded</span> <span class="o">-</span> <span class="n">N_orig</span><span class="p">))</span>
-    <span class="n">analytical</span> <span class="o">=</span> <span class="n">_analytic_transform</span><span class="p">(</span><span class="n">padded</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">f_shift</span><span class="p">,</span> <span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-        <span class="n">f_shift</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">f_shift</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-    <span class="n">reshaped_f_shift</span> <span class="o">=</span> <span class="n">f_shift</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span>
-        <span class="n">N_padded</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">shifted</span> <span class="o">=</span> <span class="n">analytical</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">2</span><span class="n">j</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">reshaped_f_shift</span> <span class="o">*</span> <span class="n">t</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">shifted</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">N_orig</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
-
-
-<div class="viewcode-block" id="frequency_shift"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.frequency_shift.html#braindecode.augmentation.functional.frequency_shift">[docs]</a><span class="k">def</span> <span class="nf">frequency_shift</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">delta_freq</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Adds a shift in the frequency domain to all channels.</span>
-
-<span class="sd">    Note that here, the shift is the same for all channels of a single example.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    delta_freq : float</span>
-<span class="sd">        The amplitude of the frequency shift (in Hz).</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        Sampling frequency of the signals to be transformed.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed inputs.</span>
-<span class="sd">    torch.Tensor</span>
-<span class="sd">        Transformed labels.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">transformed_X</span> <span class="o">=</span> <span class="n">_frequency_shift</span><span class="p">(</span>
-        <span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
-        <span class="n">fs</span><span class="o">=</span><span class="n">sfreq</span><span class="p">,</span>
-        <span class="n">f_shift</span><span class="o">=</span><span class="n">delta_freq</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">transformed_X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<span class="k">def</span> <span class="nf">_torch_normalize_vectors</span><span class="p">(</span><span class="n">rr</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Normalize surface vertices.&quot;&quot;&quot;</span>
-    <span class="n">norm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rr</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">norm</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span>
-    <span class="n">norm</span><span class="p">[</span><span class="o">~</span><span class="n">mask</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># in case norm is zero, divide by 1</span>
-    <span class="n">new_rr</span> <span class="o">=</span> <span class="n">rr</span> <span class="o">/</span> <span class="n">norm</span>
-    <span class="k">return</span> <span class="n">new_rr</span>
-
-
-<span class="k">def</span> <span class="nf">_torch_legval</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">tensor</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Evaluate a Legendre series at points x.</span>
-<span class="sd">    If `c` is of length `n + 1`, this function returns the value:</span>
-<span class="sd">    .. math:: p(x) = c_0 * L_0(x) + c_1 * L_1(x) + ... + c_n * L_n(x)</span>
-<span class="sd">    The parameter `x` is converted to an array only if it is a tuple or a</span>
-<span class="sd">    list, otherwise it is treated as a scalar. In either case, either `x`</span>
-<span class="sd">    or its elements must support multiplication and addition both with</span>
-<span class="sd">    themselves and with the elements of `c`.</span>
-<span class="sd">    If `c` is a 1-D array, then `p(x)` will have the same shape as `x`.  If</span>
-<span class="sd">    `c` is multidimensional, then the shape of the result depends on the</span>
-<span class="sd">    value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +</span>
-<span class="sd">    x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that</span>
-<span class="sd">    scalars have shape (,).</span>
-<span class="sd">    Trailing zeros in the coefficients will be used in the evaluation, so</span>
-<span class="sd">    they should be avoided if efficiency is a concern.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    x : array_like, compatible object</span>
-<span class="sd">        If `x` is a list or tuple, it is converted to an ndarray, otherwise</span>
-<span class="sd">        it is left unchanged and treated as a scalar. In either case, `x`</span>
-<span class="sd">        or its elements must support addition and multiplication with</span>
-<span class="sd">        with themselves and with the elements of `c`.</span>
-<span class="sd">    c : array_like</span>
-<span class="sd">        Array of coefficients ordered so that the coefficients for terms of</span>
-<span class="sd">        degree n are contained in c[n]. If `c` is multidimensional the</span>
-<span class="sd">        remaining indices enumerate multiple polynomials. In the two</span>
-<span class="sd">        dimensional case the coefficients may be thought of as stored in</span>
-<span class="sd">        the columns of `c`.</span>
-<span class="sd">    tensor : boolean, optional</span>
-<span class="sd">        If True, the shape of the coefficient array is extended with ones</span>
-<span class="sd">        on the right, one for each dimension of `x`. Scalars have dimension 0</span>
-<span class="sd">        for this action. The result is that every column of coefficients in</span>
-<span class="sd">        `c` is evaluated for every element of `x`. If False, `x` is broadcast</span>
-<span class="sd">        over the columns of `c` for the evaluation.  This keyword is useful</span>
-<span class="sd">        when `c` is multidimensional. The default value is True.</span>
-<span class="sd">        .. versionadded:: 1.7.0</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    values : ndarray, algebra_like</span>
-<span class="sd">        The shape of the return value is described above.</span>
-
-<span class="sd">    See Also</span>
-<span class="sd">    --------</span>
-<span class="sd">    legval2d, leggrid2d, legval3d, leggrid3d</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Code copied and modified from Numpy:</span>
-<span class="sd">    https://github.com/numpy/numpy/blob/v1.20.0/numpy/polynomial/legendre.py#L835-L920</span>
-
-<span class="sd">    Copyright (c) 2005-2021, NumPy Developers.</span>
-<span class="sd">    All rights reserved.</span>
-
-<span class="sd">    Redistribution and use in source and binary forms, with or without</span>
-<span class="sd">    modification, are permitted provided that the following conditions are</span>
-<span class="sd">    met:</span>
-<span class="sd">        * Redistributions of source code must retain the above copyright</span>
-<span class="sd">        notice, this list of conditions and the following disclaimer.</span>
-<span class="sd">        * Redistributions in binary form must reproduce the above</span>
-<span class="sd">        copyright notice, this list of conditions and the following</span>
-<span class="sd">        disclaimer in the documentation and/or other materials provided</span>
-<span class="sd">        with the distribution.</span>
-<span class="sd">        * Neither the name of the NumPy Developers nor the names of any</span>
-<span class="sd">        contributors may be used to endorse or promote products derived</span>
-<span class="sd">        from this software without specific prior written permission.</span>
-
-<span class="sd">    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
-<span class="sd">    &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
-<span class="sd">    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR</span>
-<span class="sd">    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT</span>
-<span class="sd">    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,</span>
-<span class="sd">    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT</span>
-<span class="sd">    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,</span>
-<span class="sd">    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY</span>
-<span class="sd">    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
-<span class="sd">    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span>
-<span class="sd">    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">double</span><span class="p">()</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="n">tensor</span><span class="p">:</span>
-        <span class="n">c</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">shape</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span><span class="o">*</span><span class="n">x</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span>
-
-    <span class="n">c</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">c0</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">c1</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">c0</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">c1</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">nd</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
-        <span class="n">c0</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-        <span class="n">c1</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">c</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">tmp</span> <span class="o">=</span> <span class="n">c0</span>
-            <span class="n">nd</span> <span class="o">=</span> <span class="n">nd</span> <span class="o">-</span> <span class="mi">1</span>
-            <span class="n">c0</span> <span class="o">=</span> <span class="n">c</span><span class="p">[</span><span class="o">-</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">c1</span><span class="o">*</span><span class="p">(</span><span class="n">nd</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="n">nd</span>
-            <span class="n">c1</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="p">(</span><span class="n">c1</span><span class="o">*</span><span class="n">x</span><span class="o">*</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">nd</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="n">nd</span>
-    <span class="k">return</span> <span class="n">c0</span> <span class="o">+</span> <span class="n">c1</span><span class="o">*</span><span class="n">x</span>
-
-
-<span class="k">def</span> <span class="nf">_torch_calc_g</span><span class="p">(</span><span class="n">cosang</span><span class="p">,</span> <span class="n">stiffness</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">n_legendre_terms</span><span class="o">=</span><span class="mi">50</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Calculate spherical spline g function between points on a sphere.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    cosang : array-like of float, shape(n_channels, n_channels)</span>
-<span class="sd">        cosine of angles between pairs of points on a spherical surface. This</span>
-<span class="sd">        is equivalent to the dot product of unit vectors.</span>
-<span class="sd">    stiffness : float</span>
-<span class="sd">        stiffness of the spline.</span>
-<span class="sd">    n_legendre_terms : int</span>
-<span class="sd">        number of Legendre terms to evaluate.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    G : np.ndrarray of float, shape(n_channels, n_channels)</span>
-<span class="sd">        The G matrix.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Code copied and modified from MNE-Python:</span>
-<span class="sd">    https://github.com/mne-tools/mne-python/blob/bdaa1d460201a3bc3cec95b67fc2b8d31a933652/mne/channels/interpolation.py#L35</span>
-
-<span class="sd">    Copyright © 2011-2019, authors of MNE-Python</span>
-<span class="sd">    All rights reserved.</span>
-
-<span class="sd">    Redistribution and use in source and binary forms, with or without</span>
-<span class="sd">    modification, are permitted provided that the following conditions are met:</span>
-<span class="sd">        * Redistributions of source code must retain the above copyright</span>
-<span class="sd">        notice, this list of conditions and the following disclaimer.</span>
-<span class="sd">        * Redistributions in binary form must reproduce the above copyright</span>
-<span class="sd">        notice, this list of conditions and the following disclaimer in the</span>
-<span class="sd">        documentation and/or other materials provided with the distribution.</span>
-<span class="sd">        * Neither the name of the copyright holder nor the names of its</span>
-<span class="sd">        contributors may be used to endorse or promote products derived from</span>
-<span class="sd">        this software without specific prior written permission.</span>
-
-<span class="sd">    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS &quot;AS IS&quot;</span>
-<span class="sd">    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE</span>
-<span class="sd">    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE</span>
-<span class="sd">    ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE</span>
-<span class="sd">    FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL</span>
-<span class="sd">    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR</span>
-<span class="sd">    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER</span>
-<span class="sd">    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT</span>
-<span class="sd">    LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY</span>
-<span class="sd">    OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH</span>
-<span class="sd">    DAMAGE.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">factors</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">**</span> <span class="n">stiffness</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="n">stiffness</span> <span class="o">*</span>
-                              <span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-               <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_legendre_terms</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-    <span class="k">return</span> <span class="n">_torch_legval</span><span class="p">(</span><span class="n">cosang</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">factors</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_torch_make_interpolation_matrix</span><span class="p">(</span><span class="n">pos_from</span><span class="p">,</span> <span class="n">pos_to</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute interpolation matrix based on spherical splines.</span>
-
-<span class="sd">    Implementation based on [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    pos_from : np.ndarray of float, shape(n_good_sensors, 3)</span>
-<span class="sd">        The positions to interpoloate from.</span>
-<span class="sd">    pos_to : np.ndarray of float, shape(n_bad_sensors, 3)</span>
-<span class="sd">        The positions to interpoloate.</span>
-<span class="sd">    alpha : float</span>
-<span class="sd">        Regularization parameter. Defaults to 1e-5.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    interpolation : np.ndarray of float, shape(len(pos_from), len(pos_to))</span>
-<span class="sd">        The interpolation matrix that maps good signals to the location</span>
-<span class="sd">        of bad signals.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    [1] Perrin, F., Pernier, J., Bertrand, O. and Echallier, JF. (1989).</span>
-<span class="sd">        Spherical splines for scalp potential and current density mapping.</span>
-<span class="sd">        Electroencephalography Clinical Neurophysiology, Feb; 72(2):184-7.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Code copied and modified from MNE-Python:</span>
-<span class="sd">    https://github.com/mne-tools/mne-python/blob/bdaa1d460201a3bc3cec95b67fc2b8d31a933652/mne/channels/interpolation.py#L59</span>
-
-<span class="sd">    Copyright © 2011-2019, authors of MNE-Python</span>
-<span class="sd">    All rights reserved.</span>
-
-<span class="sd">    Redistribution and use in source and binary forms, with or without</span>
-<span class="sd">    modification, are permitted provided that the following conditions are met:</span>
-<span class="sd">        * Redistributions of source code must retain the above copyright</span>
-<span class="sd">        notice, this list of conditions and the following disclaimer.</span>
-<span class="sd">        * Redistributions in binary form must reproduce the above copyright</span>
-<span class="sd">        notice, this list of conditions and the following disclaimer in the</span>
-<span class="sd">        documentation and/or other materials provided with the distribution.</span>
-<span class="sd">        * Neither the name of the copyright holder nor the names of its</span>
-<span class="sd">        contributors may be used to endorse or promote products derived from</span>
-<span class="sd">        this software without specific prior written permission.</span>
-
-<span class="sd">    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS &quot;AS IS&quot;</span>
-<span class="sd">    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE</span>
-<span class="sd">    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE</span>
-<span class="sd">    ARE DISCLAIMED. IN NO EVENT SHALL COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE</span>
-<span class="sd">    FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL</span>
-<span class="sd">    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR</span>
-<span class="sd">    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER</span>
-<span class="sd">    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT</span>
-<span class="sd">    LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY</span>
-<span class="sd">    OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH</span>
-<span class="sd">    DAMAGE.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">pos_from</span> <span class="o">=</span> <span class="n">pos_from</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-    <span class="n">pos_to</span> <span class="o">=</span> <span class="n">pos_to</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-    <span class="n">n_from</span> <span class="o">=</span> <span class="n">pos_from</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">n_to</span> <span class="o">=</span> <span class="n">pos_to</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="c1"># normalize sensor positions to sphere</span>
-    <span class="n">pos_from</span> <span class="o">=</span> <span class="n">_torch_normalize_vectors</span><span class="p">(</span><span class="n">pos_from</span><span class="p">)</span>
-    <span class="n">pos_to</span> <span class="o">=</span> <span class="n">_torch_normalize_vectors</span><span class="p">(</span><span class="n">pos_to</span><span class="p">)</span>
-
-    <span class="c1"># cosine angles between source positions</span>
-    <span class="n">cosang_from</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">pos_from</span><span class="p">,</span> <span class="n">pos_from</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-    <span class="n">cosang_to_from</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">pos_to</span><span class="p">,</span> <span class="n">pos_from</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-    <span class="n">G_from</span> <span class="o">=</span> <span class="n">_torch_calc_g</span><span class="p">(</span><span class="n">cosang_from</span><span class="p">)</span>
-    <span class="n">G_to_from</span> <span class="o">=</span> <span class="n">_torch_calc_g</span><span class="p">(</span><span class="n">cosang_to_from</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">G_from</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">n_from</span><span class="p">,</span> <span class="n">n_from</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">G_to_from</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">n_to</span><span class="p">,</span> <span class="n">n_from</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">G_from</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[::</span><span class="nb">len</span><span class="p">(</span><span class="n">G_from</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">alpha</span>
-
-    <span class="n">device</span> <span class="o">=</span> <span class="n">G_from</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">C</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">G_from</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n_from</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)]),</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_from</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)])</span>
-        <span class="p">])</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">C_inv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
-    <span class="k">except</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_LinAlgError</span><span class="p">:</span>
-        <span class="c1"># There is a stability issue with pinv since torch v1.8.0</span>
-        <span class="c1"># see https://github.com/pytorch/pytorch/issues/75494</span>
-        <span class="n">C_inv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">C</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="n">interpolation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span>
-        <span class="n">G_to_from</span><span class="p">,</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n_to</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-    <span class="p">])</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">C_inv</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">assert</span> <span class="n">interpolation</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">n_to</span><span class="p">,</span> <span class="n">n_from</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">interpolation</span>
-
-
-<span class="k">def</span> <span class="nf">_rotate_signals</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">rotations</span><span class="p">,</span> <span class="n">sensors_positions_matrix</span><span class="p">,</span> <span class="n">spherical</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">sensors_positions_matrix</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">rot_sensors_matrices</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="n">rotation</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">sensors_positions_matrix</span><span class="p">)</span> <span class="k">for</span> <span class="n">rotation</span> <span class="ow">in</span> <span class="n">rotations</span>
-    <span class="p">]</span>
-    <span class="k">if</span> <span class="n">spherical</span><span class="p">:</span>
-        <span class="n">interpolation_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
-            <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-                <span class="n">_torch_make_interpolation_matrix</span><span class="p">(</span>
-                    <span class="n">sensors_positions_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">rot_sensors_matrix</span><span class="o">.</span><span class="n">T</span>
-                <span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span>
-            <span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span> <span class="k">for</span> <span class="n">rot_sensors_matrix</span> <span class="ow">in</span> <span class="n">rot_sensors_matrices</span><span class="p">]</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">interpolation_matrix</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">transformed_X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-        <span class="n">sensors_positions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">sensors_positions_matrix</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">s</span><span class="p">,</span> <span class="n">rot_sensors_matrix</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rot_sensors_matrices</span><span class="p">):</span>
-            <span class="n">rot_sensors_positions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">rot_sensors_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">time</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">interpolator_t</span> <span class="o">=</span> <span class="n">Rbf</span><span class="p">(</span><span class="o">*</span><span class="n">sensors_positions</span><span class="p">,</span> <span class="n">X</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="p">:,</span> <span class="n">time</span><span class="p">])</span>
-                <span class="n">transformed_X</span><span class="p">[</span><span class="n">s</span><span class="p">,</span> <span class="p">:,</span> <span class="n">time</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span>
-                    <span class="n">interpolator_t</span><span class="p">(</span><span class="o">*</span><span class="n">rot_sensors_positions</span><span class="p">)</span>
-                <span class="p">)</span>
-        <span class="k">return</span> <span class="n">transformed_X</span>
-
-
-<span class="k">def</span> <span class="nf">_make_rotation_matrix</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="n">degrees</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="k">assert</span> <span class="n">axis</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">],</span> <span class="s2">&quot;axis should be either x, y or z.&quot;</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">angle</span><span class="p">,</span> <span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-        <span class="n">angle</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">degrees</span><span class="p">:</span>
-        <span class="n">angle</span> <span class="o">=</span> <span class="n">angle</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">180</span>
-
-    <span class="n">device</span> <span class="o">=</span> <span class="n">angle</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">zero</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">rot</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">),</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">zero</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">),</span> <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">)]),</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">zero</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">angle</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">angle</span><span class="p">)]),</span>
-    <span class="p">])</span>
-    <span class="k">if</span> <span class="n">axis</span> <span class="o">==</span> <span class="s2">&quot;x&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">rot</span>
-    <span class="k">elif</span> <span class="n">axis</span> <span class="o">==</span> <span class="s2">&quot;y&quot;</span><span class="p">:</span>
-        <span class="n">rot</span> <span class="o">=</span> <span class="n">rot</span><span class="p">[[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">:]</span>
-        <span class="k">return</span> <span class="n">rot</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">rot</span> <span class="o">=</span> <span class="n">rot</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">:]</span>
-        <span class="k">return</span> <span class="n">rot</span><span class="p">[:,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]]</span>
-
-
-<div class="viewcode-block" id="sensors_rotation"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.sensors_rotation.html#braindecode.augmentation.functional.sensors_rotation">[docs]</a><span class="k">def</span> <span class="nf">sensors_rotation</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sensors_positions_matrix</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">angles</span><span class="p">,</span>
-                     <span class="n">spherical_splines</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Interpolates EEG signals over sensors rotated around the desired axis</span>
-<span class="sd">    with the desired angle.</span>
-
-<span class="sd">    Suggested in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG input example or batch.</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        EEG labels for the example or batch.</span>
-<span class="sd">    sensors_positions_matrix : numpy.ndarray</span>
-<span class="sd">        Matrix giving the positions of each sensor in a 3D cartesian coordinate</span>
-<span class="sd">        system. Should have shape (3, n_channels), where n_channels is the</span>
-<span class="sd">        number of channels. Standard 10-20 positions can be obtained from</span>
-<span class="sd">        ``mne`` through::</span>
-
-<span class="sd">         &gt;&gt;&gt; ten_twenty_montage = mne.channels.make_standard_montage(</span>
-<span class="sd">         ...    &#39;standard_1020&#39;</span>
-<span class="sd">         ... ).get_positions()[&#39;ch_pos&#39;]</span>
-<span class="sd">    axis : &#39;x&#39; | &#39;y&#39; | &#39;z&#39;</span>
-<span class="sd">        Axis around which to rotate.</span>
-<span class="sd">    angles : array-like</span>
-<span class="sd">        Array of float of shape ``(batch_size,)`` containing the rotation</span>
-<span class="sd">        angles (in degrees) for each element of the input batch.</span>
-<span class="sd">    spherical_splines : bool</span>
-<span class="sd">        Whether to use spherical splines for the interpolation or not. When</span>
-<span class="sd">        `False`, standard scipy.interpolate.Rbf (with quadratic kernel) will be</span>
-<span class="sd">        used (as in the original paper).</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for</span>
-<span class="sd">       electroencephalographic data. In 2017 39th Annual International</span>
-<span class="sd">       Conference of the IEEE Engineering in Medicine and Biology Society</span>
-<span class="sd">       (EMBC) (pp. 471-474).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">rots</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="n">_make_rotation_matrix</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="n">degrees</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">angle</span> <span class="ow">in</span> <span class="n">angles</span>
-    <span class="p">]</span>
-    <span class="n">rotated_X</span> <span class="o">=</span> <span class="n">_rotate_signals</span><span class="p">(</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">rots</span><span class="p">,</span> <span class="n">sensors_positions_matrix</span><span class="p">,</span> <span class="n">spherical_splines</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">rotated_X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-<div class="viewcode-block" id="mixup"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.functional.mixup.html#braindecode.augmentation.functional.mixup">[docs]</a><span class="k">def</span> <span class="nf">mixup</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">lam</span><span class="p">,</span> <span class="n">idx_perm</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Mixes two channels of EEG data.</span>
-
-<span class="sd">    See [1]_ for details.</span>
-<span class="sd">    Implementation based on [2]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X : torch.Tensor</span>
-<span class="sd">        EEG data in form ``batch_size, n_channels, n_times``</span>
-<span class="sd">    y : torch.Tensor</span>
-<span class="sd">        Target of length ``batch_size``</span>
-<span class="sd">    lam : torch.Tensor</span>
-<span class="sd">        Values between 0 and 1 setting the linear interpolation between</span>
-<span class="sd">        examples.</span>
-<span class="sd">    idx_perm: torch.Tensor</span>
-<span class="sd">        Permuted indices of example that are mixed into original examples.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    tuple</span>
-<span class="sd">        ``X``, ``y``. Where ``X`` is augmented and ``y`` is a tuple  of length</span>
-<span class="sd">        3 containing the labels of the two mixed channels and the mixing</span>
-<span class="sd">        coefficient.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz</span>
-<span class="sd">        (2018). mixup: Beyond Empirical Risk Minimization. In 2018</span>
-<span class="sd">        International Conference on Learning Representations (ICLR)</span>
-<span class="sd">        Online: https://arxiv.org/abs/1710.09412</span>
-<span class="sd">    .. [2] https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py</span>
-<span class="sd">     &quot;&quot;&quot;</span>
-    <span class="n">device</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_times</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-
-    <span class="n">X_mix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_times</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">y_a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-    <span class="n">y_b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span>
-        <span class="n">X_mix</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">lam</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">*</span> <span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> \
-            <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">lam</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span> <span class="o">*</span> <span class="n">X</span><span class="p">[</span><span class="n">idx_perm</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>
-        <span class="n">y_a</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
-        <span class="n">y_b</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">idx_perm</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>
-
-    <span class="k">return</span> <span class="n">X_mix</span><span class="p">,</span> <span class="p">(</span><span class="n">y_a</span><span class="p">,</span> <span class="n">y_b</span><span class="p">,</span> <span class="n">lam</span><span class="p">)</span></div>
-</pre></div>
-
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deleted file mode 100644
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-              
-  <h1>Source code for braindecode.augmentation.transforms</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1">#          Alexandre Gramfort &lt;alexandre.gramfort@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Real</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">mne.channels</span> <span class="kn">import</span> <span class="n">make_standard_montage</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">Transform</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">bandstop_filter</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">channels_dropout</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">channels_permute</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">channels_shuffle</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">frequency_shift</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">ft_surrogate</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">gaussian_noise</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">mixup</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">sensors_rotation</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">sign_flip</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">smooth_time_mask</span>
-<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="n">time_reverse</span>
-
-
-<div class="viewcode-block" id="TimeReverse"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.TimeReverse.html#braindecode.augmentation.TimeReverse">[docs]</a><span class="k">class</span> <span class="nc">TimeReverse</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Flip the time axis of each input with a given probability.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument. Defaults to None.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">time_reverse</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="SignFlip"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip">[docs]</a><span class="k">class</span> <span class="nc">SignFlip</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Flip the sign axis of each input with a given probability.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument. Defaults to None.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">sign_flip</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="FTSurrogate"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate">[docs]</a><span class="k">class</span> <span class="nc">FTSurrogate</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;FT surrogate augmentation of a single EEG channel, as proposed in [1]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability: float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    phase_noise_magnitude : float | torch.Tensor, optional</span>
-<span class="sd">        Float between 0 and 1 setting the range over which the phase</span>
-<span class="sd">        pertubation is uniformly sampled:</span>
-<span class="sd">        ``[0, phase_noise_magnitude * 2 * pi]``. Defaults to 1.</span>
-<span class="sd">    channel_indep : bool, optional</span>
-<span class="sd">        Whether to sample phase perturbations independently for each channel or</span>
-<span class="sd">        not. It is advised to set it to False when spatial information is</span>
-<span class="sd">        important for the task, like in BCI. Default False.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument. Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;</span>
-<span class="sd">       Clifford, G. D. (2018). Addressing Class Imbalance in Classification</span>
-<span class="sd">       Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv</span>
-<span class="sd">       preprint arXiv:1806.08675.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">ft_surrogate</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">phase_noise_magnitude</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">channel_indep</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">phase_noise_magnitude</span><span class="p">,</span> <span class="p">(</span><span class="nb">float</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)),</span>\
-            <span class="s2">&quot;phase_noise_magnitude should be a float.&quot;</span>
-        <span class="k">assert</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">phase_noise_magnitude</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">,</span>\
-            <span class="s2">&quot;phase_noise_magnitude should be between 0 and 1.&quot;</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">channel_indep</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span> <span class="p">(</span>
-            <span class="s2">&quot;channel_indep is expected to be a boolean&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">phase_noise_magnitude</span> <span class="o">=</span> <span class="n">phase_noise_magnitude</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">channel_indep</span> <span class="o">=</span> <span class="n">channel_indep</span>
-
-<div class="viewcode-block" id="FTSurrogate.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains:</span>
-
-<span class="sd">            * phase_noise_magnitude : float</span>
-<span class="sd">                The magnitude of the transformation.</span>
-<span class="sd">            * random_state : numpy.random.Generator</span>
-<span class="sd">                The generator to use.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;phase_noise_magnitude&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase_noise_magnitude</span><span class="p">,</span>
-            <span class="s2">&quot;channel_indep&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_indep</span><span class="p">,</span>
-            <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="ChannelsDropout"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout">[docs]</a><span class="k">class</span> <span class="nc">ChannelsDropout</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly set channels to flat signal.</span>
-
-<span class="sd">    Part of the CMSAugment policy proposed in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability: float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    proba_drop: float | None, optional</span>
-<span class="sd">        Float between 0 and 1 setting the probability of dropping each channel.</span>
-<span class="sd">        Defaults to 0.2.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument and to sample channels to erase. Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).</span>
-<span class="sd">       Learning from Heterogeneous EEG Signals with Differentiable Channel</span>
-<span class="sd">       Reordering. arXiv preprint arXiv:2010.13694.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">channels_dropout</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">p_drop</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p_drop</span> <span class="o">=</span> <span class="n">p_drop</span>
-
-<div class="viewcode-block" id="ChannelsDropout.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * p_drop : float</span>
-<span class="sd">                Float between 0 and 1 setting the probability of dropping each</span>
-<span class="sd">                channel.</span>
-<span class="sd">            * random_state : numpy.random.Generator</span>
-<span class="sd">                The generator to use.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;p_drop&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">p_drop</span><span class="p">,</span>
-            <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="ChannelsShuffle"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle">[docs]</a><span class="k">class</span> <span class="nc">ChannelsShuffle</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly shuffle channels in EEG data matrix.</span>
-
-<span class="sd">    Part of the CMSAugment policy proposed in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability: float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    p_shuffle: float | None, optional</span>
-<span class="sd">        Float between 0 and 1 setting the probability of including the channel</span>
-<span class="sd">        in the set of permuted channels. Defaults to 0.2.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument, to sample which channels to shuffle and to carry the shuffle.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).</span>
-<span class="sd">       Learning from Heterogeneous EEG Signals with Differentiable Channel</span>
-<span class="sd">       Reordering. arXiv preprint arXiv:2010.13694.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">channels_shuffle</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">p_shuffle</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">p_shuffle</span> <span class="o">=</span> <span class="n">p_shuffle</span>
-
-<div class="viewcode-block" id="ChannelsShuffle.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * p_shuffle : float</span>
-<span class="sd">                Float between 0 and 1 setting the probability of including the</span>
-<span class="sd">                channel in the set of permuted channels.</span>
-<span class="sd">            * random_state : numpy.random.Generator</span>
-<span class="sd">                The generator to use.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;p_shuffle&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">p_shuffle</span><span class="p">,</span>
-            <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="GaussianNoise"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.GaussianNoise.html#braindecode.augmentation.GaussianNoise">[docs]</a><span class="k">class</span> <span class="nc">GaussianNoise</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Randomly add white noise to all channels.</span>
-
-<span class="sd">    Suggested e.g. in [1]_, [2]_ and [3]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    std : float, optional</span>
-<span class="sd">        Standard deviation to use for the additive noise. Defaults to 0.1.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data</span>
-<span class="sd">       augmentation for eeg-based emotion recognition with deep convolutional</span>
-<span class="sd">       neural networks. In International Conference on Multimedia Modeling</span>
-<span class="sd">       (pp. 82-93).</span>
-<span class="sd">    .. [2] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [3] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">gaussian_noise</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">std</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">std</span> <span class="o">=</span> <span class="n">std</span>
-
-<div class="viewcode-block" id="GaussianNoise.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.GaussianNoise.html#braindecode.augmentation.GaussianNoise.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * std : float</span>
-<span class="sd">                Standard deviation to use for the additive noise.</span>
-<span class="sd">            * random_state : numpy.random.Generator</span>
-<span class="sd">                The generator to use.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;std&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">std</span><span class="p">,</span>
-            <span class="s2">&quot;random_state&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="ChannelsSymmetry"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry">[docs]</a><span class="k">class</span> <span class="nc">ChannelsSymmetry</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Permute EEG channels inverting left and right-side sensors.</span>
-
-<span class="sd">    Suggested e.g. in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    ordered_ch_names : list</span>
-<span class="sd">        Ordered list of strings containing the names (in 10-20</span>
-<span class="sd">        nomenclature) of the EEG channels that will be transformed. The</span>
-<span class="sd">        first name should correspond the data in the first row of X, the</span>
-<span class="sd">        second name in the second row and so on.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Used to decide whether or not to transform given the probability</span>
-<span class="sd">        argument. Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.</span>
-<span class="sd">       (2018). HAMLET: interpretable human and machine co-learning technique.</span>
-<span class="sd">       arXiv preprint arXiv:1803.09702.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">channels_permute</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">ordered_ch_names</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="p">(</span>
-            <span class="nb">isinstance</span><span class="p">(</span><span class="n">ordered_ch_names</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span>
-            <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">ordered_ch_names</span><span class="p">)</span>
-        <span class="p">),</span> <span class="s2">&quot;ordered_ch_names should be a list of str.&quot;</span>
-
-        <span class="n">permutation</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ch_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ordered_ch_names</span><span class="p">):</span>
-            <span class="n">new_position</span> <span class="o">=</span> <span class="n">idx</span>
-            <span class="c1"># Find digits in channel name (assuming 10-20 system)</span>
-            <span class="n">d</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="nb">str</span><span class="o">.</span><span class="n">isdigit</span><span class="p">,</span> <span class="n">ch_name</span><span class="p">)))</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">d</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">d</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># pair/right electrodes</span>
-                    <span class="n">sym</span> <span class="o">=</span> <span class="n">d</span> <span class="o">-</span> <span class="mi">1</span>
-                <span class="k">else</span><span class="p">:</span>  <span class="c1"># odd/left electrodes</span>
-                    <span class="n">sym</span> <span class="o">=</span> <span class="n">d</span> <span class="o">+</span> <span class="mi">1</span>
-                <span class="n">new_channel</span> <span class="o">=</span> <span class="n">ch_name</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">sym</span><span class="p">))</span>
-                <span class="k">if</span> <span class="n">new_channel</span> <span class="ow">in</span> <span class="n">ordered_ch_names</span><span class="p">:</span>
-                    <span class="n">new_position</span> <span class="o">=</span> <span class="n">ordered_ch_names</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">new_channel</span><span class="p">)</span>
-            <span class="n">permutation</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_position</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">permutation</span> <span class="o">=</span> <span class="n">permutation</span>
-
-<div class="viewcode-block" id="ChannelsSymmetry.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * permutation : float</span>
-<span class="sd">                List of integers defining the new channels order.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">{</span><span class="s2">&quot;permutation&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">permutation</span><span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="SmoothTimeMask"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask">[docs]</a><span class="k">class</span> <span class="nc">SmoothTimeMask</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Smoothly replace a randomly chosen contiguous part of all channels by</span>
-<span class="sd">    zeros.</span>
-
-<span class="sd">    Suggested e.g. in [1]_ and [2]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    mask_len_samples : int | torch.Tensor, optional</span>
-<span class="sd">        Number of consecutive samples to zero out. Will be ignored if</span>
-<span class="sd">        magnitude is not set to None. Defaults to 100.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [2] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">smooth_time_mask</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">mask_len_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="k">assert</span> <span class="p">(</span>
-            <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask_len_samples</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">))</span> <span class="ow">and</span>
-            <span class="n">mask_len_samples</span> <span class="o">&gt;</span> <span class="mi">0</span>
-        <span class="p">),</span> <span class="s2">&quot;mask_len_samples has to be a positive integer&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mask_len_samples</span> <span class="o">=</span> <span class="n">mask_len_samples</span>
-
-<div class="viewcode-block" id="SmoothTimeMask.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains two elements:</span>
-
-<span class="sd">            * mask_start_per_sample : torch.tensor</span>
-<span class="sd">                Tensor of integers containing the position (in last dimension)</span>
-<span class="sd">                where to start masking the signal. Should have the same size as</span>
-<span class="sd">                the first dimension of X (i.e. one start position per example</span>
-<span class="sd">                in the batch).</span>
-<span class="sd">            * mask_len_samples : int</span>
-<span class="sd">                Number of consecutive samples to zero out.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_augmentation_params</span><span class="p">(</span><span class="o">*</span><span class="n">batch</span><span class="p">)</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">seq_length</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">mask_len_samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_len_samples</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mask_len_samples</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-            <span class="n">mask_len_samples</span> <span class="o">=</span> <span class="n">mask_len_samples</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">mask_start</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span>
-            <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">seq_length</span> <span class="o">-</span> <span class="n">mask_len_samples</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;mask_start_per_sample&quot;</span><span class="p">:</span> <span class="n">mask_start</span><span class="p">,</span>
-            <span class="s2">&quot;mask_len_samples&quot;</span><span class="p">:</span> <span class="n">mask_len_samples</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="BandstopFilter"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter">[docs]</a><span class="k">class</span> <span class="nc">BandstopFilter</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Apply a band-stop filter with desired bandwidth at a randomly selected</span>
-<span class="sd">    frequency position between 0 and ``max_freq``.</span>
-
-<span class="sd">    Suggested e.g. in [1]_ and [2]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    bandwidth : float</span>
-<span class="sd">        Bandwidth of the filter, i.e. distance between the low and high cut</span>
-<span class="sd">        frequencies.</span>
-<span class="sd">    sfreq : float, optional</span>
-<span class="sd">        Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</span>
-<span class="sd">    max_freq : float | None, optional</span>
-<span class="sd">        Maximal admissible frequency. The low cut frequency will be sampled so</span>
-<span class="sd">        that the corresponding high cut frequency + transition (=1Hz) are below</span>
-<span class="sd">        ``max_freq``. If omitted or `None`, will default to the Nyquist</span>
-<span class="sd">        frequency (``sfreq / 2``).</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).</span>
-<span class="sd">       Subject-aware contrastive learning for biosignals. arXiv preprint</span>
-<span class="sd">       arXiv:2007.04871.</span>
-<span class="sd">    .. [2] Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive</span>
-<span class="sd">       Representation Learning for Electroencephalogram Classification. In</span>
-<span class="sd">       Machine Learning for Health (pp. 238-253). PMLR.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">bandstop_filter</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">sfreq</span><span class="p">,</span>
-        <span class="n">bandwidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-        <span class="n">max_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">)</span> <span class="ow">and</span> <span class="n">bandwidth</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">,</span>\
-            <span class="s2">&quot;bandwidth should be a non-negative float.&quot;</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sfreq</span><span class="p">,</span> <span class="n">Real</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sfreq</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span>\
-            <span class="s2">&quot;sfreq should be a positive float.&quot;</span>
-        <span class="k">if</span> <span class="n">max_freq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">max_freq</span><span class="p">,</span> <span class="n">Real</span><span class="p">)</span> <span class="ow">and</span> <span class="n">max_freq</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span>\
-                <span class="s2">&quot;max_freq should be a positive float.&quot;</span>
-        <span class="n">nyq</span> <span class="o">=</span> <span class="n">sfreq</span> <span class="o">/</span> <span class="mi">2</span>
-        <span class="k">if</span> <span class="n">max_freq</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_freq</span> <span class="o">&gt;</span> <span class="n">nyq</span><span class="p">:</span>
-            <span class="n">max_freq</span> <span class="o">=</span> <span class="n">nyq</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                <span class="s2">&quot;You either passed None or a frequency greater than the&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot; Nyquist frequency (</span><span class="si">{</span><span class="n">nyq</span><span class="si">}</span><span class="s2"> Hz).&quot;</span>
-                <span class="sa">f</span><span class="s2">&quot; Falling back to max_freq = </span><span class="si">{</span><span class="n">nyq</span><span class="si">}</span><span class="s2">.&quot;</span>
-            <span class="p">)</span>
-        <span class="k">assert</span> <span class="n">bandwidth</span> <span class="o">&lt;</span> <span class="n">max_freq</span><span class="p">,</span>\
-            <span class="sa">f</span><span class="s2">&quot;`bandwidth` needs to be smaller than max_freq=</span><span class="si">{</span><span class="n">max_freq</span><span class="si">}</span><span class="s2">&quot;</span>
-
-        <span class="c1"># override bandwidth value when a magnitude is passed</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span> <span class="o">=</span> <span class="n">sfreq</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_freq</span> <span class="o">=</span> <span class="n">max_freq</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
-
-<div class="viewcode-block" id="BandstopFilter.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * sfreq : float</span>
-<span class="sd">                Sampling frequency of the signals to be filtered.</span>
-<span class="sd">            * bandwidth : float</span>
-<span class="sd">                Bandwidth of the filter, i.e. distance between the low and high</span>
-<span class="sd">                cut frequencies.</span>
-<span class="sd">            * freqs_to_notch : array-like | None</span>
-<span class="sd">                Array of floats of size ``(batch_size,)`` containing the center</span>
-<span class="sd">                of the frequency band to filter out for each sample in the</span>
-<span class="sd">                batch. Frequencies should be greater than</span>
-<span class="sd">                ``bandwidth/2 + transition`` and lower than</span>
-<span class="sd">                ``sfreq/2 - bandwidth/2 - transition`` (where</span>
-<span class="sd">                ``transition = 1 Hz``).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_augmentation_params</span><span class="p">(</span><span class="o">*</span><span class="n">batch</span><span class="p">)</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Prevents transitions from going below 0 and above max_freq</span>
-        <span class="n">notched_freqs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span>
-            <span class="n">low</span><span class="o">=</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
-            <span class="n">high</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_freq</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
-            <span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;sfreq&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span><span class="p">,</span>
-            <span class="s2">&quot;bandwidth&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
-            <span class="s2">&quot;freqs_to_notch&quot;</span><span class="p">:</span> <span class="n">notched_freqs</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="FrequencyShift"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift">[docs]</a><span class="k">class</span> <span class="nc">FrequencyShift</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Add a random shift in the frequency domain to all channels.</span>
-
-<span class="sd">    Note that here, the shift is the same for all channels of a single example.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        Sampling frequency of the signals to be transformed.</span>
-<span class="sd">    max_delta_freq : float | torch.Tensor, optional</span>
-<span class="sd">        Maximum shift in Hz that can be sampled (in absolute value).</span>
-<span class="sd">        Defaults to 2 (shift sampled between -2 and 2 Hz).</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">frequency_shift</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">sfreq</span><span class="p">,</span>
-        <span class="n">max_delta_freq</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sfreq</span><span class="p">,</span> <span class="n">Real</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sfreq</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span>\
-            <span class="s2">&quot;sfreq should be a positive float.&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span> <span class="o">=</span> <span class="n">sfreq</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_delta_freq</span> <span class="o">=</span> <span class="n">max_delta_freq</span>
-
-<div class="viewcode-block" id="FrequencyShift.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains</span>
-
-<span class="sd">            * delta_freq : float</span>
-<span class="sd">                The amplitude of the frequency shift (in Hz).</span>
-<span class="sd">            * sfreq : float</span>
-<span class="sd">                Sampling frequency of the signals to be transformed.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_augmentation_params</span><span class="p">(</span><span class="o">*</span><span class="n">batch</span><span class="p">)</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">u</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-            <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span>
-        <span class="p">)</span>
-        <span class="n">max_delta_freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_delta_freq</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">max_delta_freq</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-            <span class="n">max_delta_freq</span> <span class="o">=</span> <span class="n">max_delta_freq</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">delta_freq</span> <span class="o">=</span> <span class="n">u</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">max_delta_freq</span> <span class="o">-</span> <span class="n">max_delta_freq</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;delta_freq&quot;</span><span class="p">:</span> <span class="n">delta_freq</span><span class="p">,</span>
-            <span class="s2">&quot;sfreq&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-
-
-<span class="k">def</span> <span class="nf">_get_standard_10_20_positions</span><span class="p">(</span><span class="n">raw_or_epoch</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ordered_ch_names</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns standard 10-20 sensors position matrix (for instantiating</span>
-<span class="sd">    SensorsRotation for example).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    raw_or_epoch : mne.io.Raw | mne.Epoch, optional</span>
-<span class="sd">        Example of raw or epoch to retrive ordered channels list from. Need to</span>
-<span class="sd">        be named as in 10-20. By default None.</span>
-<span class="sd">    ordered_ch_names : list, optional</span>
-<span class="sd">        List of strings representing the channels of the montage considered.</span>
-<span class="sd">        The order has to be consistent with the order of channels in the input</span>
-<span class="sd">        matrices that will be fed to `SensorsRotation` transform. By</span>
-<span class="sd">        default None.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">raw_or_epoch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">ordered_ch_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span>\
-        <span class="s2">&quot;At least one of raw_or_epoch and ordered_ch_names is needed.&quot;</span>
-    <span class="k">if</span> <span class="n">ordered_ch_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">ordered_ch_names</span> <span class="o">=</span> <span class="n">raw_or_epoch</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;ch_names&#39;</span><span class="p">]</span>
-    <span class="n">ten_twenty_montage</span> <span class="o">=</span> <span class="n">make_standard_montage</span><span class="p">(</span><span class="s1">&#39;standard_1020&#39;</span><span class="p">)</span>
-    <span class="n">positions_dict</span> <span class="o">=</span> <span class="n">ten_twenty_montage</span><span class="o">.</span><span class="n">get_positions</span><span class="p">()[</span><span class="s1">&#39;ch_pos&#39;</span><span class="p">]</span>
-    <span class="n">positions_subdict</span> <span class="o">=</span> <span class="p">{</span>
-        <span class="n">k</span><span class="p">:</span> <span class="n">positions_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ordered_ch_names</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">positions_dict</span>
-    <span class="p">}</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">positions_subdict</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span><span class="o">.</span><span class="n">T</span>
-
-
-<div class="viewcode-block" id="SensorsRotation"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SensorsRotation.html#braindecode.augmentation.SensorsRotation">[docs]</a><span class="k">class</span> <span class="nc">SensorsRotation</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Interpolates EEG signals over sensors rotated around the desired axis</span>
-<span class="sd">    with an angle sampled uniformly between ``-max_degree`` and ``max_degree``.</span>
-
-<span class="sd">    Suggested in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    sensors_positions_matrix : numpy.ndarray</span>
-<span class="sd">        Matrix giving the positions of each sensor in a 3D cartesian coordinate</span>
-<span class="sd">        system. Should have shape (3, n_channels), where n_channels is the</span>
-<span class="sd">        number of channels. Standard 10-20 positions can be obtained from</span>
-<span class="sd">        `mne` through::</span>
-
-<span class="sd">         &gt;&gt;&gt; ten_twenty_montage = mne.channels.make_standard_montage(</span>
-<span class="sd">         ...    &#39;standard_1020&#39;</span>
-<span class="sd">         ... ).get_positions()[&#39;ch_pos&#39;]</span>
-
-<span class="sd">    axis : &#39;x&#39; | &#39;y&#39; | &#39;z&#39;, optional</span>
-<span class="sd">        Axis around which to rotate. Defaults to &#39;z&#39;.</span>
-<span class="sd">    max_degree : float, optional</span>
-<span class="sd">        Maximum rotation. Rotation angles will be sampled between</span>
-<span class="sd">        ``-max_degree`` and ``max_degree``. Defaults to 15 degrees.</span>
-<span class="sd">    spherical_splines : bool, optional</span>
-<span class="sd">        Whether to use spherical splines for the interpolation or not. When</span>
-<span class="sd">        ``False``, standard scipy.interpolate.Rbf (with quadratic kernel) will</span>
-<span class="sd">        be used (as in the original paper). Defaults to True.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for</span>
-<span class="sd">       electroencephalographic data. In 2017 39th Annual International</span>
-<span class="sd">       Conference of the IEEE Engineering in Medicine and Biology Society</span>
-<span class="sd">       (EMBC) (pp. 471-474).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">sensors_rotation</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">sensors_positions_matrix</span><span class="p">,</span>
-        <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;z&#39;</span><span class="p">,</span>
-        <span class="n">max_degrees</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-        <span class="n">spherical_splines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sensors_positions_matrix</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-            <span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-                <span class="n">sensors_positions_matrix</span>
-            <span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sensors_positions_matrix</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">),</span>\
-            <span class="s2">&quot;sensors_positions should be an Tensor&quot;</span>
-        <span class="k">assert</span> <span class="p">(</span>
-            <span class="nb">isinstance</span><span class="p">(</span><span class="n">max_degrees</span><span class="p">,</span> <span class="p">(</span><span class="n">Real</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">))</span> <span class="ow">and</span>
-            <span class="n">max_degrees</span> <span class="o">&gt;=</span> <span class="mi">0</span>
-        <span class="p">),</span> <span class="s2">&quot;max_degrees should be non-negative float.&quot;</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">axis</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;z&#39;</span><span class="p">],</span>\
-            <span class="s2">&quot;axis can be either x, y or z.&quot;</span>
-        <span class="k">assert</span> <span class="n">sensors_positions_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span>\
-            <span class="s2">&quot;sensors_positions_matrix shape should be 3 x n_channels.&quot;</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">spherical_splines</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span>\
-            <span class="s2">&quot;spherical_splines should be a boolean&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">sensors_positions_matrix</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">axis</span> <span class="o">=</span> <span class="n">axis</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">spherical_splines</span> <span class="o">=</span> <span class="n">spherical_splines</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_degrees</span> <span class="o">=</span> <span class="n">max_degrees</span>
-
-<div class="viewcode-block" id="SensorsRotation.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SensorsRotation.html#braindecode.augmentation.SensorsRotation.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params : dict</span>
-<span class="sd">            Contains four elements:</span>
-
-<span class="sd">            * sensors_positions_matrix : numpy.ndarray</span>
-<span class="sd">                Matrix giving the positions of each sensor in a 3D cartesian</span>
-<span class="sd">                coordinate system. Should have shape (3, n_channels), where</span>
-<span class="sd">                n_channels is the number of channels.</span>
-<span class="sd">            * axis : &#39;x&#39; | &#39;y&#39; | &#39;z&#39;</span>
-<span class="sd">                Axis around which to rotate.</span>
-<span class="sd">            * angles : array-like</span>
-<span class="sd">                Array of float of shape ``(batch_size,)`` containing the</span>
-<span class="sd">                rotation angles (in degrees) for each element of the input</span>
-<span class="sd">                batch, sampled uniformly between ``-max_degrees``and</span>
-<span class="sd">                ``max_degrees``.</span>
-<span class="sd">            * spherical_splines : bool</span>
-<span class="sd">                Whether to use spherical splines for the interpolation or not.</span>
-<span class="sd">                When ``False``, standard scipy.interpolate.Rbf (with quadratic</span>
-<span class="sd">                kernel) will be used (as in the original paper).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_augmentation_params</span><span class="p">(</span><span class="o">*</span><span class="n">batch</span><span class="p">)</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">u</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span>
-            <span class="n">low</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">high</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-            <span class="n">size</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="p">)</span>
-        <span class="n">max_degrees</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_degrees</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">max_degrees</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
-            <span class="n">max_degrees</span> <span class="o">=</span> <span class="n">max_degrees</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">random_angles</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="n">u</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">max_degrees</span> <span class="o">-</span> <span class="n">max_degrees</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;sensors_positions_matrix&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">sensors_positions_matrix</span><span class="p">,</span>
-            <span class="s2">&quot;axis&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">axis</span><span class="p">,</span>
-            <span class="s2">&quot;angles&quot;</span><span class="p">:</span> <span class="n">random_angles</span><span class="p">,</span>
-            <span class="s2">&quot;spherical_splines&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spherical_splines</span>
-        <span class="p">}</span></div></div>
-
-
-<div class="viewcode-block" id="SensorsZRotation"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SensorsZRotation.html#braindecode.augmentation.SensorsZRotation">[docs]</a><span class="k">class</span> <span class="nc">SensorsZRotation</span><span class="p">(</span><span class="n">SensorsRotation</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Interpolates EEG signals over sensors rotated around the Z axis</span>
-<span class="sd">    with an angle sampled uniformly between ``-max_degree`` and ``max_degree``.</span>
-
-<span class="sd">    Suggested in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    ordered_ch_names : list</span>
-<span class="sd">        List of strings representing the channels of the montage considered.</span>
-<span class="sd">        Has to be in standard 10-20 style. The order has to be consistent with</span>
-<span class="sd">        the order of channels in the input matrices that will be fed to the</span>
-<span class="sd">        transform. This channel will be used to compute approximate sensors</span>
-<span class="sd">        positions from a standard 10-20 montage.</span>
-<span class="sd">    max_degree : float, optional</span>
-<span class="sd">        Maximum rotation. Rotation angles will be sampled between</span>
-<span class="sd">        ``-max_degree`` and ``max_degree``. Defaults to 15 degrees.</span>
-<span class="sd">    spherical_splines : bool, optional</span>
-<span class="sd">        Whether to use spherical splines for the interpolation or not. When</span>
-<span class="sd">        ``False``, standard scipy.interpolate.Rbf (with quadratic kernel) will</span>
-<span class="sd">        be used (as in the original paper). Defaults to True.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for</span>
-<span class="sd">       electroencephalographic data. In 2017 39th Annual International</span>
-<span class="sd">       Conference of the IEEE Engineering in Medicine and Biology Society</span>
-<span class="sd">       (EMBC) (pp. 471-474).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">ordered_ch_names</span><span class="p">,</span>
-        <span class="n">max_degrees</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-        <span class="n">spherical_splines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="n">_get_standard_10_20_positions</span><span class="p">(</span><span class="n">ordered_ch_names</span><span class="o">=</span><span class="n">ordered_ch_names</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">sensors_positions_matrix</span><span class="o">=</span><span class="n">sensors_positions_matrix</span><span class="p">,</span>
-            <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;z&#39;</span><span class="p">,</span>
-            <span class="n">max_degrees</span><span class="o">=</span><span class="n">max_degrees</span><span class="p">,</span>
-            <span class="n">spherical_splines</span><span class="o">=</span><span class="n">spherical_splines</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="SensorsYRotation"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SensorsYRotation.html#braindecode.augmentation.SensorsYRotation">[docs]</a><span class="k">class</span> <span class="nc">SensorsYRotation</span><span class="p">(</span><span class="n">SensorsRotation</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Interpolates EEG signals over sensors rotated around the Y axis</span>
-<span class="sd">    with an angle sampled uniformly between ``-max_degree`` and ``max_degree``.</span>
-
-<span class="sd">    Suggested in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    ordered_ch_names : list</span>
-<span class="sd">        List of strings representing the channels of the montage considered.</span>
-<span class="sd">        Has to be in standard 10-20 style. The order has to be consistent with</span>
-<span class="sd">        the order of channels in the input matrices that will be fed to the</span>
-<span class="sd">        transform. This channel will be used to compute approximate sensors</span>
-<span class="sd">        positions from a standard 10-20 montage.</span>
-<span class="sd">    max_degree : float, optional</span>
-<span class="sd">        Maximum rotation. Rotation angles will be sampled between</span>
-<span class="sd">        ``-max_degree`` and ``max_degree``. Defaults to 15 degrees.</span>
-<span class="sd">    spherical_splines : bool, optional</span>
-<span class="sd">        Whether to use spherical splines for the interpolation or not. When</span>
-<span class="sd">        ``False``, standard scipy.interpolate.Rbf (with quadratic kernel) will</span>
-<span class="sd">        be used (as in the original paper). Defaults to True.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for</span>
-<span class="sd">       electroencephalographic data. In 2017 39th Annual International</span>
-<span class="sd">       Conference of the IEEE Engineering in Medicine and Biology Society</span>
-<span class="sd">       (EMBC) (pp. 471-474).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">ordered_ch_names</span><span class="p">,</span>
-        <span class="n">max_degrees</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-        <span class="n">spherical_splines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="n">_get_standard_10_20_positions</span><span class="p">(</span><span class="n">ordered_ch_names</span><span class="o">=</span><span class="n">ordered_ch_names</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">sensors_positions_matrix</span><span class="o">=</span><span class="n">sensors_positions_matrix</span><span class="p">,</span>
-            <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span>
-            <span class="n">max_degrees</span><span class="o">=</span><span class="n">max_degrees</span><span class="p">,</span>
-            <span class="n">spherical_splines</span><span class="o">=</span><span class="n">spherical_splines</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="SensorsXRotation"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.SensorsXRotation.html#braindecode.augmentation.SensorsXRotation">[docs]</a><span class="k">class</span> <span class="nc">SensorsXRotation</span><span class="p">(</span><span class="n">SensorsRotation</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Interpolates EEG signals over sensors rotated around the X axis</span>
-<span class="sd">    with an angle sampled uniformly between ``-max_degree`` and ``max_degree``.</span>
-
-<span class="sd">    Suggested in [1]_</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    probability : float</span>
-<span class="sd">        Float setting the probability of applying the operation.</span>
-<span class="sd">    ordered_ch_names : list</span>
-<span class="sd">        List of strings representing the channels of the montage considered.</span>
-<span class="sd">        Has to be in standard 10-20 style. The order has to be consistent with</span>
-<span class="sd">        the order of channels in the input matrices that will be fed to the</span>
-<span class="sd">        transform. This channel will be used to compute approximate sensors</span>
-<span class="sd">        positions from a standard 10-20 montage.</span>
-<span class="sd">    max_degree : float, optional</span>
-<span class="sd">        Maximum rotation. Rotation angles will be sampled between</span>
-<span class="sd">        ``-max_degree`` and ``max_degree``. Defaults to 15 degrees.</span>
-<span class="sd">    spherical_splines : bool, optional</span>
-<span class="sd">        Whether to use spherical splines for the interpolation or not. When</span>
-<span class="sd">        ``False``, standard scipy.interpolate.Rbf (with quadratic kernel) will</span>
-<span class="sd">        be used (as in the original paper). Defaults to True.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for</span>
-<span class="sd">       electroencephalographic data. In 2017 39th Annual International</span>
-<span class="sd">       Conference of the IEEE Engineering in Medicine and Biology Society</span>
-<span class="sd">       (EMBC) (pp. 471-474).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">probability</span><span class="p">,</span>
-        <span class="n">ordered_ch_names</span><span class="p">,</span>
-        <span class="n">max_degrees</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-        <span class="n">spherical_splines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="n">sensors_positions_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-            <span class="n">_get_standard_10_20_positions</span><span class="p">(</span><span class="n">ordered_ch_names</span><span class="o">=</span><span class="n">ordered_ch_names</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="n">probability</span><span class="p">,</span>
-            <span class="n">sensors_positions_matrix</span><span class="o">=</span><span class="n">sensors_positions_matrix</span><span class="p">,</span>
-            <span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span>
-            <span class="n">max_degrees</span><span class="o">=</span><span class="n">max_degrees</span><span class="p">,</span>
-            <span class="n">spherical_splines</span><span class="o">=</span><span class="n">spherical_splines</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="Mixup"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup">[docs]</a><span class="k">class</span> <span class="nc">Mixup</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Implements Iterator for Mixup for EEG data. See [1]_.</span>
-<span class="sd">    Implementation based on [2]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    alpha: float</span>
-<span class="sd">        Mixup hyperparameter.</span>
-<span class="sd">    beta_per_sample: bool (default=False)</span>
-<span class="sd">        By default, one mixing coefficient per batch is drawn from a beta</span>
-<span class="sd">        distribution. If True, one mixing coefficient per sample is drawn.</span>
-<span class="sd">    random_state: int | numpy.random.Generator, optional</span>
-<span class="sd">        Seed to be used to instantiate numpy random number generator instance.</span>
-<span class="sd">        Defaults to None.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz</span>
-<span class="sd">       (2018). mixup: Beyond Empirical Risk Minimization. In 2018</span>
-<span class="sd">       International Conference on Learning Representations (ICLR)</span>
-<span class="sd">       Online: https://arxiv.org/abs/1710.09412</span>
-<span class="sd">    .. [2] https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">operation</span> <span class="o">=</span> <span class="nb">staticmethod</span><span class="p">(</span><span class="n">mixup</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">alpha</span><span class="p">,</span>
-        <span class="n">beta_per_sample</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">probability</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>  <span class="c1"># Mixup has to be applied to whole batches</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">beta_per_sample</span> <span class="o">=</span> <span class="n">beta_per_sample</span>
-
-<div class="viewcode-block" id="Mixup.get_augmentation_params"><a class="viewcode-back" href="../../../generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup.get_augmentation_params">[docs]</a>    <span class="k">def</span> <span class="nf">get_augmentation_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return transform parameters.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : tensor.Tensor</span>
-<span class="sd">            The data.</span>
-<span class="sd">        y : tensor.Tensor</span>
-<span class="sd">            The labels.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        params: dict</span>
-<span class="sd">            Contains the values sampled uniformly between 0 and 1 setting the</span>
-<span class="sd">            linear interpolation between examples (lam) and the shuffled</span>
-<span class="sd">            indices of examples that are mixed into original examples</span>
-<span class="sd">            (idx_perm).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">device</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">device</span>
-        <span class="n">batch_size</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta_per_sample</span><span class="p">:</span>
-                <span class="n">lam</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">beta</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
-                <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">lam</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-                <span class="n">lam</span> <span class="o">*=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">beta</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">lam</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="n">idx_perm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">permutation</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,))</span>
-
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s2">&quot;lam&quot;</span><span class="p">:</span> <span class="n">lam</span><span class="p">,</span>
-            <span class="s2">&quot;idx_perm&quot;</span><span class="p">:</span> <span class="n">idx_perm</span><span class="p">,</span>
-        <span class="p">}</span></div></div>
-</pre></div>
-
-            </article>
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-              
-  <h1>Source code for braindecode.classifier</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">get_scorer</span>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <span class="n">EpochTimer</span><span class="p">,</span> <span class="n">BatchScoring</span><span class="p">,</span> <span class="n">PrintLog</span><span class="p">,</span> <span class="n">EpochScoring</span>
-<span class="kn">from</span> <span class="nn">skorch.classifier</span> <span class="kn">import</span> <span class="n">NeuralNet</span>
-<span class="kn">from</span> <span class="nn">skorch.classifier</span> <span class="kn">import</span> <span class="n">NeuralNetClassifier</span>
-<span class="kn">from</span> <span class="nn">skorch.utils</span> <span class="kn">import</span> <span class="n">train_loss_score</span><span class="p">,</span> <span class="n">valid_loss_score</span><span class="p">,</span> <span class="n">noop</span><span class="p">,</span> <span class="n">to_numpy</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">.training.scoring</span> <span class="kn">import</span> <span class="p">(</span><span class="n">PostEpochTrainScoring</span><span class="p">,</span>
-                               <span class="n">CroppedTrialEpochScoring</span><span class="p">,</span>
-                               <span class="n">CroppedTimeSeriesEpochScoring</span><span class="p">,</span>
-                               <span class="n">predict_trials</span><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">ThrowAwayIndexLoader</span><span class="p">,</span> <span class="n">update_estimator_docstring</span>
-
-
-<div class="viewcode-block" id="EEGClassifier"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier">[docs]</a><span class="k">class</span> <span class="nc">EEGClassifier</span><span class="p">(</span><span class="n">NeuralNetClassifier</span><span class="p">):</span>
-    <span class="n">doc</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;Classifier that does not assume softmax activation.</span>
-<span class="s2">    Calls loss function directly without applying log or anything.</span>
-
-<span class="s2">    Parameters</span>
-<span class="s2">    ----------</span>
-<span class="s2">    cropped: bool (default=False)</span>
-<span class="s2">        Defines whether torch model passed to this class is cropped or not.</span>
-<span class="s2">        Currently used for callbacks definition.</span>
-
-<span class="s2">    callbacks: None or list of strings or list of Callback instances (default=None)</span>
-<span class="s2">        More callbacks, in addition to those returned by</span>
-<span class="s2">        ``get_default_callbacks``. Each callback should inherit from</span>
-<span class="s2">        :class:`skorch.callbacks.Callback`. If not ``None``, callbacks can be a</span>
-<span class="s2">        list of strings specifying `sklearn` scoring functions (for scoring</span>
-<span class="s2">        functions names see: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)</span>
-<span class="s2">        or a list of callbacks where the callback names are inferred from the</span>
-<span class="s2">        class name. Name conflicts are resolved by appending a count suffix</span>
-<span class="s2">        starting with 1, e.g. ``EpochScoring_1``. Alternatively,</span>
-<span class="s2">        a tuple ``(name, callback)`` can be passed, where ``name``</span>
-<span class="s2">        should be unique. Callbacks may or may not be instantiated.</span>
-<span class="s2">        The callback name can be used to set parameters on specific</span>
-<span class="s2">        callbacks (e.g., for the callback with name ``&#39;print_log&#39;``, use</span>
-<span class="s2">        ``net.set_params(callbacks__print_log__keys_ignored=[&#39;epoch&#39;,</span>
-<span class="s2">        &#39;train_loss&#39;])``).</span>
-
-<span class="s2">    iterator_train__shuffle: bool (default=True)</span>
-<span class="s2">        Defines whether train dataset will be shuffled. As skorch does not</span>
-<span class="s2">        shuffle the train dataset by default this one overwrites this option.</span>
-
-<span class="s2">    aggregate_predictions: bool (default=True)</span>
-<span class="s2">        Whether to average cropped predictions to obtain window predictions. Used only in the</span>
-<span class="s2">        cropped mode.</span>
-
-<span class="s2">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
-    <span class="vm">__doc__</span> <span class="o">=</span> <span class="n">update_estimator_docstring</span><span class="p">(</span><span class="n">NeuralNetClassifier</span><span class="p">,</span> <span class="n">doc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">cropped</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">iterator_train__drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">aggregate_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span> <span class="o">=</span> <span class="n">cropped</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_predictions</span> <span class="o">=</span> <span class="n">aggregate_predictions</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span>
-                         <span class="n">callbacks</span><span class="o">=</span><span class="n">callbacks</span><span class="p">,</span>
-                         <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="n">iterator_train__shuffle</span><span class="p">,</span>
-                         <span class="n">iterator_train__drop_last</span><span class="o">=</span><span class="n">iterator_train__drop_last</span><span class="p">,</span>
-                         <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_yield_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="c1"># Here we parse the callbacks supplied as strings,</span>
-        <span class="c1"># e.g. &#39;accuracy&#39;, to the callbacks skorch expects</span>
-        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span><span class="p">,</span> <span class="n">named_by_user</span> <span class="ow">in</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_yield_callbacks</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;str&#39;</span><span class="p">:</span>
-                <span class="n">train_cb</span><span class="p">,</span> <span class="n">valid_cb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_str_callback</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
-                <span class="k">yield</span> <span class="n">train_cb</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">yield</span> <span class="n">valid_cb</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">yield</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span><span class="p">,</span> <span class="n">named_by_user</span>
-
-    <span class="k">def</span> <span class="nf">_parse_str_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cb_supplied_name</span><span class="p">):</span>
-        <span class="n">scoring</span> <span class="o">=</span> <span class="n">get_scorer</span><span class="p">(</span><span class="n">cb_supplied_name</span><span class="p">)</span>
-        <span class="n">scoring_name</span> <span class="o">=</span> <span class="n">scoring</span><span class="o">.</span><span class="n">_score_func</span><span class="o">.</span><span class="vm">__name__</span>
-        <span class="k">assert</span> <span class="n">scoring_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span>
-                        <span class="p">(</span><span class="s1">&#39;_score&#39;</span><span class="p">,</span> <span class="s1">&#39;_error&#39;</span><span class="p">,</span> <span class="s1">&#39;_deviance&#39;</span><span class="p">,</span> <span class="s1">&#39;_loss&#39;</span><span class="p">))</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">scoring_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;_score&#39;</span><span class="p">)</span> <span class="ow">or</span>
-                <span class="n">cb_supplied_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;neg_&#39;</span><span class="p">)):</span>
-            <span class="n">lower_is_better</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">lower_is_better</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">train_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;train_</span><span class="si">{</span><span class="n">cb_supplied_name</span><span class="si">}</span><span class="s1">&#39;</span>
-        <span class="n">valid_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;valid_</span><span class="si">{</span><span class="n">cb_supplied_name</span><span class="si">}</span><span class="s1">&#39;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span><span class="p">:</span>
-            <span class="c1"># TODO: use CroppedTimeSeriesEpochScoring when time series target</span>
-            <span class="c1"># In case of cropped decoding we are using braindecode</span>
-            <span class="c1"># specific scoring created for cropped decoding</span>
-            <span class="n">train_scoring</span> <span class="o">=</span> <span class="n">CroppedTrialEpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">train_name</span>
-            <span class="p">)</span>
-            <span class="n">valid_scoring</span> <span class="o">=</span> <span class="n">CroppedTrialEpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">valid_name</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">train_scoring</span> <span class="o">=</span> <span class="n">PostEpochTrainScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">train_name</span>
-            <span class="p">)</span>
-            <span class="n">valid_scoring</span> <span class="o">=</span> <span class="n">EpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">valid_name</span>
-            <span class="p">)</span>
-        <span class="n">named_by_user</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">train_valid_callbacks</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="p">(</span><span class="n">train_name</span><span class="p">,</span> <span class="n">train_scoring</span><span class="p">,</span> <span class="n">named_by_user</span><span class="p">),</span>
-            <span class="p">(</span><span class="n">valid_name</span><span class="p">,</span> <span class="n">valid_scoring</span><span class="p">,</span> <span class="n">named_by_user</span><span class="p">)</span>
-        <span class="p">]</span>
-        <span class="k">return</span> <span class="n">train_valid_callbacks</span>
-
-    <span class="c1"># pylint: disable=arguments-differ</span>
-<div class="viewcode-block" id="EEGClassifier.get_loss"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_loss">[docs]</a>    <span class="k">def</span> <span class="nf">get_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return the loss for this batch by calling NeuralNet get_loss.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        y_pred : torch tensor</span>
-<span class="sd">            Predicted target values</span>
-<span class="sd">        y_true : torch tensor</span>
-<span class="sd">            True target values.</span>
-<span class="sd">        X : input data, compatible with skorch.dataset.Dataset</span>
-<span class="sd">            By default, you should be able to pass:</span>
-
-<span class="sd">                * numpy arrays</span>
-<span class="sd">                * torch tensors</span>
-<span class="sd">                * pandas DataFrame or Series</span>
-<span class="sd">                * scipy sparse CSR matrices</span>
-<span class="sd">                * a dictionary of the former three</span>
-<span class="sd">                * a list/tuple of the former three</span>
-<span class="sd">                * a Dataset</span>
-
-<span class="sd">            If this doesn&#39;t work with your data, you have to pass a</span>
-<span class="sd">            ``Dataset`` that can deal with the data.</span>
-<span class="sd">        training : bool (default=False)</span>
-<span class="sd">            Whether train mode should be used or not.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        loss : float</span>
-<span class="sd">            The loss value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">NeuralNet</span><span class="o">.</span><span class="n">get_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="EEGClassifier.get_iterator"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_iterator">[docs]</a>    <span class="k">def</span> <span class="nf">get_iterator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_index</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">iterator</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">training</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">drop_index</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">ThrowAwayIndexLoader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterator</span><span class="p">,</span> <span class="n">is_regression</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">iterator</span></div>
-
-<div class="viewcode-block" id="EEGClassifier.on_batch_end"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.on_batch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_batch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># If training is false, assume that our loader has indices for this</span>
-        <span class="c1"># batch</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">training</span><span class="p">:</span>
-            <span class="n">epoch_cbs</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">callbacks_</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="p">(</span><span class="n">CroppedTrialEpochScoring</span><span class="p">,</span> <span class="n">CroppedTimeSeriesEpochScoring</span><span class="p">))</span> <span class="ow">and</span> <span class="p">(</span>
-                        <span class="nb">hasattr</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="s1">&#39;window_inds_&#39;</span><span class="p">))</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">cb</span><span class="o">.</span><span class="n">on_train</span><span class="p">):</span>
-                    <span class="n">epoch_cbs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
-            <span class="c1"># for trialwise decoding stuffs it might also be we don&#39;t have</span>
-            <span class="c1"># cropped loader, so no indices there</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">epoch_cbs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">epoch_cbs</span><span class="p">:</span>
-                    <span class="n">cb</span><span class="o">.</span><span class="n">window_inds_</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="o">=</span> <span class="kc">None</span></div>
-
-<div class="viewcode-block" id="EEGClassifier.predict_with_window_inds_and_ys"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_with_window_inds_and_ys">[docs]</a>    <span class="k">def</span> <span class="nf">predict_with_window_inds_and_ys</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">i_window_in_trials</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">i_window_stops</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">window_ys</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">drop_index</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-            <span class="n">i_window_in_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-            <span class="n">i_window_stops</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-                <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">to_numpy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">))))</span>
-            <span class="n">window_ys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
-        <span class="n">i_window_in_trials</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span>
-        <span class="n">i_window_stops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">i_window_stops</span><span class="p">)</span>
-        <span class="n">window_ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">window_ys</span><span class="p">)</span>
-        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span>
-            <span class="n">preds</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="o">=</span><span class="n">i_window_in_trials</span><span class="p">,</span>
-            <span class="n">i_window_stops</span><span class="o">=</span><span class="n">i_window_stops</span><span class="p">,</span> <span class="n">window_ys</span><span class="o">=</span><span class="n">window_ys</span><span class="p">)</span></div>
-
-    <span class="c1"># Removes default EpochScoring callback computing &#39;accuracy&#39; to work properly</span>
-    <span class="c1"># with cropped decoding.</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">_default_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">[</span>
-            <span class="p">(</span><span class="s2">&quot;epoch_timer&quot;</span><span class="p">,</span> <span class="n">EpochTimer</span><span class="p">()),</span>
-            <span class="p">(</span>
-                <span class="s2">&quot;train_loss&quot;</span><span class="p">,</span>
-                <span class="n">BatchScoring</span><span class="p">(</span>
-                    <span class="n">train_loss_score</span><span class="p">,</span>
-                    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;train_loss&quot;</span><span class="p">,</span>
-                    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="n">target_extractor</span><span class="o">=</span><span class="n">noop</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">),</span>
-            <span class="p">(</span>
-                <span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span>
-                <span class="n">BatchScoring</span><span class="p">(</span>
-                    <span class="n">valid_loss_score</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span> <span class="n">target_extractor</span><span class="o">=</span><span class="n">noop</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">),</span>
-            <span class="p">(</span><span class="s2">&quot;print_log&quot;</span><span class="p">,</span> <span class="n">PrintLog</span><span class="p">()),</span>
-        <span class="p">]</span>
-
-<div class="viewcode-block" id="EEGClassifier.predict_proba"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_proba">[docs]</a>    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return the output of the module&#39;s forward method as a numpy</span>
-<span class="sd">        array. In case of cropped decoding returns averaged values for</span>
-<span class="sd">        each trial.</span>
-
-<span class="sd">        If the module&#39;s forward method returns multiple outputs as a</span>
-<span class="sd">        tuple, it is assumed that the first output contains the</span>
-<span class="sd">        relevant information and the other values are ignored.</span>
-<span class="sd">        If all values are relevant or module&#39;s output for each crop</span>
-<span class="sd">        is needed, consider using :func:`~skorch.NeuralNet.forward`</span>
-<span class="sd">        instead.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : input data, compatible with skorch.dataset.Dataset</span>
-<span class="sd">          By default, you should be able to pass:</span>
-
-<span class="sd">            * numpy arrays</span>
-<span class="sd">            * torch tensors</span>
-<span class="sd">            * pandas DataFrame or Series</span>
-<span class="sd">            * scipy sparse CSR matrices</span>
-<span class="sd">            * a dictionary of the former three</span>
-<span class="sd">            * a list/tuple of the former three</span>
-<span class="sd">            * a Dataset</span>
-
-<span class="sd">          If this doesn&#39;t work with your data, you have to pass a</span>
-<span class="sd">          ``Dataset`` that can deal with the data.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        y_proba : numpy ndarray</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="c1"># Normally, we have to average the predictions across crops/timesteps</span>
-        <span class="c1"># to get one prediction per window/trial</span>
-        <span class="c1"># Predictions may be already averaged in CroppedTrialEpochScoring (y_pred.shape==2).</span>
-        <span class="c1"># However, when predictions are computed outside of CroppedTrialEpochScoring</span>
-        <span class="c1"># we have to average predictions, hence the check if len(y_pred.shape) == 3</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_predictions</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">y_pred</span></div>
-
-<div class="viewcode-block" id="EEGClassifier.predict"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return class labels for samples in X.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : input data, compatible with skorch.dataset.Dataset</span>
-<span class="sd">          By default, you should be able to pass:</span>
-
-<span class="sd">            * numpy arrays</span>
-<span class="sd">            * torch tensors</span>
-<span class="sd">            * pandas DataFrame or Series</span>
-<span class="sd">            * scipy sparse CSR matrices</span>
-<span class="sd">            * a dictionary of the former three</span>
-<span class="sd">            * a list/tuple of the former three</span>
-<span class="sd">            * a Dataset</span>
-
-<span class="sd">          If this doesn&#39;t work with your data, you have to pass a</span>
-<span class="sd">          ``Dataset`` that can deal with the data.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        y_pred : numpy ndarray</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="EEGClassifier.predict_trials"><a class="viewcode-back" href="../../generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_trials">[docs]</a>    <span class="k">def</span> <span class="nf">predict_trials</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Create trialwise predictions and optionally also return trialwise</span>
-<span class="sd">        labels from cropped dataset.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X: braindecode.datasets.BaseConcatDataset</span>
-<span class="sd">            A braindecode dataset to be predicted.</span>
-<span class="sd">        return_targets: bool</span>
-<span class="sd">            If True, additionally returns the trial targets.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        trial_predictions: np.ndarray</span>
-<span class="sd">            3-dimensional array (n_trials x n_classes x n_predictions), where</span>
-<span class="sd">            the number of predictions depend on the chosen window size and the</span>
-<span class="sd">            receptive field of the network.</span>
-<span class="sd">        trial_labels: np.ndarray</span>
-<span class="sd">            2-dimensional array (n_trials x n_targets) where the number of</span>
-<span class="sd">            targets depends on the decoding paradigm and can be either a single</span>
-<span class="sd">            value, multiple values, or a sequence.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                <span class="s2">&quot;This method was designed to predict trials in cropped mode. &quot;</span>
-                <span class="s2">&quot;Calling it when cropped is False will give the same result as &quot;</span>
-                <span class="s2">&quot;&#39;.predict&#39;.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-            <span class="n">preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">return_targets</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">preds</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">()[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-            <span class="k">return</span> <span class="n">preds</span>
-        <span class="k">return</span> <span class="n">predict_trials</span><span class="p">(</span>
-            <span class="n">module</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">,</span>
-            <span class="n">dataset</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
-            <span class="n">return_targets</span><span class="o">=</span><span class="n">return_targets</span><span class="p">,</span>
-            <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
-            <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">loader</span><span class="o">.</span><span class="n">num_workers</span><span class="p">,</span>
-        <span class="p">)</span></div></div>
-</pre></div>
-
-            </article>
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-  <h1>Source code for braindecode.datasets.base</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Dataset classes.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#          David Sabbagh &lt;dav.sabbagh@gmail.com&gt;</span>
-<span class="c1">#          Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">json</span>
-<span class="kn">import</span> <span class="nn">shutil</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">ConcatDataset</span>
-
-
-<span class="k">def</span> <span class="nf">_create_description</span><span class="p">(</span><span class="n">description</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">description</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">description</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">)</span> <span class="ow">and</span>
-                <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">description</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">description</span><span class="si">}</span><span class="s2">&#39; has to be either a &quot;</span>
-                             <span class="sa">f</span><span class="s2">&quot;pandas.Series or a dict.&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">description</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-            <span class="n">description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">description</span>
-
-
-<div class="viewcode-block" id="BaseDataset"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset">[docs]</a><span class="k">class</span> <span class="nc">BaseDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns samples from an mne.io.Raw object along with a target.</span>
-
-<span class="sd">    Dataset which serves samples from an mne.io.Raw object along with a target.</span>
-<span class="sd">    The target is unique for the dataset, and is obtained through the</span>
-<span class="sd">    `description` attribute.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    raw : mne.io.Raw</span>
-<span class="sd">        Continuous data.</span>
-<span class="sd">    description : dict | pandas.Series | None</span>
-<span class="sd">        Holds additional description about the continuous signal / subject.</span>
-<span class="sd">    target_name : str | tuple | None</span>
-<span class="sd">        Name(s) of the index in `description` that should be used to provide the</span>
-<span class="sd">        target (e.g., to be used in a prediction task later on).</span>
-<span class="sd">    transform : callable | None</span>
-<span class="sd">        On-the-fly transform applied to the example before it is returned.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">raw</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">raw</span> <span class="o">=</span> <span class="n">raw</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="o">=</span> <span class="n">_create_description</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-
-        <span class="c1"># save target name for load/save later</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_name</span><span class="p">(</span><span class="n">target_name</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">raw</span><span class="p">[:,</span> <span class="n">index</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_name</span><span class="p">]</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">raw</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span>
-
-    <span class="nd">@transform</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Transform needs to be a callable.&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span>
-
-<div class="viewcode-block" id="BaseDataset.set_description"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset.set_description">[docs]</a>    <span class="k">def</span> <span class="nf">set_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Update (add or overwrite) the dataset description.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        description: dict | pd.Series</span>
-<span class="sd">            Description in the form key: value.</span>
-<span class="sd">        overwrite: bool</span>
-<span class="sd">            Has to be True if a key in description already exists in the</span>
-<span class="sd">            dataset description.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">description</span> <span class="o">=</span> <span class="n">_create_description</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">description</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># if the key is already in the existing description, drop it</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="n">overwrite</span><span class="p">,</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&#39; already in description. Please &quot;</span>
-                                   <span class="sa">f</span><span class="s2">&quot;rename or set overwrite to True.&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_description</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="o">=</span> <span class="n">description</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="p">,</span> <span class="n">description</span><span class="p">])</span></div>
-
-    <span class="k">def</span> <span class="nf">_target_name</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_name</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">target_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target_name</span><span class="p">,</span> <span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;target_name has to be None, str, tuple or list&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">target_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">target_name</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># convert tuple of names or single name to list</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target_name</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
-                <span class="n">target_name</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">target_name</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target_name</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-                <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target_name</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-                <span class="n">target_name</span> <span class="o">=</span> <span class="p">[</span><span class="n">target_name</span><span class="p">]</span>
-            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target_name</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span>
-            <span class="c1"># check if target name(s) can be read from description</span>
-            <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">target_name</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="p">:</span>
-                    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; not in description. &#39;__getitem__&#39;&quot;</span>
-                                  <span class="sa">f</span><span class="s2">&quot;will fail unless an appropriate target is&quot;</span>
-                                  <span class="sa">f</span><span class="s2">&quot; added to description.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="c1"># return a list of str if there are multiple targets and a str otherwise</span>
-        <span class="k">return</span> <span class="n">target_name</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">target_name</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">target_name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="WindowsDataset"><a class="viewcode-back" href="../../../generated/braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset">[docs]</a><span class="k">class</span> <span class="nc">WindowsDataset</span><span class="p">(</span><span class="n">BaseDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns windows from an mne.Epochs object along with a target.</span>
-
-<span class="sd">    Dataset which serves windows from an mne.Epochs object along with their</span>
-<span class="sd">    target and additional information. The `metadata` attribute of the Epochs</span>
-<span class="sd">    object must contain a column called `target`, which will be used to return</span>
-<span class="sd">    the target that corresponds to a window. Additional columns</span>
-<span class="sd">    `i_window_in_trial`, `i_start_in_trial`, `i_stop_in_trial` are also</span>
-<span class="sd">    required to serve information about the windowing (e.g., useful for cropped</span>
-<span class="sd">    training).</span>
-<span class="sd">    See `braindecode.datautil.windowers` to directly create a `WindowsDataset`</span>
-<span class="sd">    from a `BaseDataset` object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    windows : mne.Epochs</span>
-<span class="sd">        Windows obtained through the application of a windower to a BaseDataset</span>
-<span class="sd">        (see `braindecode.datautil.windowers`).</span>
-<span class="sd">    description : dict | pandas.Series | None</span>
-<span class="sd">        Holds additional info about the windows.</span>
-<span class="sd">    transform : callable | None</span>
-<span class="sd">        On-the-fly transform applied to a window before it is returned.</span>
-<span class="sd">    targets_from : str</span>
-<span class="sd">        Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs `misc`</span>
-<span class="sd">        channels (time series targets). It can be `metadata` (default) or `channels`.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">windows</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;metadata&#39;</span><span class="p">,</span>
-                 <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">windows</span> <span class="o">=</span> <span class="n">windows</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="o">=</span> <span class="n">_create_description</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">last_target_only</span> <span class="o">=</span> <span class="n">last_target_only</span>
-        <span class="k">if</span> <span class="n">targets_from</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;metadata&#39;</span><span class="p">,</span> <span class="s1">&#39;channels&#39;</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Wrong value for parameter `targets_from`.&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">targets_from</span> <span class="o">=</span> <span class="n">targets_from</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">crop_inds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-            <span class="p">:,</span> <span class="p">[</span><span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">,</span> <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">,</span>
-                <span class="s1">&#39;i_stop_in_trial&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">targets_from</span> <span class="o">==</span> <span class="s1">&#39;metadata&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Get a window and its target.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        index : int</span>
-<span class="sd">            Index to the window (and target) to return.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        np.ndarray</span>
-<span class="sd">            Window of shape (n_channels, n_times).</span>
-<span class="sd">        int</span>
-<span class="sd">            Target for the windows.</span>
-<span class="sd">        np.ndarray</span>
-<span class="sd">            Crop indices.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">item</span><span class="o">=</span><span class="n">index</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">targets_from</span> <span class="o">==</span> <span class="s1">&#39;metadata&#39;</span><span class="p">:</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">misc_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_channel_types</span><span class="p">())</span> <span class="o">==</span> <span class="s1">&#39;misc&#39;</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_target_only</span><span class="p">:</span>
-                <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">misc_mask</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">misc_mask</span><span class="p">,</span> <span class="p">:]</span>
-            <span class="c1"># remove the target channels from raw</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="o">~</span><span class="n">misc_mask</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="c1"># necessary to cast as list to get list of three tensors from batch,</span>
-        <span class="c1"># otherwise get single 2d-tensor...</span>
-        <span class="n">crop_inds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">crop_inds</span><span class="p">[</span><span class="n">index</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">crop_inds</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">events</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span>
-
-    <span class="nd">@transform</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">callable</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Transform needs to be a callable.&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_transform</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span>
-
-<div class="viewcode-block" id="WindowsDataset.set_description"><a class="viewcode-back" href="../../../generated/braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset.set_description">[docs]</a>    <span class="k">def</span> <span class="nf">set_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Update (add or overwrite) the dataset description.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        description: dict | pd.Series</span>
-<span class="sd">            Description in the form key: value.</span>
-<span class="sd">        overwrite: bool</span>
-<span class="sd">            Has to be True if a key in description already exists in the</span>
-<span class="sd">            dataset description.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">description</span> <span class="o">=</span> <span class="n">_create_description</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">description</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="c1"># if they key is already in the existing description, drop it</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_description</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="n">overwrite</span><span class="p">,</span> <span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&#39;</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&#39; already in description. Please &quot;</span>
-                                   <span class="sa">f</span><span class="s2">&quot;rename or set overwrite to True.&quot;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_description</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="p">,</span> <span class="n">description</span><span class="p">])</span></div></div>
-
-
-<div class="viewcode-block" id="BaseConcatDataset"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset">[docs]</a><span class="k">class</span> <span class="nc">BaseConcatDataset</span><span class="p">(</span><span class="n">ConcatDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A base class for concatenated datasets. Holds either mne.Raw or</span>
-<span class="sd">    mne.Epoch in self.datasets and has a pandas DataFrame with additional</span>
-<span class="sd">    description.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    list_of_ds : list</span>
-<span class="sd">        list of BaseDataset, BaseConcatDataset or WindowsDataset</span>
-<span class="sd">    target_transform : callable | None</span>
-<span class="sd">        Optional function to call on targets before returning them.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">list_of_ds</span><span class="p">,</span> <span class="n">target_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="c1"># if we get a list of BaseConcatDataset, get all the individual datasets</span>
-        <span class="k">if</span> <span class="n">list_of_ds</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">list_of_ds</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">BaseConcatDataset</span><span class="p">):</span>
-            <span class="n">list_of_ds</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">list_of_ds</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">]</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">list_of_ds</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">target_transform</span>
-
-    <span class="k">def</span> <span class="nf">_get_sequence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(),</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
-            <span class="n">out_i</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span>
-            <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_i</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-
-        <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
-
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        idx : int | list</span>
-<span class="sd">            Index of window and target to return. If provided as a list of</span>
-<span class="sd">            ints, multiple windows and targets will be extracted and</span>
-<span class="sd">            concatenated. The target output can be modified on the</span>
-<span class="sd">            fly by the ``traget_transform`` parameter.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>  <span class="c1"># Sample multiple windows</span>
-            <span class="n">item</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_sequence</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">item</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">item</span> <span class="o">=</span> <span class="n">item</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_transform</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]),)</span> <span class="o">+</span> <span class="n">item</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span>
-        <span class="k">return</span> <span class="n">item</span>
-
-<div class="viewcode-block" id="BaseConcatDataset.split"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split">[docs]</a>    <span class="k">def</span> <span class="nf">split</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">by</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="nb">property</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">split_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Split the dataset based on information listed in its description</span>
-<span class="sd">        DataFrame or based on indices.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        by : str | list | dict</span>
-<span class="sd">            If ``by`` is a string, splitting is performed based on the</span>
-<span class="sd">            description DataFrame column with this name.</span>
-<span class="sd">            If ``by`` is a (list of) list of integers, the position in the first</span>
-<span class="sd">            list corresponds to the split id and the integers to the</span>
-<span class="sd">            datapoints of that split.</span>
-<span class="sd">            If a dict then each key will be used in the returned</span>
-<span class="sd">            splits dict and each value should be a list of int.</span>
-<span class="sd">        property : str</span>
-<span class="sd">            Some property which is listed in info DataFrame.</span>
-<span class="sd">        split_ids : list | dict</span>
-<span class="sd">            List of indices to be combined in a subset.</span>
-<span class="sd">            It can be a list of int or a list of list of int.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        splits : dict</span>
-<span class="sd">            A dictionary with the name of the split (a string) as key and the</span>
-<span class="sd">            dataset as value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">args_not_none</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">by</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="nb">property</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="n">split_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
-        <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">args_not_none</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Splitting requires exactly one argument.&quot;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">property</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">split_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Keyword arguments `property` and `split_ids` &quot;</span>
-                          <span class="s2">&quot;are deprecated and will be removed in the future. &quot;</span>
-                          <span class="s2">&quot;Use `by` instead.&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">)</span>
-            <span class="n">by</span> <span class="o">=</span> <span class="nb">property</span> <span class="k">if</span> <span class="nb">property</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">split_ids</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">by</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="n">split_ids</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="n">k</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">by</span><span class="p">)</span><span class="o">.</span><span class="n">groups</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="p">}</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">by</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
-            <span class="n">split_ids</span> <span class="o">=</span> <span class="n">by</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># assume list(int)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">by</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
-                <span class="n">by</span> <span class="o">=</span> <span class="p">[</span><span class="n">by</span><span class="p">]</span>
-            <span class="c1"># assume list(list(int))</span>
-            <span class="n">split_ids</span> <span class="o">=</span> <span class="p">{</span><span class="n">split_i</span><span class="p">:</span> <span class="n">split</span> <span class="k">for</span> <span class="n">split_i</span><span class="p">,</span> <span class="n">split</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">by</span><span class="p">)}</span>
-
-        <span class="k">return</span> <span class="p">{</span><span class="nb">str</span><span class="p">(</span><span class="n">split_name</span><span class="p">):</span> <span class="n">BaseConcatDataset</span><span class="p">(</span>
-            <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">ds_ind</span><span class="p">]</span> <span class="k">for</span> <span class="n">ds_ind</span> <span class="ow">in</span> <span class="n">ds_inds</span><span class="p">],</span> <span class="n">target_transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_transform</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">split_name</span><span class="p">,</span> <span class="n">ds_inds</span> <span class="ow">in</span> <span class="n">split_ids</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span></div>
-
-<div class="viewcode-block" id="BaseConcatDataset.get_metadata"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata">[docs]</a>    <span class="k">def</span> <span class="nf">get_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Concatenate the metadata and description of the wrapped Epochs.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        metadata : pd.DataFrame</span>
-<span class="sd">            DataFrame containing as many rows as there are windows in the</span>
-<span class="sd">            BaseConcatDataset, with the metadata and description information</span>
-<span class="sd">            for each window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">([</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">WindowsDataset</span><span class="p">)</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">]):</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;Metadata dataframe can only be computed when all &#39;</span>
-                            <span class="s1">&#39;datasets are WindowsDataset.&#39;</span><span class="p">)</span>
-
-        <span class="n">all_dfs</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
-            <span class="n">df</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">metadata</span>
-            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
-            <span class="n">all_dfs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">all_dfs</span><span class="p">)</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">[</span><span class="n">ds</span><span class="o">.</span><span class="n">transform</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">]</span>
-
-    <span class="nd">@transform</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">)):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">fn</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">target_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_target_transform</span>
-
-    <span class="nd">@target_transform</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">target_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fn</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">callable</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span> <span class="ow">or</span> <span class="n">fn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;target_transform must be a callable.&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_target_transform</span> <span class="o">=</span> <span class="n">fn</span>
-
-    <span class="k">def</span> <span class="nf">_outdated_save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;This is a copy of the old saving function, that had inconsistent</span>
-<span class="sd">        functionality for BaseDataset and WindowsDataset. It only exists to</span>
-<span class="sd">        assure backwards compatibility by still being able to run the old tests.</span>
-
-<span class="sd">        Save dataset to files.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        path : str</span>
-<span class="sd">            Directory to which .fif / -epo.fif and .json files are stored.</span>
-<span class="sd">        overwrite : bool</span>
-<span class="sd">            Whether to delete old files (.json, .fif, -epo.fif) in specified</span>
-<span class="sd">            directory prior to saving.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;This function only exists for backwards compatibility &#39;</span>
-                      <span class="s1">&#39;purposes. DO NOT USE!&#39;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expect at least one dataset&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;raw&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">hasattr</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;windows&#39;</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset should have either raw or windows &quot;</span>
-                             <span class="s2">&quot;attribute&quot;</span><span class="p">)</span>
-        <span class="n">file_name_templates</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">-raw.fif&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">-epo.fif&quot;</span><span class="p">]</span>
-        <span class="n">description_file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;description.json&#39;</span><span class="p">)</span>
-        <span class="n">target_file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;target_name.json&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">overwrite</span><span class="p">:</span>
-            <span class="kn">from</span> <span class="nn">braindecode.datautil.serialization</span> <span class="kn">import</span> \
-                <span class="n">_check_save_dir_empty</span>  <span class="c1"># Import here to avoid circular import</span>
-            <span class="n">_check_save_dir_empty</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">file_name_template</span> <span class="ow">in</span> <span class="n">file_name_templates</span><span class="p">:</span>
-                <span class="n">file_names</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-                    <span class="n">path</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;*</span><span class="si">{</span><span class="n">file_name_template</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span>
-                <span class="n">_</span> <span class="o">=</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">file_names</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">target_file_name</span><span class="p">):</span>
-                <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">target_file_name</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">description_file_name</span><span class="p">):</span>
-                <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">description_file_name</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">kwarg_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span>
-                               <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]:</span>
-                <span class="n">kwarg_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">kwarg_name</span><span class="p">,</span> <span class="s1">&#39;json&#39;</span><span class="p">]))</span>
-                <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">kwarg_path</span><span class="p">):</span>
-                    <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">kwarg_path</span><span class="p">)</span>
-
-        <span class="n">is_raw</span> <span class="o">=</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;raw&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-            <span class="n">file_name_template</span> <span class="o">=</span> <span class="n">file_name_templates</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="c1"># for checks that all have same target name and for</span>
-            <span class="c1"># saving later</span>
-            <span class="n">target_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">target_name</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">file_name_template</span> <span class="o">=</span> <span class="n">file_name_templates</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">i_ds</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
-            <span class="n">full_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">file_name_template</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_ds</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-                <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">full_file_path</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">)</span>
-                <span class="k">assert</span> <span class="n">ds</span><span class="o">.</span><span class="n">target_name</span> <span class="o">==</span> <span class="n">target_name</span><span class="p">,</span> <span class="p">(</span>
-                    <span class="s2">&quot;All datasets should have same target name&quot;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">ds</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">full_file_path</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-            <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="s1">&#39;target_name&#39;</span><span class="p">:</span> <span class="n">target_name</span><span class="p">},</span> <span class="nb">open</span><span class="p">(</span><span class="n">target_file_name</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">description_file_name</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">kwarg_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span>
-                           <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kwarg_name</span><span class="p">):</span>
-                <span class="n">kwargs_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">kwarg_name</span><span class="p">,</span> <span class="s1">&#39;json&#39;</span><span class="p">]))</span>
-                <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kwarg_name</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">kwargs</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">kwargs_path</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">))</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">description</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">description</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">])</span>
-        <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">df</span>
-
-<div class="viewcode-block" id="BaseConcatDataset.set_description"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.set_description">[docs]</a>    <span class="k">def</span> <span class="nf">set_description</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Update (add or overwrite) the dataset description.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        description: dict | pd.DataFrame</span>
-<span class="sd">            Description in the form key: value where the length of the value</span>
-<span class="sd">            has to match the number of datasets.</span>
-<span class="sd">        overwrite: bool</span>
-<span class="sd">            Has to be True if a key in description already exists in the</span>
-<span class="sd">            dataset description.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">description</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">description</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">ds</span><span class="p">,</span> <span class="n">value_</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-                <span class="n">ds</span><span class="o">.</span><span class="n">set_description</span><span class="p">({</span><span class="n">key</span><span class="p">:</span> <span class="n">value_</span><span class="p">},</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="BaseConcatDataset.save"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save">[docs]</a>    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Save datasets to files by creating one subdirectory for each dataset:</span>
-<span class="sd">        path/</span>
-<span class="sd">            0/</span>
-<span class="sd">                0-raw.fif | 0-epo.fif</span>
-<span class="sd">                description.json</span>
-<span class="sd">                raw_preproc_kwargs.json (if raws were preprocessed)</span>
-<span class="sd">                window_kwargs.json (if this is a windowed dataset)</span>
-<span class="sd">                window_preproc_kwargs.json  (if windows were preprocessed)</span>
-<span class="sd">                target_name.json (if target_name is not None and dataset is raw)</span>
-<span class="sd">            1/</span>
-<span class="sd">                1-raw.fif | 1-epo.fif</span>
-<span class="sd">                description.json</span>
-<span class="sd">                raw_preproc_kwargs.json (if raws were preprocessed)</span>
-<span class="sd">                window_kwargs.json (if this is a windowed dataset)</span>
-<span class="sd">                window_preproc_kwargs.json  (if windows were preprocessed)</span>
-<span class="sd">                target_name.json (if target_name is not None and dataset is raw)</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        path : str</span>
-<span class="sd">            Directory in which subdirectories are created to store</span>
-<span class="sd">             -raw.fif | -epo.fif and .json files to.</span>
-<span class="sd">        overwrite : bool</span>
-<span class="sd">            Whether to delete old subdirectories that will be saved to in this</span>
-<span class="sd">            call.</span>
-<span class="sd">        offset : int</span>
-<span class="sd">            If provided, the integer is added to the id of the dataset in the</span>
-<span class="sd">            concat. This is useful in the setting of very large datasets, where</span>
-<span class="sd">            one dataset has to be processed and saved at a time to account for</span>
-<span class="sd">            its original position.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Expect at least one dataset&quot;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;raw&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">hasattr</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;windows&#39;</span><span class="p">)):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataset should have either raw or windows &quot;</span>
-                             <span class="s2">&quot;attribute&quot;</span><span class="p">)</span>
-        <span class="n">path_contents</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="n">n_sub_dirs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">([</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">path_contents</span><span class="p">])</span>
-        <span class="k">for</span> <span class="n">i_ds</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
-            <span class="c1"># remove subdirectory from list of untouched files / subdirectories</span>
-            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">i_ds</span> <span class="o">+</span> <span class="n">offset</span><span class="p">)</span> <span class="ow">in</span> <span class="n">path_contents</span><span class="p">:</span>
-                <span class="n">path_contents</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i_ds</span> <span class="o">+</span> <span class="n">offset</span><span class="p">))</span>
-            <span class="c1"># save_dir/i_ds/</span>
-            <span class="n">sub_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">i_ds</span> <span class="o">+</span> <span class="n">offset</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">overwrite</span><span class="p">:</span>
-                    <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">FileExistsError</span><span class="p">(</span>
-                        <span class="sa">f</span><span class="s1">&#39;Subdirectory </span><span class="si">{</span><span class="n">sub_dir</span><span class="si">}</span><span class="s1"> already exists. Please select&#39;</span>
-                        <span class="sa">f</span><span class="s1">&#39; a different directory, set overwrite=True, or &#39;</span>
-                        <span class="sa">f</span><span class="s1">&#39;resolve manually.&#39;</span><span class="p">)</span>
-            <span class="c1"># save_dir/{i_ds+offset}/</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">)</span>
-            <span class="c1"># save_dir/{i_ds+offset}/{i_ds+offset}-{raw_or_epo}.fif</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_save_signals</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">,</span> <span class="n">i_ds</span><span class="p">,</span> <span class="n">offset</span><span class="p">)</span>
-            <span class="c1"># save_dir/{i_ds+offset}/description.json</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_save_description</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">)</span>
-            <span class="c1"># save_dir/{i_ds+offset}/raw_preproc_kwargs.json</span>
-            <span class="c1"># save_dir/{i_ds+offset}/window_kwargs.json</span>
-            <span class="c1"># save_dir/{i_ds+offset}/window_preproc_kwargs.json</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_save_kwargs</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">)</span>
-            <span class="c1"># save_dir/{i_ds+offset}/target_name.json</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_save_target_name</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">overwrite</span><span class="p">:</span>
-            <span class="c1"># the following will be True for all datasets preprocessed and</span>
-            <span class="c1"># stored in parallel with braindecode.preprocessing.preprocess</span>
-            <span class="k">if</span> <span class="n">i_ds</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">offset</span> <span class="o">&lt;</span> <span class="n">n_sub_dirs</span><span class="p">:</span>
-                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The number of saved datasets (</span><span class="si">{</span><span class="n">i_ds</span><span class="o">+</span><span class="mi">1</span><span class="o">+</span><span class="n">offset</span><span class="si">}</span><span class="s2">) &quot;</span>
-                              <span class="sa">f</span><span class="s2">&quot;does not match the number of existing &quot;</span>
-                              <span class="sa">f</span><span class="s2">&quot;subdirectories (</span><span class="si">{</span><span class="n">n_sub_dirs</span><span class="si">}</span><span class="s2">). You may now &quot;</span>
-                              <span class="sa">f</span><span class="s2">&quot;encounter a mix of differently preprocessed &quot;</span>
-                              <span class="sa">f</span><span class="s2">&quot;datasets!&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="c1"># if path contains files or directories that were not touched, raise</span>
-        <span class="c1"># warning</span>
-        <span class="k">if</span> <span class="n">path_contents</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Chosen directory </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1"> contains other &#39;</span>
-                          <span class="sa">f</span><span class="s1">&#39;subdirectories or files </span><span class="si">{</span><span class="n">path_contents</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span></div>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_save_signals</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">,</span> <span class="n">i_ds</span><span class="p">,</span> <span class="n">offset</span><span class="p">):</span>
-        <span class="n">raw_or_epo</span> <span class="o">=</span> <span class="s1">&#39;raw&#39;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="s1">&#39;raw&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;epo&#39;</span>
-        <span class="n">fif_file_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">i_ds</span> <span class="o">+</span> <span class="n">offset</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">raw_or_epo</span><span class="si">}</span><span class="s1">.fif&#39;</span>
-        <span class="n">fif_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">fif_file_name</span><span class="p">)</span>
-        <span class="n">raw_or_epo</span> <span class="o">=</span> <span class="s1">&#39;raw&#39;</span> <span class="k">if</span> <span class="n">raw_or_epo</span> <span class="o">==</span> <span class="s1">&#39;raw&#39;</span> <span class="k">else</span> <span class="s1">&#39;windows&#39;</span>
-
-        <span class="c1"># The following appears to be necessary to avoid a CI failure when</span>
-        <span class="c1"># preprocessing WindowsDatasets with serialization enabled. The failure</span>
-        <span class="c1"># comes from `mne.epochs._check_consistency` which ensures the Epochs&#39;s</span>
-        <span class="c1"># object `times` attribute is not writeable.</span>
-        <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">raw_or_epo</span><span class="p">)</span><span class="o">.</span><span class="n">times</span><span class="o">.</span><span class="n">flags</span><span class="p">[</span><span class="s1">&#39;WRITEABLE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-
-        <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">raw_or_epo</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">fif_file_path</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_save_description</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">description</span><span class="p">):</span>
-        <span class="n">description_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="s1">&#39;description.json&#39;</span><span class="p">)</span>
-        <span class="n">description</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span><span class="n">description_file_path</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_save_kwargs</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">kwargs_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]:</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">):</span>
-                <span class="n">kwargs_file_name</span> <span class="o">=</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">kwargs_name</span><span class="p">,</span> <span class="s1">&#39;json&#39;</span><span class="p">])</span>
-                <span class="n">kwargs_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">kwargs_file_name</span><span class="p">)</span>
-                <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">kwargs_file_path</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                        <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">kwargs</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_save_target_name</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">ds</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="s1">&#39;target_name&#39;</span><span class="p">):</span>
-            <span class="n">target_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="s1">&#39;target_name.json&#39;</span><span class="p">)</span>
-            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">target_file_path</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="s1">&#39;target_name&#39;</span><span class="p">:</span> <span class="n">ds</span><span class="o">.</span><span class="n">target_name</span><span class="p">},</span> <span class="n">f</span><span class="p">)</span></div>
-</pre></div>
-
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-            
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diff --git a/stable/_modules/braindecode/datasets/bcicomp.html b/stable/_modules/braindecode/datasets/bcicomp.html
deleted file mode 100644
index b5aeea97..00000000
--- a/stable/_modules/braindecode/datasets/bcicomp.html
+++ /dev/null
@@ -1,607 +0,0 @@
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-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
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-<form class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
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-  <h1>Source code for braindecode.datasets.bcicomp</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">glob</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">os.path</span> <span class="k">as</span> <span class="nn">osp</span>
-<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">remove</span>
-<span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">unpack_archive</span>
-
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">mne.utils</span> <span class="kn">import</span> <span class="n">verbose</span>
-<span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <span class="n">loadmat</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-
-<span class="n">DATASET_URL</span> <span class="o">=</span> <span class="s1">&#39;https://stacks.stanford.edu/file/druid:zk881ps0522/&#39;</span> \
-              <span class="s1">&#39;BCI_Competion4_dataset4_data_fingerflexions.zip&#39;</span>
-
-
-<div class="viewcode-block" id="BCICompetitionIVDataset4"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4">[docs]</a><span class="k">class</span> <span class="nc">BCICompetitionIVDataset4</span><span class="p">(</span><span class="n">BaseConcatDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;BCI competition IV dataset 4.</span>
-
-<span class="sd">    Contains ECoG recordings for three patients moving fingers during the experiment.</span>
-<span class="sd">    Targets correspond to the time courses of the flexion of each of five fingers.</span>
-<span class="sd">    See http://www.bbci.de/competition/iv/desc_4.pdf and</span>
-<span class="sd">    http://www.bbci.de/competition/iv/ for the dataset and competition description.</span>
-<span class="sd">    ECoG library containing the dataset: https://searchworks.stanford.edu/view/zk881ps0522</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    When using this dataset please cite [1]_ .</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    subject_ids : list(int) | int | None</span>
-<span class="sd">        (list of) int of subject(s) to be loaded. If None, load all available</span>
-<span class="sd">        subjects. Should be in range 1-3.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Miller, Kai J. &quot;A library of human electrocorticographic data and analyses.&quot;</span>
-<span class="sd">    Nature human behaviour 3, no. 11 (2019): 1225-1235. https://doi.org/10.1038/s41562-019-0678-3</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">possible_subjects</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="n">data_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">download</span><span class="p">()</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subject_ids</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="n">subject_ids</span> <span class="o">=</span> <span class="p">[</span><span class="n">subject_ids</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">subject_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">subject_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">possible_subjects</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_validate_subjects</span><span class="p">(</span><span class="n">subject_ids</span><span class="p">)</span>
-        <span class="n">files_list</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">data_path</span><span class="si">}</span><span class="s1">/sub</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">_comp.mat&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">subject_ids</span><span class="p">]</span>
-        <span class="n">datasets</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="n">files_list</span><span class="p">:</span>
-            <span class="n">raw_train</span><span class="p">,</span> <span class="n">raw_test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_data_to_mne</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-            <span class="n">desc_train</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
-                <span class="n">subject</span><span class="o">=</span><span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;sub&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="n">file_name</span><span class="o">=</span><span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
-                <span class="n">session</span><span class="o">=</span><span class="s1">&#39;train&#39;</span>
-            <span class="p">)</span>
-            <span class="n">desc_test</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
-                <span class="n">subject</span><span class="o">=</span><span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;sub&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
-                <span class="n">file_name</span><span class="o">=</span><span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
-                <span class="n">session</span><span class="o">=</span><span class="s1">&#39;test&#39;</span>
-            <span class="p">)</span>
-            <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw_train</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="n">desc_train</span><span class="p">))</span>
-            <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw_test</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="n">desc_test</span><span class="p">))</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
-
-<div class="viewcode-block" id="BCICompetitionIVDataset4.download"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4.download">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Download the dataset.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        path  (None | str) – Location of where to look for the data storing location.</span>
-<span class="sd">        If None, the environment variable or config parameter</span>
-<span class="sd">        MNE_DATASETS_(dataset)_PATH is used. If it doesn’t exist, the “~/mne_data”</span>
-<span class="sd">        directory is used. If the dataset is not found under the given path, the data</span>
-<span class="sd">        will be automatically downloaded to the specified folder.</span>
-<span class="sd">        force_update (bool) – Force update of the dataset even if a local copy exists.</span>
-<span class="sd">        verbose (bool, str, int, or None) – If not None, override default verbose level</span>
-<span class="sd">        (see mne.verbose())</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">signature</span> <span class="o">=</span> <span class="s1">&#39;BCICompetitionIVDataset4&#39;</span>
-        <span class="n">folder_name</span> <span class="o">=</span> <span class="s1">&#39;BCI_Competion4_dataset4_data_fingerflexions&#39;</span>
-        <span class="c1"># Check if the dataset already exists (unpacked). We have to do that manually</span>
-        <span class="c1"># because we are removing .zip file from disk to save disk space.</span>
-
-        <span class="kn">from</span> <span class="nn">moabb.datasets.download</span> <span class="kn">import</span> <span class="n">get_dataset_path</span>  <span class="c1"># keep soft depenency</span>
-        <span class="n">path</span> <span class="o">=</span> <span class="n">get_dataset_path</span><span class="p">(</span><span class="n">signature</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
-        <span class="n">key_dest</span> <span class="o">=</span> <span class="s2">&quot;MNE-</span><span class="si">{:s}</span><span class="s2">-data&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">signature</span><span class="o">.</span><span class="n">lower</span><span class="p">())</span>
-        <span class="c1"># We do not use mne _url_to_local_path due to &#39;:&#39; in the url that causes problems on Windows</span>
-        <span class="n">destination</span> <span class="o">=</span> <span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">key_dest</span><span class="p">,</span> <span class="n">folder_name</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="s1">&#39;*.mat&#39;</span><span class="p">))))</span> <span class="o">==</span> <span class="mi">6</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">destination</span>
-        <span class="n">data_path</span> <span class="o">=</span> <span class="n">_data_dl</span><span class="p">(</span><span class="n">DATASET_URL</span><span class="p">,</span> <span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">destination</span><span class="p">,</span> <span class="n">folder_name</span><span class="p">,</span> <span class="n">signature</span><span class="p">),</span>
-                             <span class="n">force_update</span><span class="o">=</span><span class="n">force_update</span><span class="p">)</span>
-        <span class="n">unpack_archive</span><span class="p">(</span><span class="n">data_path</span><span class="p">,</span> <span class="n">osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">destination</span><span class="p">))</span>
-        <span class="c1"># removes .zip file that the data was unpacked from</span>
-        <span class="n">remove</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">destination</span></div>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_prepare_targets</span><span class="p">(</span><span class="n">upsampled_targets</span><span class="p">,</span> <span class="n">targets_stride</span><span class="p">):</span>
-        <span class="n">original_targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="n">upsampled_targets</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">original_targets</span><span class="p">[::</span><span class="n">targets_stride</span><span class="p">]</span> <span class="o">=</span> <span class="n">upsampled_targets</span><span class="p">[::</span><span class="n">targets_stride</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">original_targets</span>
-
-    <span class="k">def</span> <span class="nf">_load_data_to_mne</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">loadmat</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-        <span class="n">test_labels</span> <span class="o">=</span> <span class="n">loadmat</span><span class="p">(</span><span class="n">file_path</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;comp.mat&#39;</span><span class="p">,</span> <span class="s1">&#39;testlabels.mat&#39;</span><span class="p">))</span>
-        <span class="n">train_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;train_data&#39;</span><span class="p">]</span>
-        <span class="n">test_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;test_data&#39;</span><span class="p">]</span>
-        <span class="n">upsampled_train_targets</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;train_dg&#39;</span><span class="p">]</span>
-        <span class="n">upsampled_test_targets</span> <span class="o">=</span> <span class="n">test_labels</span><span class="p">[</span><span class="s1">&#39;test_dg&#39;</span><span class="p">]</span>
-
-        <span class="n">signal_sfreq</span> <span class="o">=</span> <span class="mi">1000</span>
-        <span class="n">original_target_sfreq</span> <span class="o">=</span> <span class="mi">25</span>
-        <span class="n">targets_stride</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">signal_sfreq</span> <span class="o">/</span> <span class="n">original_target_sfreq</span><span class="p">)</span>
-
-        <span class="n">original_targets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_targets</span><span class="p">(</span><span class="n">upsampled_train_targets</span><span class="p">,</span> <span class="n">targets_stride</span><span class="p">)</span>
-        <span class="n">original_test_targets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_targets</span><span class="p">(</span><span class="n">upsampled_test_targets</span><span class="p">,</span> <span class="n">targets_stride</span><span class="p">)</span>
-
-        <span class="n">ch_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">ch_names</span> <span class="o">+=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;target_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">original_targets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">ch_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;ecog&#39;</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">ch_types</span> <span class="o">+=</span> <span class="p">[</span><span class="s1">&#39;misc&#39;</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">original_targets</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-
-        <span class="n">info</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">create_info</span><span class="p">(</span><span class="n">sfreq</span><span class="o">=</span><span class="n">signal_sfreq</span><span class="p">,</span> <span class="n">ch_names</span><span class="o">=</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="n">ch_types</span><span class="p">)</span>
-        <span class="n">info</span><span class="p">[</span><span class="s1">&#39;temp&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">target_sfreq</span><span class="o">=</span><span class="n">original_target_sfreq</span><span class="p">)</span>
-        <span class="n">train_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">train_data</span><span class="p">,</span> <span class="n">original_targets</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">test_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">test_data</span><span class="p">,</span> <span class="n">original_test_targets</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">raw_train</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">info</span><span class="o">=</span><span class="n">info</span><span class="p">)</span>
-        <span class="n">raw_test</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">test_data</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">info</span><span class="o">=</span><span class="n">info</span><span class="p">)</span>
-        <span class="c1"># TODO: show how to resample targets</span>
-        <span class="k">return</span> <span class="n">raw_train</span><span class="p">,</span> <span class="n">raw_test</span>
-
-    <span class="k">def</span> <span class="nf">_validate_subjects</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subject_ids</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">((</span><span class="n">subject</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">possible_subjects</span> <span class="k">for</span> <span class="n">subject</span> <span class="ow">in</span> <span class="n">subject_ids</span><span class="p">)):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s1">&#39;Wrong subject_ids parameter. Possible values: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">possible_subjects</span><span class="si">}</span><span class="s1">. &#39;</span>
-                    <span class="sa">f</span><span class="s1">&#39;Provided </span><span class="si">{</span><span class="n">subject_ids</span><span class="si">}</span><span class="s1">.&#39;</span>
-                <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s1">&#39;Wrong subject_ids format. Expected types: None, list, tuple, int.&#39;</span>
-            <span class="p">)</span></div>
-
-
-<span class="nd">@verbose</span>
-<span class="k">def</span> <span class="nf">_data_dl</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">destination</span><span class="p">,</span> <span class="n">force_update</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="c1"># Code taken from moabb due to problem with &#39;:&#39; occurring in path</span>
-    <span class="c1"># On Windows &#39;:&#39; is a forbidden in folder name</span>
-    <span class="c1"># moabb/datasets/download.py</span>
-
-    <span class="kn">from</span> <span class="nn">pooch</span> <span class="kn">import</span> <span class="n">file_hash</span><span class="p">,</span> <span class="n">retrieve</span>  <span class="c1"># keep soft depenency</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">osp</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">destination</span><span class="p">)</span> <span class="ow">or</span> <span class="n">force_update</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">osp</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">destination</span><span class="p">):</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">destination</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">osp</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">destination</span><span class="p">)):</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">destination</span><span class="p">))</span>
-        <span class="n">known_hash</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">known_hash</span> <span class="o">=</span> <span class="n">file_hash</span><span class="p">(</span><span class="n">destination</span><span class="p">)</span>
-    <span class="n">data_path</span> <span class="o">=</span> <span class="n">retrieve</span><span class="p">(</span>
-        <span class="n">url</span><span class="p">,</span> <span class="n">known_hash</span><span class="p">,</span> <span class="n">fname</span><span class="o">=</span><span class="n">osp</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">url</span><span class="p">),</span> <span class="n">path</span><span class="o">=</span><span class="n">osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">destination</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">data_path</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/datasets/mne.html b/stable/_modules/braindecode/datasets/mne.html
deleted file mode 100644
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-              
-  <h1>Source code for braindecode.datasets.mne</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span><span class="p">,</span> <span class="n">WindowsDataset</span>
-
-
-<div class="viewcode-block" id="create_from_mne_raw"><a class="viewcode-back" href="../../../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw">[docs]</a><span class="k">def</span> <span class="nf">create_from_mne_raw</span><span class="p">(</span>
-        <span class="n">raws</span><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span>
-        <span class="n">descriptions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">accepted_bads_ratio</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create WindowsDatasets from mne.RawArrays</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    raws: array-like</span>
-<span class="sd">        list of mne.RawArrays</span>
-<span class="sd">    trial_start_offset_samples: int</span>
-<span class="sd">        start offset from original trial onsets in samples</span>
-<span class="sd">    trial_stop_offset_samples: int</span>
-<span class="sd">        stop offset from original trial stop in samples</span>
-<span class="sd">    window_size_samples: int</span>
-<span class="sd">        window size</span>
-<span class="sd">    window_stride_samples: int</span>
-<span class="sd">        stride between windows</span>
-<span class="sd">    drop_last_window: bool</span>
-<span class="sd">        whether or not have a last overlapping window, when</span>
-<span class="sd">        windows do not equally divide the continuous signal</span>
-<span class="sd">    descriptions: array-like</span>
-<span class="sd">        list of dicts or pandas.Series with additional information about the raws</span>
-<span class="sd">    mapping: dict(str: int)</span>
-<span class="sd">        mapping from event description to target value</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        if True, preload the data of the Epochs objects.</span>
-<span class="sd">    drop_bad_windows: bool</span>
-<span class="sd">        If True, call `.drop_bad()` on the resulting mne.Epochs object. This</span>
-<span class="sd">        step allows identifying e.g., windows that fall outside of the</span>
-<span class="sd">        continuous recording. It is suggested to run this step here as otherwise</span>
-<span class="sd">        the BaseConcatDataset has to be updated as well.</span>
-<span class="sd">    accepted_bads_ratio: float, optional</span>
-<span class="sd">        Acceptable proportion of trials withinconsistent length in a raw. If</span>
-<span class="sd">        the number of trials whose length is exceeded by the window size is</span>
-<span class="sd">        smaller than this, then only the corresponding trials are dropped, but</span>
-<span class="sd">        the computation continues. Otherwise, an error is raised. Defaults to</span>
-<span class="sd">        0.0 (raise an error).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    windows_datasets: BaseConcatDataset</span>
-<span class="sd">        X and y transformed to a dataset format that is compativle with skorch</span>
-<span class="sd">        and braindecode</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Prevent circular import</span>
-    <span class="kn">from</span> <span class="nn">..preprocessing.windowers</span> <span class="kn">import</span> <span class="n">create_windows_from_events</span>
-    <span class="k">if</span> <span class="n">descriptions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">descriptions</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">raws</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;length of &#39;raws&#39; (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">raws</span><span class="p">)</span><span class="si">}</span><span class="s2">) and &#39;description&#39; &quot;</span>
-                <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">descriptions</span><span class="p">)</span><span class="si">}</span><span class="s2">) has to match&quot;</span><span class="p">)</span>
-        <span class="n">base_datasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">desc</span><span class="p">)</span> <span class="k">for</span> <span class="n">raw</span><span class="p">,</span> <span class="n">desc</span> <span class="ow">in</span>
-                         <span class="nb">zip</span><span class="p">(</span><span class="n">raws</span><span class="p">,</span> <span class="n">descriptions</span><span class="p">)]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">base_datasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span> <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <span class="n">raws</span><span class="p">]</span>
-
-    <span class="n">base_datasets</span> <span class="o">=</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">base_datasets</span><span class="p">)</span>
-    <span class="n">windows_datasets</span> <span class="o">=</span> <span class="n">create_windows_from_events</span><span class="p">(</span>
-        <span class="n">base_datasets</span><span class="p">,</span>
-        <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="n">trial_start_offset_samples</span><span class="p">,</span>
-        <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="o">=</span><span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="o">=</span><span class="n">window_stride_samples</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="o">=</span><span class="n">drop_last_window</span><span class="p">,</span>
-        <span class="n">mapping</span><span class="o">=</span><span class="n">mapping</span><span class="p">,</span>
-        <span class="n">drop_bad_windows</span><span class="o">=</span><span class="n">drop_bad_windows</span><span class="p">,</span>
-        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span>
-        <span class="n">accepted_bads_ratio</span><span class="o">=</span><span class="n">accepted_bads_ratio</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">windows_datasets</span></div>
-
-
-<div class="viewcode-block" id="create_from_mne_epochs"><a class="viewcode-back" href="../../../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs">[docs]</a><span class="k">def</span> <span class="nf">create_from_mne_epochs</span><span class="p">(</span><span class="n">list_of_epochs</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span>
-                           <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create WindowsDatasets from mne.Epochs</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    list_of_epochs: array-like</span>
-<span class="sd">        list of mne.Epochs</span>
-<span class="sd">    window_size_samples: int</span>
-<span class="sd">        window size</span>
-<span class="sd">    window_stride_samples: int</span>
-<span class="sd">        stride between windows</span>
-<span class="sd">    drop_last_window: bool</span>
-<span class="sd">        whether or not have a last overlapping window, when</span>
-<span class="sd">        windows do not equally divide the continuous signal</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    windows_datasets: BaseConcatDataset</span>
-<span class="sd">        X and y transformed to a dataset format that is compativle with skorch</span>
-<span class="sd">        and braindecode</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Prevent circular import</span>
-    <span class="kn">from</span> <span class="nn">..preprocessing.windowers</span> <span class="kn">import</span> <span class="n">_check_windowing_arguments</span>
-    <span class="n">_check_windowing_arguments</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span>
-                               <span class="n">window_stride_samples</span><span class="p">)</span>
-
-    <span class="n">list_of_windows_ds</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">epochs</span> <span class="ow">in</span> <span class="n">list_of_epochs</span><span class="p">:</span>
-        <span class="n">event_descriptions</span> <span class="o">=</span> <span class="n">epochs</span><span class="o">.</span><span class="n">events</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
-        <span class="n">original_trial_starts</span> <span class="o">=</span> <span class="n">epochs</span><span class="o">.</span><span class="n">events</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="n">stop</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">epochs</span><span class="o">.</span><span class="n">times</span><span class="p">)</span> <span class="o">-</span> <span class="n">window_size_samples</span>
-
-        <span class="c1"># already includes last incomplete window start</span>
-        <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">drop_last_window</span> <span class="ow">and</span> <span class="n">starts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">stop</span><span class="p">:</span>
-            <span class="c1"># if last window does not end at trial stop, make it stop there</span>
-            <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">starts</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span>
-
-        <span class="n">fake_events</span> <span class="o">=</span> <span class="p">[[</span><span class="n">start</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">start</span> <span class="ow">in</span>
-                       <span class="n">starts</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">trial_i</span><span class="p">,</span> <span class="n">trial</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
-            <span class="n">metadata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-                <span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)),</span>
-                <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">:</span> <span class="n">starts</span> <span class="o">+</span> <span class="n">original_trial_starts</span><span class="p">[</span><span class="n">trial_i</span><span class="p">],</span>
-                <span class="s1">&#39;i_stop_in_trial&#39;</span><span class="p">:</span> <span class="n">starts</span> <span class="o">+</span> <span class="n">original_trial_starts</span><span class="p">[</span>
-                    <span class="n">trial_i</span><span class="p">]</span> <span class="o">+</span> <span class="n">window_size_samples</span><span class="p">,</span>
-                <span class="s1">&#39;target&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)</span> <span class="o">*</span> <span class="p">[</span><span class="n">event_descriptions</span><span class="p">[</span><span class="n">trial_i</span><span class="p">]]</span>
-            <span class="p">})</span>
-            <span class="c1"># window size - 1, since tmax is inclusive</span>
-            <span class="n">mne_epochs</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span><span class="p">(</span>
-                <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">trial</span><span class="p">,</span> <span class="n">epochs</span><span class="o">.</span><span class="n">info</span><span class="p">),</span> <span class="n">fake_events</span><span class="p">,</span>
-                <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">tmax</span><span class="o">=</span><span class="p">(</span><span class="n">window_size_samples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">epochs</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">],</span>
-                <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">)</span>
-
-            <span class="n">mne_epochs</span><span class="o">.</span><span class="n">drop_bad</span><span class="p">(</span><span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-            <span class="n">windows_ds</span> <span class="o">=</span> <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">mne_epochs</span><span class="p">)</span>
-            <span class="n">list_of_windows_ds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">list_of_windows_ds</span><span class="p">)</span></div>
-</pre></div>
-
-            </article>
-            
-            
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-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
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diff --git a/stable/_modules/braindecode/datasets/moabb.html b/stable/_modules/braindecode/datasets/moabb.html
deleted file mode 100644
index 1f275ac3..00000000
--- a/stable/_modules/braindecode/datasets/moabb.html
+++ /dev/null
@@ -1,577 +0,0 @@
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-  <h1>Source code for braindecode.datasets.moabb</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Dataset objects for some public datasets.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#          David Sabbagh &lt;dav.sabbagh@gmail.com&gt;</span>
-<span class="c1">#          Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <span class="n">_update_moabb_docstring</span>
-
-
-<span class="k">def</span> <span class="nf">_find_dataset_in_moabb</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">dataset_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="c1"># soft dependency on moabb</span>
-    <span class="kn">from</span> <span class="nn">moabb.datasets.utils</span> <span class="kn">import</span> <span class="n">dataset_list</span>
-    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">dataset_list</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="vm">__name__</span><span class="p">:</span>
-            <span class="c1"># return an instance of the found dataset class</span>
-            <span class="k">if</span> <span class="n">dataset_kwargs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">dataset</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">dataset</span><span class="p">(</span><span class="o">**</span><span class="n">dataset_kwargs</span><span class="p">)</span>
-    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;&#39;dataset_name&#39; not found in moabb datasets&quot;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_fetch_and_unpack_moabb_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">):</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">subject_ids</span><span class="p">)</span>
-    <span class="n">raws</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">,</span> <span class="n">session_ids</span><span class="p">,</span> <span class="n">run_ids</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">subj_id</span><span class="p">,</span> <span class="n">subj_data</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="k">for</span> <span class="n">sess_id</span><span class="p">,</span> <span class="n">sess_data</span> <span class="ow">in</span> <span class="n">subj_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">for</span> <span class="n">run_id</span><span class="p">,</span> <span class="n">raw</span> <span class="ow">in</span> <span class="n">sess_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-                <span class="c1"># set annotation if empty</span>
-                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">raw</span><span class="o">.</span><span class="n">annotations</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">annots</span> <span class="o">=</span> <span class="n">_annotations_from_moabb_stim_channel</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">dataset</span><span class="p">)</span>
-                    <span class="n">raw</span><span class="o">.</span><span class="n">set_annotations</span><span class="p">(</span><span class="n">annots</span><span class="p">)</span>
-                <span class="n">raws</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span>
-                <span class="n">subject_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subj_id</span><span class="p">)</span>
-                <span class="n">session_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sess_id</span><span class="p">)</span>
-                <span class="n">run_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">run_id</span><span class="p">)</span>
-    <span class="n">description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-        <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subject_ids</span><span class="p">,</span>
-        <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="n">session_ids</span><span class="p">,</span>
-        <span class="s1">&#39;run&#39;</span><span class="p">:</span> <span class="n">run_ids</span>
-    <span class="p">})</span>
-    <span class="k">return</span> <span class="n">raws</span><span class="p">,</span> <span class="n">description</span>
-
-
-<span class="k">def</span> <span class="nf">_annotations_from_moabb_stim_channel</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
-    <span class="c1"># find events from stim channel</span>
-    <span class="n">events</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">find_events</span><span class="p">(</span><span class="n">raw</span><span class="p">)</span>
-
-    <span class="c1"># get annotations from events</span>
-    <span class="n">event_desc</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">event_id</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-    <span class="n">annots</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">annotations_from_events</span><span class="p">(</span><span class="n">events</span><span class="p">,</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span> <span class="n">event_desc</span><span class="p">)</span>
-
-    <span class="c1"># set trial on and offset given by moabb</span>
-    <span class="n">onset</span><span class="p">,</span> <span class="n">offset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">interval</span>
-    <span class="n">annots</span><span class="o">.</span><span class="n">onset</span> <span class="o">+=</span> <span class="n">onset</span>
-    <span class="n">annots</span><span class="o">.</span><span class="n">duration</span> <span class="o">+=</span> <span class="n">offset</span> <span class="o">-</span> <span class="n">onset</span>
-    <span class="k">return</span> <span class="n">annots</span>
-
-
-<span class="k">def</span> <span class="nf">fetch_data_with_moabb</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">,</span> <span class="n">dataset_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="c1"># ToDo: update path to where moabb downloads / looks for the data</span>
-    <span class="sd">&quot;&quot;&quot;Fetch data using moabb.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataset_name: str</span>
-<span class="sd">        the name of a dataset included in moabb</span>
-<span class="sd">    subject_ids: list(int) | int</span>
-<span class="sd">        (list of) int of subject(s) to be fetched</span>
-<span class="sd">    dataset_kwargs: dict, optional</span>
-<span class="sd">        optional dictionary containing keyword arguments</span>
-<span class="sd">        to pass to the moabb dataset when instantiating it.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    raws: mne.Raw</span>
-<span class="sd">    info: pandas.DataFrame</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">_find_dataset_in_moabb</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">dataset_kwargs</span><span class="p">)</span>
-    <span class="n">subject_id</span> <span class="o">=</span> <span class="p">[</span><span class="n">subject_ids</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subject_ids</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">else</span> <span class="n">subject_ids</span>
-    <span class="k">return</span> <span class="n">_fetch_and_unpack_moabb_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">subject_id</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="MOABBDataset"><a class="viewcode-back" href="../../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset">[docs]</a><span class="k">class</span> <span class="nc">MOABBDataset</span><span class="p">(</span><span class="n">BaseConcatDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;A class for moabb datasets.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    dataset_name: str</span>
-<span class="sd">        name of dataset included in moabb to be fetched</span>
-<span class="sd">    subject_ids: list(int) | int | None</span>
-<span class="sd">        (list of) int of subject(s) to be fetched. If None, data of all</span>
-<span class="sd">        subjects is fetched.</span>
-<span class="sd">    dataset_kwargs: dict, optional</span>
-<span class="sd">        optional dictionary containing keyword arguments</span>
-<span class="sd">        to pass to the moabb dataset when instantiating it.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset_name</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">,</span> <span class="n">dataset_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="n">raws</span><span class="p">,</span> <span class="n">description</span> <span class="o">=</span> <span class="n">fetch_data_with_moabb</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">,</span> <span class="n">dataset_kwargs</span><span class="p">)</span>
-        <span class="n">all_base_ds</span> <span class="o">=</span> <span class="p">[</span><span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span>
-                       <span class="k">for</span> <span class="n">raw</span><span class="p">,</span> <span class="p">(</span><span class="n">_</span><span class="p">,</span> <span class="n">row</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">raws</span><span class="p">,</span> <span class="n">description</span><span class="o">.</span><span class="n">iterrows</span><span class="p">())]</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">all_base_ds</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="BNCI2014001"><a class="viewcode-back" href="../../../generated/braindecode.datasets.BNCI2014001.html#braindecode.datasets.BNCI2014001">[docs]</a><span class="k">class</span> <span class="nc">BNCI2014001</span><span class="p">(</span><span class="n">MOABBDataset</span><span class="p">):</span>
-    <span class="n">doc</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;See moabb.datasets.bnci.BNCI2014001</span>
-
-<span class="s2">    Parameters</span>
-<span class="s2">    ----------</span>
-<span class="s2">    subject_ids: list(int) | int | None</span>
-<span class="s2">        (list of) int of subject(s) to be fetched. If None, data of all</span>
-<span class="s2">        subjects is fetched.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <span class="n">BNCI2014001</span>
-        <span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_update_moabb_docstring</span><span class="p">(</span><span class="n">BNCI2014001</span><span class="p">,</span> <span class="n">doc</span><span class="p">)</span>
-    <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
-        <span class="k">pass</span>  <span class="c1"># keep moabb soft dependency, otherwise crash on loading of datasets.__init__.py</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="n">subject_ids</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="HGD"><a class="viewcode-back" href="../../../generated/braindecode.datasets.HGD.html#braindecode.datasets.HGD">[docs]</a><span class="k">class</span> <span class="nc">HGD</span><span class="p">(</span><span class="n">MOABBDataset</span><span class="p">):</span>
-    <span class="n">doc</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;See moabb.datasets.schirrmeister2017.Schirrmeister2017</span>
-
-<span class="s2">    Parameters</span>
-<span class="s2">    ----------</span>
-<span class="s2">    subject_ids: list(int) | int | None</span>
-<span class="s2">        (list of) int of subject(s) to be fetched. If None, data of all</span>
-<span class="s2">        subjects is fetched.</span>
-<span class="s2">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">moabb.datasets</span> <span class="kn">import</span> <span class="n">Schirrmeister2017</span>
-        <span class="vm">__doc__</span> <span class="o">=</span> <span class="n">_update_moabb_docstring</span><span class="p">(</span><span class="n">Schirrmeister2017</span><span class="p">,</span> <span class="n">doc</span><span class="p">)</span>
-    <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
-        <span class="k">pass</span>  <span class="c1"># keep moabb soft dependency, otherwise crash on loading of datasets.__init__.py</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject_ids</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;Schirrmeister2017&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="n">subject_ids</span><span class="p">)</span></div>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/datasets/sleep_physionet.html b/stable/_modules/braindecode/datasets/sleep_physionet.html
deleted file mode 100644
index a7ab1ca9..00000000
--- a/stable/_modules/braindecode/datasets/sleep_physionet.html
+++ /dev/null
@@ -1,536 +0,0 @@
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-              
-  <h1>Source code for braindecode.datasets.sleep_physionet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">from</span> <span class="nn">mne.datasets.sleep_physionet.age</span> <span class="kn">import</span> <span class="n">fetch_data</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-
-
-<div class="viewcode-block" id="SleepPhysionet"><a class="viewcode-back" href="../../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet">[docs]</a><span class="k">class</span> <span class="nc">SleepPhysionet</span><span class="p">(</span><span class="n">BaseConcatDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sleep Physionet dataset.</span>
-
-<span class="sd">    Sleep dataset from https://physionet.org/content/sleep-edfx/1.0.0/.</span>
-<span class="sd">    Contains overnight recordings from 78 healthy subjects.</span>
-
-<span class="sd">    See [MNE example](https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    subject_ids: list(int) | int | None</span>
-<span class="sd">        (list of) int of subject(s) to be loaded. If None, load all available</span>
-<span class="sd">        subjects.</span>
-<span class="sd">    recording_ids: list(int) | None</span>
-<span class="sd">        Recordings to load per subject (each subject except 13 has two</span>
-<span class="sd">        recordings). Can be [1], [2] or [1, 2] (same as None).</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        If True, preload the data of the Raw objects.</span>
-<span class="sd">    load_eeg_only: bool</span>
-<span class="sd">        If True, only load the EEG channels and discard the others (EOG, EMG,</span>
-<span class="sd">        temperature, respiration) to avoid resampling the other signals.</span>
-<span class="sd">    crop_wake_mins: float</span>
-<span class="sd">        Number of minutes of wake time to keep before the first sleep event</span>
-<span class="sd">        and after the last sleep event. Used to reduce the imbalance in this</span>
-<span class="sd">        dataset. Default of 30 mins.</span>
-<span class="sd">    crop : None | tuple</span>
-<span class="sd">        If not None crop the raw files (e.g. to use only the first 3h).</span>
-<span class="sd">        Example: ``crop=(0, 3600*3)`` to keep only the first 3h.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">load_eeg_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">crop</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">subject_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">subject_ids</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">83</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">recording_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">recording_ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-
-        <span class="n">paths</span> <span class="o">=</span> <span class="n">fetch_data</span><span class="p">(</span>
-            <span class="n">subject_ids</span><span class="p">,</span> <span class="n">recording</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;warn&#39;</span><span class="p">)</span>
-
-        <span class="n">all_base_ds</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">:</span>
-            <span class="n">raw</span><span class="p">,</span> <span class="n">desc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_raw</span><span class="p">(</span>
-                <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">load_eeg_only</span><span class="o">=</span><span class="n">load_eeg_only</span><span class="p">,</span>
-                <span class="n">crop_wake_mins</span><span class="o">=</span><span class="n">crop_wake_mins</span><span class="p">,</span> <span class="n">crop</span><span class="o">=</span><span class="n">crop</span><span class="p">)</span>
-            <span class="n">base_ds</span> <span class="o">=</span> <span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">desc</span><span class="p">)</span>
-            <span class="n">all_base_ds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">base_ds</span><span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">all_base_ds</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_load_raw</span><span class="p">(</span><span class="n">raw_fname</span><span class="p">,</span> <span class="n">ann_fname</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">load_eeg_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                  <span class="n">crop_wake_mins</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">crop</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="n">ch_mapping</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;EOG horizontal&#39;</span><span class="p">:</span> <span class="s1">&#39;eog&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;Resp oro-nasal&#39;</span><span class="p">:</span> <span class="s1">&#39;misc&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;EMG submental&#39;</span><span class="p">:</span> <span class="s1">&#39;misc&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;Temp rectal&#39;</span><span class="p">:</span> <span class="s1">&#39;misc&#39;</span><span class="p">,</span>
-            <span class="s1">&#39;Event marker&#39;</span><span class="p">:</span> <span class="s1">&#39;misc&#39;</span>
-        <span class="p">}</span>
-        <span class="n">exclude</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">ch_mapping</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="k">if</span> <span class="n">load_eeg_only</span> <span class="k">else</span> <span class="p">()</span>
-
-        <span class="n">raw</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span><span class="p">(</span><span class="n">raw_fname</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="n">exclude</span><span class="p">)</span>
-        <span class="n">annots</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">read_annotations</span><span class="p">(</span><span class="n">ann_fname</span><span class="p">)</span>
-        <span class="n">raw</span><span class="o">.</span><span class="n">set_annotations</span><span class="p">(</span><span class="n">annots</span><span class="p">,</span> <span class="n">emit_warning</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">crop_wake_mins</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># Find first and last sleep stages</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;1&#39;</span><span class="p">,</span> <span class="s1">&#39;2&#39;</span><span class="p">,</span> <span class="s1">&#39;3&#39;</span><span class="p">,</span> <span class="s1">&#39;4&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">annots</span><span class="o">.</span><span class="n">description</span><span class="p">]</span>
-            <span class="n">sleep_event_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-
-            <span class="c1"># Crop raw</span>
-            <span class="n">tmin</span> <span class="o">=</span> <span class="n">annots</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">sleep_event_inds</span><span class="p">[</span><span class="mi">0</span><span class="p">])][</span><span class="s1">&#39;onset&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">crop_wake_mins</span> <span class="o">*</span> <span class="mi">60</span>
-            <span class="n">tmax</span> <span class="o">=</span> <span class="n">annots</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">sleep_event_inds</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])][</span><span class="s1">&#39;onset&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">crop_wake_mins</span> <span class="o">*</span> <span class="mi">60</span>
-            <span class="n">raw</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><span class="n">tmin</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">tmin</span><span class="p">,</span> <span class="n">raw</span><span class="o">.</span><span class="n">times</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-                     <span class="n">tmax</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">tmax</span><span class="p">,</span> <span class="n">raw</span><span class="o">.</span><span class="n">times</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
-
-        <span class="c1"># Rename EEG channels</span>
-        <span class="n">ch_names</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">i</span><span class="p">:</span> <span class="n">i</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;EEG &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span> <span class="k">if</span> <span class="s1">&#39;EEG&#39;</span> <span class="ow">in</span> <span class="n">i</span><span class="p">}</span>
-        <span class="n">raw</span><span class="o">.</span><span class="n">rename_channels</span><span class="p">(</span><span class="n">ch_names</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">load_eeg_only</span><span class="p">:</span>
-            <span class="n">raw</span><span class="o">.</span><span class="n">set_channel_types</span><span class="p">(</span><span class="n">ch_mapping</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">crop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">raw</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><span class="o">*</span><span class="n">crop</span><span class="p">)</span>
-
-        <span class="n">basename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">raw_fname</span><span class="p">)</span>
-        <span class="n">subj_nb</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">basename</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">sess_nb</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">basename</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
-        <span class="n">desc</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="n">subj_nb</span><span class="p">,</span> <span class="s1">&#39;recording&#39;</span><span class="p">:</span> <span class="n">sess_nb</span><span class="p">},</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">raw</span><span class="p">,</span> <span class="n">desc</span></div>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/datasets/tuh.html b/stable/_modules/braindecode/datasets/tuh.html
deleted file mode 100644
index b1bdbf9b..00000000
--- a/stable/_modules/braindecode/datasets/tuh.html
+++ /dev/null
@@ -1,734 +0,0 @@
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-<form class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
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-  <h1>Source code for braindecode.datasets.tuh</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Dataset classes for the Temple University Hospital (TUH) EEG Corpus and the</span>
-<span class="sd">TUH Abnormal EEG Corpus.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">re</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">glob</span>
-<span class="kn">from</span> <span class="nn">unittest</span> <span class="kn">import</span> <span class="n">mock</span>
-<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timezone</span>
-
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-
-
-<div class="viewcode-block" id="TUH"><a class="viewcode-back" href="../../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH">[docs]</a><span class="k">class</span> <span class="nc">TUH</span><span class="p">(</span><span class="n">BaseConcatDataset</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Temple University Hospital (TUH) EEG Corpus</span>
-<span class="sd">    (www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    path: str</span>
-<span class="sd">        Parent directory of the dataset.</span>
-<span class="sd">    recording_ids: list(int) | int</span>
-<span class="sd">        A (list of) int of recording id(s) to be read (order matters and will</span>
-<span class="sd">        overwrite default chronological order, e.g. if recording_ids=[1,0],</span>
-<span class="sd">        then the first recording returned by this class will be chronologically</span>
-<span class="sd">        later then the second recording. Provide recording_ids in ascending</span>
-<span class="sd">        order to preserve chronological order.).</span>
-<span class="sd">    target_name: str</span>
-<span class="sd">        Can be &#39;gender&#39;, or &#39;age&#39;.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        If True, preload the data of the Raw objects.</span>
-<span class="sd">    add_physician_reports: bool</span>
-<span class="sd">        If True, the physician reports will be read from disk and added to the</span>
-<span class="sd">        description.</span>
-<span class="sd">    n_jobs: int</span>
-<span class="sd">        Number of jobs to be used to read files in parallel.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="c1"># create an index of all files and gather easily accessible info</span>
-        <span class="c1"># without actually touching the files</span>
-        <span class="n">file_paths</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;**/*.edf&#39;</span><span class="p">),</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">descriptions</span> <span class="o">=</span> <span class="n">_create_chronological_description</span><span class="p">(</span><span class="n">file_paths</span><span class="p">)</span>
-        <span class="c1"># limit to specified recording ids before doing slow stuff</span>
-        <span class="k">if</span> <span class="n">recording_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">descriptions</span> <span class="o">=</span> <span class="n">descriptions</span><span class="p">[</span><span class="n">recording_ids</span><span class="p">]</span>
-        <span class="c1"># this is the second loop (slow)</span>
-        <span class="c1"># create datasets gathering more info about the files touching them</span>
-        <span class="c1"># reading the raws and potentially preloading the data</span>
-        <span class="c1"># disable joblib for tests. mocking seems to fail otherwise</span>
-        <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">base_datasets</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_create_dataset</span><span class="p">(</span>
-                <span class="n">descriptions</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">target_name</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">add_physician_reports</span><span class="p">)</span>
-                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">descriptions</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">base_datasets</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)(</span><span class="n">delayed</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_create_dataset</span><span class="p">)(</span>
-                <span class="n">descriptions</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">target_name</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">add_physician_reports</span>
-            <span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">descriptions</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">base_datasets</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_create_dataset</span><span class="p">(</span><span class="n">description</span><span class="p">,</span> <span class="n">target_name</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span>
-                        <span class="n">add_physician_reports</span><span class="p">):</span>
-        <span class="n">file_path</span> <span class="o">=</span> <span class="n">description</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="s1">&#39;path&#39;</span><span class="p">]</span>
-
-        <span class="c1"># parse age and gender information from EDF header</span>
-        <span class="n">age</span><span class="p">,</span> <span class="n">gender</span> <span class="o">=</span> <span class="n">_parse_age_and_gender_from_edf_header</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-        <span class="n">raw</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
-
-        <span class="c1"># Use recording date from path as EDF header is sometimes wrong</span>
-        <span class="n">meas_date</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">tzinfo</span><span class="o">=</span><span class="n">timezone</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span> \
-            <span class="k">if</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;meas_date&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;meas_date&#39;</span><span class="p">]</span>
-        <span class="n">raw</span><span class="o">.</span><span class="n">set_meas_date</span><span class="p">(</span><span class="n">meas_date</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
-            <span class="o">*</span><span class="n">description</span><span class="p">[[</span><span class="s1">&#39;year&#39;</span><span class="p">,</span> <span class="s1">&#39;month&#39;</span><span class="p">,</span> <span class="s1">&#39;day&#39;</span><span class="p">]]))</span>
-
-        <span class="c1"># read info relevant for preprocessing from raw without loading it</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;age&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">age</span><span class="p">),</span>
-            <span class="s1">&#39;gender&#39;</span><span class="p">:</span> <span class="n">gender</span><span class="p">,</span>
-        <span class="p">}</span>
-        <span class="k">if</span> <span class="n">add_physician_reports</span><span class="p">:</span>
-            <span class="n">physician_report</span> <span class="o">=</span> <span class="n">_read_physician_report</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-            <span class="n">d</span><span class="p">[</span><span class="s1">&#39;report&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">physician_report</span>
-        <span class="n">additional_description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
-        <span class="n">description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">description</span><span class="p">,</span> <span class="n">additional_description</span><span class="p">])</span>
-        <span class="n">base_dataset</span> <span class="o">=</span> <span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span>
-                                   <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">base_dataset</span></div>
-
-
-<span class="k">def</span> <span class="nf">_create_chronological_description</span><span class="p">(</span><span class="n">file_paths</span><span class="p">):</span>
-    <span class="c1"># this is the first loop (fast)</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="n">file_paths</span><span class="p">:</span>
-        <span class="n">description</span> <span class="o">=</span> <span class="n">_parse_description_from_file_path</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-        <span class="n">descriptions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">description</span><span class="p">))</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">(</span><span class="n">descriptions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="c1"># order descriptions chronologically</span>
-    <span class="n">descriptions</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span>
-        <span class="p">[</span><span class="s2">&quot;year&quot;</span><span class="p">,</span> <span class="s2">&quot;month&quot;</span><span class="p">,</span> <span class="s2">&quot;day&quot;</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">,</span> <span class="s2">&quot;segment&quot;</span><span class="p">],</span>
-        <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="c1"># https://stackoverflow.com/questions/42284617/reset-column-index-pandas</span>
-    <span class="n">descriptions</span> <span class="o">=</span> <span class="n">descriptions</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">return</span> <span class="n">descriptions</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_description_from_file_path</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
-    <span class="c1"># stackoverflow.com/questions/3167154/how-to-split-a-dos-path-into-its-components-in-python  # noqa</span>
-    <span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">normpath</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-    <span class="n">tokens</span> <span class="o">=</span> <span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)</span>
-    <span class="c1"># expect file paths as tuh_eeg/version/file_type/reference/data_split/</span>
-    <span class="c1">#                          subject/recording session/file</span>
-    <span class="c1"># e.g.                 tuh_eeg/v1.1.0/edf/01_tcp_ar/027/00002729/</span>
-    <span class="c1">#                          s001_2006_04_12/00002729_s001.edf</span>
-    <span class="n">version</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">7</span><span class="p">]</span>
-    <span class="n">year</span><span class="p">,</span> <span class="n">month</span><span class="p">,</span> <span class="n">day</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
-    <span class="n">subject_id</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]</span>
-    <span class="n">session</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">segment</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-    <span class="k">return</span> <span class="p">{</span>
-        <span class="s1">&#39;path&#39;</span><span class="p">:</span> <span class="n">file_path</span><span class="p">,</span>
-        <span class="s1">&#39;version&#39;</span><span class="p">:</span> <span class="n">version</span><span class="p">,</span>
-        <span class="s1">&#39;year&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">year</span><span class="p">),</span>
-        <span class="s1">&#39;month&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">month</span><span class="p">),</span>
-        <span class="s1">&#39;day&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">day</span><span class="p">),</span>
-        <span class="s1">&#39;subject&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">subject_id</span><span class="p">),</span>
-        <span class="s1">&#39;session&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">session</span><span class="p">[</span><span class="mi">1</span><span class="p">:]),</span>
-        <span class="s1">&#39;segment&#39;</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">segment</span><span class="p">[</span><span class="mi">1</span><span class="p">:]),</span>
-    <span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">_read_physician_report</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
-    <span class="n">directory</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-    <span class="n">txt_file</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="s1">&#39;**/*.txt&#39;</span><span class="p">),</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="c1"># check that there is at most one txt file in the same directory</span>
-    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">txt_file</span><span class="p">)</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-    <span class="n">report</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
-    <span class="k">if</span> <span class="n">txt_file</span><span class="p">:</span>
-        <span class="n">txt_file</span> <span class="o">=</span> <span class="n">txt_file</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="c1"># somewhere in the corpus, encoding apparently changed</span>
-        <span class="c1"># first try to read as utf-8, if it does not work use latin-1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">txt_file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="n">report</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-        <span class="k">except</span> <span class="ne">UnicodeDecodeError</span><span class="p">:</span>
-            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">txt_file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;latin-1&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="n">report</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">report</span>
-
-
-<span class="k">def</span> <span class="nf">_read_edf_header</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
-    <span class="n">f</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span>
-    <span class="n">header</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="mi">88</span><span class="p">)</span>
-    <span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">header</span>
-
-
-<span class="k">def</span> <span class="nf">_parse_age_and_gender_from_edf_header</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
-    <span class="n">header</span> <span class="o">=</span> <span class="n">_read_edf_header</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-    <span class="c1"># bytes 8 to 88 contain ascii local patient identification</span>
-    <span class="c1"># see https://www.teuniz.net/edfbrowser/edf%20format%20description.html</span>
-    <span class="n">patient_id</span> <span class="o">=</span> <span class="n">header</span><span class="p">[</span><span class="mi">8</span><span class="p">:]</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;ascii&quot;</span><span class="p">)</span>
-    <span class="n">age</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-    <span class="n">found_age</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;Age:(\d+)&quot;</span><span class="p">,</span> <span class="n">patient_id</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">found_age</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">age</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">found_age</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">gender</span> <span class="o">=</span> <span class="s2">&quot;X&quot;</span>
-    <span class="n">found_gender</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;\s([F|M])\s&quot;</span><span class="p">,</span> <span class="n">patient_id</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">found_gender</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">gender</span> <span class="o">=</span> <span class="n">found_gender</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">age</span><span class="p">,</span> <span class="n">gender</span>
-
-
-<div class="viewcode-block" id="TUHAbnormal"><a class="viewcode-back" href="../../../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal">[docs]</a><span class="k">class</span> <span class="nc">TUHAbnormal</span><span class="p">(</span><span class="n">TUH</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Temple University Hospital (TUH) Abnormal EEG Corpus.</span>
-<span class="sd">    see www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tuab</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    path: str</span>
-<span class="sd">        Parent directory of the dataset.</span>
-<span class="sd">    recording_ids: list(int) | int</span>
-<span class="sd">        A (list of) int of recording id(s) to be read (order matters and will</span>
-<span class="sd">        overwrite default chronological order, e.g. if recording_ids=[1,0],</span>
-<span class="sd">        then the first recording returned by this class will be chronologically</span>
-<span class="sd">        later then the second recording. Provide recording_ids in ascending</span>
-<span class="sd">        order to preserve chronological order.).</span>
-<span class="sd">    target_name: str</span>
-<span class="sd">        Can be &#39;pathological&#39;, &#39;gender&#39;, or &#39;age&#39;.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        If True, preload the data of the Raw objects.</span>
-<span class="sd">    add_physician_reports: bool</span>
-<span class="sd">        If True, the physician reports will be read from disk and added to the</span>
-<span class="sd">        description.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="s1">&#39;pathological&#39;</span><span class="p">,</span>
-                 <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span>
-                         <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">,</span>
-                         <span class="n">add_physician_reports</span><span class="o">=</span><span class="n">add_physician_reports</span><span class="p">,</span>
-                         <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
-        <span class="n">additional_descriptions</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">file_path</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">path</span><span class="p">:</span>
-            <span class="n">additional_description</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_parse_additional_description_from_file_path</span><span class="p">(</span><span class="n">file_path</span><span class="p">))</span>
-            <span class="n">additional_descriptions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">additional_description</span><span class="p">)</span>
-        <span class="n">additional_descriptions</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">additional_descriptions</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="n">additional_descriptions</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_parse_additional_description_from_file_path</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
-        <span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">normpath</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
-        <span class="n">tokens</span> <span class="o">=</span> <span class="n">file_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)</span>
-        <span class="c1"># expect paths as version/file type/data_split/pathology status/</span>
-        <span class="c1">#                     reference/subset/subject/recording session/file</span>
-        <span class="c1"># e.g.            v2.0.0/edf/train/normal/01_tcp_ar/000/00000021/</span>
-        <span class="c1">#                     s004_2013_08_15/00000021_s004_t000.edf</span>
-        <span class="k">assert</span> <span class="p">(</span><span class="s1">&#39;abnormal&#39;</span> <span class="ow">in</span> <span class="n">tokens</span> <span class="ow">or</span> <span class="s1">&#39;normal&#39;</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">),</span> <span class="p">(</span>
-            <span class="s1">&#39;No pathology labels found.&#39;</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="p">(</span><span class="s1">&#39;train&#39;</span> <span class="ow">in</span> <span class="n">tokens</span> <span class="ow">or</span> <span class="s1">&#39;eval&#39;</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">),</span> <span class="p">(</span>
-            <span class="s1">&#39;No train or eval set information found.&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">{</span>
-            <span class="s1">&#39;version&#39;</span><span class="p">:</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">9</span><span class="p">],</span>
-            <span class="s1">&#39;train&#39;</span><span class="p">:</span> <span class="s1">&#39;train&#39;</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">,</span>
-            <span class="s1">&#39;pathological&#39;</span><span class="p">:</span> <span class="s1">&#39;abnormal&#39;</span> <span class="ow">in</span> <span class="n">tokens</span><span class="p">,</span>
-        <span class="p">}</span></div>
-
-
-<span class="k">def</span> <span class="nf">_fake_raw</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="n">sfreq</span> <span class="o">=</span> <span class="mi">10</span>
-    <span class="n">ch_names</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="s1">&#39;EEG A1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-REF&#39;</span><span class="p">,</span>
-        <span class="s1">&#39;EEG FP1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-REF&#39;</span><span class="p">,</span>
-        <span class="s1">&#39;EEG C4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-REF&#39;</span><span class="p">,</span>
-        <span class="s1">&#39;EEG F7-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-REF&#39;</span><span class="p">,</span>
-        <span class="s1">&#39;EEG T6-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-REF&#39;</span><span class="p">]</span>
-    <span class="n">duration_min</span> <span class="o">=</span> <span class="mi">6</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ch_names</span><span class="p">),</span> <span class="n">duration_min</span> <span class="o">*</span> <span class="n">sfreq</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span>
-    <span class="n">info</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">create_info</span><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="n">sfreq</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">)</span>
-    <span class="n">raw</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">info</span><span class="o">=</span><span class="n">info</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">raw</span>
-
-
-<span class="k">def</span> <span class="nf">_get_header</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-    <span class="n">all_paths</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">_TUH_EEG_PATHS</span><span class="p">,</span> <span class="o">**</span><span class="n">_TUH_EEG_ABNORMAL_PATHS</span><span class="p">}</span>
-    <span class="k">return</span> <span class="n">all_paths</span><span class="p">[</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-
-
-<span class="n">_TUH_EEG_PATHS</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="c1"># These are actual file paths and edf headers from the TUH EEG Corpus (v1.1.0 and v1.2.0)</span>
-    <span class="s1">&#39;tuh_eeg/v1.1.0/edf/01_tcp_ar/000/00000000/s001_2015_12_30/00000000_s001_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00000000 M 01-JAN-1978 00000000 Age:37                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_eeg/v1.1.0/edf/01_tcp_ar/099/00009932/s004_2014_09_30/00009932_s004_t013.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00009932 F 01-JAN-1961 00009932 Age:53                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_eeg/v1.1.0/edf/02_tcp_le/000/00000058/s001_2003_02_05/00000058_s001_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00000058 M 01-JAN-2003 00000058 Age:0.0109                                      &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_eeg/v1.1.0/edf/03_tcp_ar_a/123/00012331/s003_2014_12_14/00012331_s003_t002.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00012331 M 01-JAN-1975 00012331 Age:39                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_eeg/v1.2.0/edf/03_tcp_ar_a/149/00014928/s004_2016_01_15/00014928_s004_t007.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00014928 F 01-JAN-1933 00014928 Age:83                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-<span class="p">}</span>
-<span class="n">_TUH_EEG_ABNORMAL_PATHS</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="c1"># these are actual file paths and edf headers from TUH Abnormal EEG Corpus (v2.0.0)</span>
-    <span class="s1">&#39;tuh_abnormal_eeg/v2.0.0/edf/train/normal/01_tcp_ar/078/00007871/s001_2011_07_05/00007871_s001_t001.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00007871 F 01-JAN-1988 00007871 Age:23                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_abnormal_eeg/v2.0.0/edf/train/normal/01_tcp_ar/097/00009777/s001_2012_09_17/00009777_s001_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00009777 M 01-JAN-1986 00009777 Age:26                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_abnormal_eeg/v2.0.0/edf/train/abnormal/01_tcp_ar/083/00008393/s002_2012_02_21/00008393_s002_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00008393 M 01-JAN-1960 00008393 Age:52                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_abnormal_eeg/v2.0.0/edf/train/abnormal/01_tcp_ar/012/00001200/s003_2010_12_06/00001200_s003_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00001200 M 01-JAN-1963 00001200 Age:47                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-    <span class="s1">&#39;tuh_abnormal_eeg/v2.0.0/edf/eval/abnormal/01_tcp_ar/059/00005932/s004_2013_03_14/00005932_s004_t000.edf&#39;</span><span class="p">:</span> <span class="sa">b</span><span class="s1">&#39;0       00005932 M 01-JAN-1963 00005932 Age:50                                          &#39;</span><span class="p">,</span>  <span class="c1"># noqa E501</span>
-<span class="p">}</span>
-
-
-<span class="k">class</span> <span class="nc">_TUHMock</span><span class="p">(</span><span class="n">TUH</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Mocked class for testing and examples.&quot;&quot;&quot;</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;glob.glob&#39;</span><span class="p">,</span> <span class="n">return_value</span><span class="o">=</span><span class="n">_TUH_EEG_PATHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;mne.io.read_raw_edf&#39;</span><span class="p">,</span> <span class="n">new</span><span class="o">=</span><span class="n">_fake_raw</span><span class="p">)</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;braindecode.datasets.tuh._read_edf_header&#39;</span><span class="p">,</span>
-                <span class="n">new</span><span class="o">=</span><span class="n">_get_header</span><span class="p">)</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mock_glob</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span>
-                         <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span>
-                         <span class="n">add_physician_reports</span><span class="o">=</span><span class="n">add_physician_reports</span><span class="p">,</span>
-                         <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_TUHAbnormalMock</span><span class="p">(</span><span class="n">TUHAbnormal</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Mocked class for testing and examples.&quot;&quot;&quot;</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;glob.glob&#39;</span><span class="p">,</span> <span class="n">return_value</span><span class="o">=</span><span class="n">_TUH_EEG_ABNORMAL_PATHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;mne.io.read_raw_edf&#39;</span><span class="p">,</span> <span class="n">new</span><span class="o">=</span><span class="n">_fake_raw</span><span class="p">)</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;braindecode.datasets.tuh._read_edf_header&#39;</span><span class="p">,</span>
-                <span class="n">new</span><span class="o">=</span><span class="n">_get_header</span><span class="p">)</span>
-    <span class="nd">@mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span><span class="s1">&#39;braindecode.datasets.tuh._read_physician_report&#39;</span><span class="p">,</span>
-                <span class="n">return_value</span><span class="o">=</span><span class="s1">&#39;simple_test&#39;</span><span class="p">)</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mock_glob</span><span class="p">,</span> <span class="n">mock_report</span><span class="p">,</span> <span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">target_name</span><span class="o">=</span><span class="s1">&#39;pathological&#39;</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span>
-                         <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span>
-                         <span class="n">add_physician_reports</span><span class="o">=</span><span class="n">add_physician_reports</span><span class="p">,</span>
-                         <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/datasets/xy.html b/stable/_modules/braindecode/datasets/xy.html
deleted file mode 100644
index 2a8fabb2..00000000
--- a/stable/_modules/braindecode/datasets/xy.html
+++ /dev/null
@@ -1,508 +0,0 @@
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-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
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-  <h1>Source code for braindecode.datasets.xy</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-
-<span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="create_from_X_y"><a class="viewcode-back" href="../../../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y">[docs]</a><span class="k">def</span> <span class="nf">create_from_X_y</span><span class="p">(</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">ch_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create a BaseConcatDataset of WindowsDatasets from X and y to be used for</span>
-<span class="sd">    decoding with skorch and braindecode, where X is a list of pre-cut trials</span>
-<span class="sd">    and y are corresponding targets.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X: array-like</span>
-<span class="sd">        list of pre-cut trials as n_trials x n_channels x n_times</span>
-<span class="sd">    y: array-like</span>
-<span class="sd">        targets corresponding to the trials</span>
-<span class="sd">    drop_last_window: bool</span>
-<span class="sd">        whether or not have a last overlapping window, when</span>
-<span class="sd">        windows/windows do not equally divide the continuous signal</span>
-<span class="sd">    sfreq: float</span>
-<span class="sd">        Sampling frequency of signals.</span>
-<span class="sd">    ch_names: array-like</span>
-<span class="sd">        Names of the channels.</span>
-<span class="sd">    window_size_samples: int</span>
-<span class="sd">        window size</span>
-<span class="sd">    window_stride_samples: int</span>
-<span class="sd">        stride between windows</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    windows_datasets: BaseConcatDataset</span>
-<span class="sd">        X and y transformed to a dataset format that is compatible with skorch</span>
-<span class="sd">        and braindecode</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Prevent circular import</span>
-    <span class="kn">from</span> <span class="nn">..preprocessing.windowers</span> <span class="kn">import</span> <span class="p">(</span>
-        <span class="n">create_fixed_length_windows</span><span class="p">,</span> <span class="p">)</span>
-    <span class="n">n_samples_per_x</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">base_datasets</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">ch_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">ch_names</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-        <span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No channel names given, set to 0-</span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">).&quot;</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">target</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="n">n_samples_per_x</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">info</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">create_info</span><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="n">sfreq</span><span class="p">)</span>
-        <span class="n">raw</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">info</span><span class="p">)</span>
-        <span class="n">base_dataset</span> <span class="o">=</span> <span class="n">BaseDataset</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">({</span><span class="s2">&quot;target&quot;</span><span class="p">:</span> <span class="n">target</span><span class="p">}),</span>
-                                   <span class="n">target_name</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">)</span>
-        <span class="n">base_datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">base_dataset</span><span class="p">)</span>
-    <span class="n">base_datasets</span> <span class="o">=</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">base_datasets</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">n_samples_per_x</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;if &#39;window_size_samples&#39; and &quot;</span>
-                             <span class="s2">&quot;&#39;window_stride_samples&#39; are None, &quot;</span>
-                             <span class="s2">&quot;all trials have to have the same length&quot;</span><span class="p">)</span>
-        <span class="n">window_size_samples</span> <span class="o">=</span> <span class="n">n_samples_per_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">window_stride_samples</span> <span class="o">=</span> <span class="n">n_samples_per_x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">windows_datasets</span> <span class="o">=</span> <span class="n">create_fixed_length_windows</span><span class="p">(</span>
-        <span class="n">base_datasets</span><span class="p">,</span>
-        <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="o">=</span><span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="o">=</span><span class="n">window_stride_samples</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="o">=</span><span class="n">drop_last_window</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">windows_datasets</span></div>
-</pre></div>
-
-            </article>
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diff --git a/stable/_modules/braindecode/datautil/serialization.html b/stable/_modules/braindecode/datautil/serialization.html
deleted file mode 100644
index dd9a1e3b..00000000
--- a/stable/_modules/braindecode/datautil/serialization.html
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-  <h1>Source code for braindecode.datautil.serialization</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Convenience functions for storing and loading of windows datasets.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">json</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
-
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-
-<span class="kn">from</span> <span class="nn">..datasets.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span><span class="p">,</span> <span class="n">WindowsDataset</span>
-
-
-<div class="viewcode-block" id="save_concat_dataset"><a class="viewcode-back" href="../../../generated/braindecode.datautil.save_concat_dataset.html#braindecode.datautil.save_concat_dataset">[docs]</a><span class="k">def</span> <span class="nf">save_concat_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">concat_dataset</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;&quot;save_concat_dataset()&quot; is deprecated and will be removed in&#39;</span>
-                  <span class="s1">&#39; the future. Use dataset.save() instead.&#39;</span><span class="p">)</span>
-    <span class="n">concat_dataset</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_outdated_load_concat_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">ids_to_load</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                  <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from</span>
-<span class="sd">    files.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    path: str</span>
-<span class="sd">        Path to the directory of the .fif / -epo.fif and .json files.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        Whether to preload the data.</span>
-<span class="sd">    ids_to_load: None | list(int)</span>
-<span class="sd">        Ids of specific files to load.</span>
-<span class="sd">    target_name: None or str</span>
-<span class="sd">        Load specific description column as target. If not given, take saved</span>
-<span class="sd">        target name.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    concat_dataset: BaseConcatDataset of BaseDatasets or WindowsDatasets</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># assume we have a single concat dataset to load</span>
-    <span class="n">is_raw</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0-raw.fif&#39;</span><span class="p">))</span>
-    <span class="k">assert</span> <span class="ow">not</span> <span class="p">(</span><span class="ow">not</span> <span class="n">is_raw</span> <span class="ow">and</span> <span class="n">target_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">),</span> <span class="p">(</span>
-        <span class="s1">&#39;Setting a new target is only supported for raws.&#39;</span><span class="p">)</span>
-    <span class="n">is_epochs</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0-epo.fif&#39;</span><span class="p">))</span>
-    <span class="n">paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">path</span><span class="p">]</span>
-    <span class="c1"># assume we have multiple concat datasets to load</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">is_raw</span> <span class="ow">or</span> <span class="n">is_epochs</span><span class="p">):</span>
-        <span class="n">is_raw</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">,</span> <span class="s1">&#39;0-raw.fif&#39;</span><span class="p">))</span>
-        <span class="n">is_epochs</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0&#39;</span><span class="p">,</span> <span class="s1">&#39;0-epo.fif&#39;</span><span class="p">))</span>
-        <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;*&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
-        <span class="n">paths</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="n">paths</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">sep</span><span class="p">)[</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
-        <span class="k">if</span> <span class="n">ids_to_load</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">paths</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_to_load</span><span class="p">]</span>
-        <span class="n">ids_to_load</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="c1"># if we have neither a single nor multiple datasets, something went wrong</span>
-    <span class="k">assert</span> <span class="n">is_raw</span> <span class="ow">or</span> <span class="n">is_epochs</span><span class="p">,</span> <span class="p">(</span>
-        <span class="sa">f</span><span class="s1">&#39;Expect either raw or epo to exist in </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1"> or in &#39;</span>
-        <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="n">datasets</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">paths</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">is_raw</span> <span class="ow">and</span> <span class="n">target_name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">target_file_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;target_name.json&#39;</span><span class="p">)</span>
-            <span class="n">target_name</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">target_file_name</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">))[</span><span class="s1">&#39;target_name&#39;</span><span class="p">]</span>
-
-        <span class="n">all_signals</span><span class="p">,</span> <span class="n">description</span> <span class="o">=</span> <span class="n">_load_signals_and_description</span><span class="p">(</span>
-            <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="o">=</span><span class="n">is_raw</span><span class="p">,</span>
-            <span class="n">ids_to_load</span><span class="o">=</span><span class="n">ids_to_load</span>
-        <span class="p">)</span>
-        <span class="k">for</span> <span class="n">i_signal</span><span class="p">,</span> <span class="n">signal</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_signals</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-                <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">BaseDataset</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">description</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i_signal</span><span class="p">],</span>
-                                <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-                    <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">description</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">i_signal</span><span class="p">])</span>
-                <span class="p">)</span>
-    <span class="n">concat_ds</span> <span class="o">=</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">kwarg_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]:</span>
-        <span class="n">kwarg_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">kwarg_name</span><span class="p">,</span> <span class="s1">&#39;json&#39;</span><span class="p">]))</span>
-        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">kwarg_path</span><span class="p">):</span>
-            <span class="n">kwargs</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">kwarg_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">))</span>
-            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">kwarg</span><span class="p">)</span> <span class="k">for</span> <span class="n">kwarg</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">]</span>
-            <span class="nb">setattr</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">kwarg_name</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">concat_ds</span>
-
-
-<span class="k">def</span> <span class="nf">_load_signals_and_description</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">,</span> <span class="n">ids_to_load</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="n">all_signals</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">file_name</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">-raw.fif&quot;</span> <span class="k">if</span> <span class="n">is_raw</span> <span class="k">else</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">-epo.fif&quot;</span>
-    <span class="n">description_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s2">&quot;description.json&quot;</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">ids_to_load</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">file_names</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;*</span><span class="si">{</span><span class="n">file_name</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">))</span>
-        <span class="c1"># Extract ids, e.g.,</span>
-        <span class="c1"># &#39;/home/schirrmr/data/preproced-tuh/all-sensors/11-raw.fif&#39; -&gt;</span>
-        <span class="c1"># &#39;11-raw.fif&#39; -&gt; 11</span>
-        <span class="n">ids_to_load</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
-            <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">f</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">file_names</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_to_load</span><span class="p">:</span>
-        <span class="n">fif_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">file_name</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-        <span class="n">all_signals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_load_signals</span><span class="p">(</span><span class="n">fif_file</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">))</span>
-    <span class="n">description_df</span> <span class="o">=</span> <span class="n">description_df</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ids_to_load</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">all_signals</span><span class="p">,</span> <span class="n">description_df</span>
-
-
-<span class="k">def</span> <span class="nf">_load_signals</span><span class="p">(</span><span class="n">fif_file</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-        <span class="n">signals</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_fif</span><span class="p">(</span><span class="n">fif_file</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">fif_file</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;-epo.fif&#39;</span><span class="p">):</span>
-        <span class="n">signals</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">read_epochs</span><span class="p">(</span><span class="n">fif_file</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;fif_file must end with raw.fif or epo.fif.&#39;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">signals</span>
-
-
-<div class="viewcode-block" id="load_concat_dataset"><a class="viewcode-back" href="../../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset">[docs]</a><span class="k">def</span> <span class="nf">load_concat_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">ids_to_load</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                        <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from</span>
-<span class="sd">    files.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    path: str</span>
-<span class="sd">        Path to the directory of the .fif / -epo.fif and .json files.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        Whether to preload the data.</span>
-<span class="sd">    ids_to_load: list of int | None</span>
-<span class="sd">        Ids of specific files to load.</span>
-<span class="sd">    target_name: str | list | None</span>
-<span class="sd">        Load specific description column as target. If not given, take saved</span>
-<span class="sd">        target name.</span>
-<span class="sd">    n_jobs: int</span>
-<span class="sd">        Number of jobs to be used to read files in parallel.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    concat_dataset: BaseConcatDataset of BaseDatasets or WindowsDatasets</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># if we encounter a dataset that was saved in &#39;the old way&#39;, call the</span>
-    <span class="c1"># corresponding &#39;old&#39; loading function</span>
-    <span class="k">if</span> <span class="n">_is_outdated_saved</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;The way your dataset was saved is deprecated by now. &quot;</span>
-                      <span class="s2">&quot;Please save it again using dataset.save().&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">_outdated_load_concat_dataset</span><span class="p">(</span>
-            <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">ids_to_load</span><span class="o">=</span><span class="n">ids_to_load</span><span class="p">,</span>
-            <span class="n">target_name</span><span class="o">=</span><span class="n">target_name</span><span class="p">)</span>
-
-    <span class="c1"># else we have a dataset saved in the new way with subdirectories in path</span>
-    <span class="c1"># for every dataset with description.json and -epo.fif or -raw.fif,</span>
-    <span class="c1"># target_name.json, raw_preproc_kwargs.json, window_kwargs.json,</span>
-    <span class="c1"># window_preproc_kwargs.json</span>
-    <span class="k">if</span> <span class="n">ids_to_load</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">ids_to_load</span> <span class="o">=</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">p</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">path</span><span class="p">)]</span>
-        <span class="n">ids_to_load</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">ids_to_load</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">i</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-    <span class="n">ids_to_load</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_to_load</span><span class="p">]</span>
-    <span class="n">first_raw_fif_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
-        <span class="n">path</span><span class="p">,</span> <span class="n">ids_to_load</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">ids_to_load</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1">-raw.fif&#39;</span><span class="p">)</span>
-    <span class="n">is_raw</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">first_raw_fif_path</span><span class="p">)</span>
-    <span class="c1"># Parallelization of mne.read_epochs with preload=False fails with</span>
-    <span class="c1"># &#39;TypeError: cannot pickle &#39;_io.BufferedReader&#39; object&#39;.</span>
-    <span class="c1"># So ignore n_jobs in that case and load with a single job.</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">is_raw</span> <span class="ow">and</span> <span class="n">n_jobs</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s1">&#39;Parallelized reading with `preload=False` is not supported for &#39;</span>
-            <span class="s1">&#39;windowed data. Will use `n_jobs=1`.&#39;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="n">n_jobs</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="n">datasets</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">_load_parallel</span><span class="p">)(</span><span class="n">path</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ids_to_load</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">datasets</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_load_parallel</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">):</span>
-    <span class="n">sub_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
-    <span class="n">file_name_patterns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">-raw.fif&#39;</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{}</span><span class="s1">-epo.fif&#39;</span><span class="p">]</span>
-    <span class="k">if</span> <span class="nb">all</span><span class="p">([</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)))</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">file_name_patterns</span><span class="p">]):</span>
-        <span class="k">raise</span> <span class="ne">FileExistsError</span><span class="p">(</span><span class="s1">&#39;Found -raw.fif and -epo.fif in directory.&#39;</span><span class="p">)</span>
-    <span class="n">fif_name_pattern</span> <span class="o">=</span> <span class="n">file_name_patterns</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_raw</span> <span class="k">else</span> <span class="n">file_name_patterns</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">fif_file_name</span> <span class="o">=</span> <span class="n">fif_name_pattern</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="n">fif_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">fif_file_name</span><span class="p">)</span>
-    <span class="n">signals</span> <span class="o">=</span> <span class="n">_load_signals</span><span class="p">(</span><span class="n">fif_file_path</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">is_raw</span><span class="p">)</span>
-    <span class="n">description_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="s1">&#39;description.json&#39;</span><span class="p">)</span>
-    <span class="n">description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="n">description_file_path</span><span class="p">,</span> <span class="n">typ</span><span class="o">=</span><span class="s1">&#39;series&#39;</span><span class="p">)</span>
-    <span class="n">target_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="s1">&#39;target_name.json&#39;</span><span class="p">)</span>
-    <span class="n">target_name</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">target_file_path</span><span class="p">):</span>
-        <span class="n">target_name</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">target_file_path</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">))[</span><span class="s1">&#39;target_name&#39;</span><span class="p">]</span>
-
-    <span class="k">if</span> <span class="n">is_raw</span><span class="p">:</span>
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">BaseDataset</span><span class="p">(</span><span class="n">signals</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span> <span class="n">target_name</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">window_kwargs</span> <span class="o">=</span> <span class="n">_load_kwargs_json</span><span class="p">(</span><span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="n">sub_dir</span><span class="p">)</span>
-        <span class="n">windows_ds_kwargs</span> <span class="o">=</span> <span class="p">[</span><span class="n">kwargs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kwargs</span> <span class="ow">in</span> <span class="n">window_kwargs</span> <span class="k">if</span> <span class="n">kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;WindowsDataset&#39;</span><span class="p">]</span>
-        <span class="n">windows_ds_kwargs</span> <span class="o">=</span> <span class="n">windows_ds_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">windows_ds_kwargs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="p">{}</span>
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">signals</span><span class="p">,</span> <span class="n">description</span><span class="p">,</span>
-                                 <span class="n">targets_from</span><span class="o">=</span><span class="n">windows_ds_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;targets_from&#39;</span><span class="p">,</span> <span class="s1">&#39;metadata&#39;</span><span class="p">),</span>
-                                 <span class="n">last_target_only</span><span class="o">=</span><span class="n">windows_ds_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;last_target_only&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-                                 <span class="p">)</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="n">window_kwargs</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">kwargs_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]:</span>
-        <span class="n">kwargs</span> <span class="o">=</span> <span class="n">_load_kwargs_json</span><span class="p">(</span><span class="n">kwargs_name</span><span class="p">,</span> <span class="n">sub_dir</span><span class="p">)</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">dataset</span>
-
-
-<span class="k">def</span> <span class="nf">_load_kwargs_json</span><span class="p">(</span><span class="n">kwargs_name</span><span class="p">,</span> <span class="n">sub_dir</span><span class="p">):</span>
-    <span class="n">kwargs_file_name</span> <span class="o">=</span> <span class="s1">&#39;.&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">kwargs_name</span><span class="p">,</span> <span class="s1">&#39;json&#39;</span><span class="p">])</span>
-    <span class="n">kwargs_file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sub_dir</span><span class="p">,</span> <span class="n">kwargs_file_name</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">kwargs_file_path</span><span class="p">):</span>
-        <span class="n">kwargs</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">kwargs_file_path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">))</span>
-        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="n">kwarg</span><span class="p">)</span> <span class="k">for</span> <span class="n">kwarg</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">kwargs</span>
-
-
-<span class="k">def</span> <span class="nf">_is_outdated_saved</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Data was saved in the old way if there are &#39;description.json&#39;, &#39;-raw.fif&#39;</span>
-<span class="sd">    or &#39;-epo.fif&#39; in path (no subdirectories) OR if there are more &#39;fif&#39; files</span>
-<span class="sd">    than &#39;description.json&#39; files.&quot;&quot;&quot;</span>
-    <span class="n">description_files</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;**/description.json&#39;</span><span class="p">))</span>
-    <span class="n">fif_files</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;**/*-raw.fif&#39;</span><span class="p">))</span> <span class="o">+</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;**/*-epo.fif&#39;</span><span class="p">))</span>
-    <span class="n">multiple</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">description_files</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">fif_files</span><span class="p">)</span>
-    <span class="n">kwargs_in_path</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">kwarg_name</span><span class="p">))</span>
-         <span class="k">for</span> <span class="n">kwarg_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;raw_preproc_kwargs&#39;</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span>
-                            <span class="s1">&#39;window_preproc_kwargs&#39;</span><span class="p">]])</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;description.json&#39;</span><span class="p">))</span> <span class="ow">or</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0-raw.fif&#39;</span><span class="p">))</span> <span class="ow">or</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;0-epo.fif&#39;</span><span class="p">))</span> <span class="ow">or</span>
-            <span class="n">multiple</span> <span class="ow">or</span>
-            <span class="n">kwargs_in_path</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_check_save_dir_empty</span><span class="p">(</span><span class="n">save_dir</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Make sure a BaseConcatDataset can be saved under a given directory.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    save_dir : str</span>
-<span class="sd">        Directory under which a `BaseConcatDataset` will be saved.</span>
-
-<span class="sd">    Raises</span>
-<span class="sd">    -------</span>
-<span class="sd">    FileExistsError</span>
-<span class="sd">        If ``save_dir`` is not a valid directory for saving.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">sub_dirs</span> <span class="o">=</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()</span>
-                <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">&#39;*&#39;</span><span class="p">))]</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">sub_dirs</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">FileExistsError</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s1">&#39;Directory </span><span class="si">{</span><span class="n">save_dir</span><span class="si">}</span><span class="s1"> already contains subdirectories. Please &#39;</span>
-            <span class="s1">&#39;select a different directory, set overwrite=True, or resolve &#39;</span>
-            <span class="s1">&#39;manually.&#39;</span><span class="p">)</span>
-</pre></div>
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-  <h1>Source code for braindecode.models.deep4</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">elu</span>
-
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Expression</span><span class="p">,</span> <span class="n">AvgPool2dWithConv</span><span class="p">,</span> <span class="n">Ensure4d</span>
-<span class="kn">from</span> <span class="nn">.functions</span> <span class="kn">import</span> <span class="n">identity</span><span class="p">,</span> <span class="n">transpose_time_to_spat</span><span class="p">,</span> <span class="n">squeeze_final_output</span>
-<span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">np_to_th</span>
-
-
-<div class="viewcode-block" id="Deep4Net"><a class="viewcode-back" href="../../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net">[docs]</a><span class="k">class</span> <span class="nc">Deep4Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Deep ConvNet model from Schirrmeister et al 2017.</span>
-
-<span class="sd">    Model described in [Schirrmeister2017]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">     Number of EEG input channels.</span>
-<span class="sd">    n_classes: int</span>
-<span class="sd">        Number of classes to predict (number of output filters of last layer).</span>
-<span class="sd">    input_window_samples: int | None</span>
-<span class="sd">        Only used to determine the length of the last convolutional kernel if</span>
-<span class="sd">        final_conv_length is &quot;auto&quot;.</span>
-<span class="sd">    final_conv_length: int | str</span>
-<span class="sd">        Length of the final convolution layer.</span>
-<span class="sd">        If set to &quot;auto&quot;, input_window_samples must not be None.</span>
-<span class="sd">    n_filters_time: int</span>
-<span class="sd">        Number of temporal filters.</span>
-<span class="sd">    n_filters_spat: int</span>
-<span class="sd">        Number of spatial filters.</span>
-<span class="sd">    filter_time_length: int</span>
-<span class="sd">        Length of the temporal filter in layer 1.</span>
-<span class="sd">    pool_time_length: int</span>
-<span class="sd">        Length of temporal pooling filter.</span>
-<span class="sd">    pool_time_stride: int</span>
-<span class="sd">        Length of stride between temporal pooling filters.</span>
-<span class="sd">    n_filters_2: int</span>
-<span class="sd">        Number of temporal filters in layer 2.</span>
-<span class="sd">    filter_length_2: int</span>
-<span class="sd">        Length of the temporal filter in layer 2.</span>
-<span class="sd">    n_filters_3: int</span>
-<span class="sd">        Number of temporal filters in layer 3.</span>
-<span class="sd">    filter_length_3: int</span>
-<span class="sd">        Length of the temporal filter in layer 3.</span>
-<span class="sd">    n_filters_4: int</span>
-<span class="sd">        Number of temporal filters in layer 4.</span>
-<span class="sd">    filter_length_4: int</span>
-<span class="sd">        Length of the temporal filter in layer 4.</span>
-<span class="sd">    first_conv_nonlin: callable</span>
-<span class="sd">        Non-linear activation function to be used after convolution in layer 1.</span>
-<span class="sd">    first_pool_mode: str</span>
-<span class="sd">        Pooling mode in layer 1. &quot;max&quot; or &quot;mean&quot;.</span>
-<span class="sd">    first_pool_nonlin: callable</span>
-<span class="sd">        Non-linear activation function to be used after pooling in layer 1.</span>
-<span class="sd">    later_conv_nonlin: callable</span>
-<span class="sd">        Non-linear activation function to be used after convolution in later layers.</span>
-<span class="sd">    later_pool_mode: str</span>
-<span class="sd">        Pooling mode in later layers. &quot;max&quot; or &quot;mean&quot;.</span>
-<span class="sd">    later_pool_nonlin: callable</span>
-<span class="sd">        Non-linear activation function to be used after pooling in later layers.</span>
-<span class="sd">    drop_prob: float</span>
-<span class="sd">        Dropout probability.</span>
-<span class="sd">    split_first_layer: bool</span>
-<span class="sd">        Split first layer into temporal and spatial layers (True) or just use temporal (False).</span>
-<span class="sd">        There would be no non-linearity between the split layers.</span>
-<span class="sd">    batch_norm: bool</span>
-<span class="sd">        Whether to use batch normalisation.</span>
-<span class="sd">    batch_norm_alpha: float</span>
-<span class="sd">        Momentum for BatchNorm2d.</span>
-<span class="sd">    stride_before_pool: bool</span>
-<span class="sd">        Stride before pooling.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,</span>
-<span class="sd">       L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.</span>
-<span class="sd">       &amp; Ball, T. (2017).</span>
-<span class="sd">       Deep learning with convolutional neural networks for EEG decoding and</span>
-<span class="sd">       visualization.</span>
-<span class="sd">       Human Brain Mapping , Aug. 2017.</span>
-<span class="sd">       Online: http://dx.doi.org/10.1002/hbm.23730</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">in_chans</span><span class="p">,</span>
-        <span class="n">n_classes</span><span class="p">,</span>
-        <span class="n">input_window_samples</span><span class="p">,</span>
-        <span class="n">final_conv_length</span><span class="p">,</span>
-        <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-        <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-        <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-        <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-        <span class="n">n_filters_2</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-        <span class="n">filter_length_2</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">n_filters_3</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-        <span class="n">filter_length_3</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">n_filters_4</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
-        <span class="n">filter_length_4</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="n">first_conv_nonlin</span><span class="o">=</span><span class="n">elu</span><span class="p">,</span>
-        <span class="n">first_pool_mode</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span>
-        <span class="n">first_pool_nonlin</span><span class="o">=</span><span class="n">identity</span><span class="p">,</span>
-        <span class="n">later_conv_nonlin</span><span class="o">=</span><span class="n">elu</span><span class="p">,</span>
-        <span class="n">later_pool_mode</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span>
-        <span class="n">later_pool_nonlin</span><span class="o">=</span><span class="n">identity</span><span class="p">,</span>
-        <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-        <span class="n">stride_before_pool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">input_window_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">final_conv_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span> <span class="o">=</span> <span class="n">n_filters_time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span> <span class="o">=</span> <span class="n">n_filters_spat</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span> <span class="o">=</span> <span class="n">filter_time_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_length</span> <span class="o">=</span> <span class="n">pool_time_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_stride</span> <span class="o">=</span> <span class="n">pool_time_stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_2</span> <span class="o">=</span> <span class="n">n_filters_2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_2</span> <span class="o">=</span> <span class="n">filter_length_2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_3</span> <span class="o">=</span> <span class="n">n_filters_3</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_3</span> <span class="o">=</span> <span class="n">filter_length_3</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_4</span> <span class="o">=</span> <span class="n">n_filters_4</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_4</span> <span class="o">=</span> <span class="n">filter_length_4</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">first_nonlin</span> <span class="o">=</span> <span class="n">first_conv_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">first_pool_mode</span> <span class="o">=</span> <span class="n">first_pool_mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">first_pool_nonlin</span> <span class="o">=</span> <span class="n">first_pool_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">later_conv_nonlin</span> <span class="o">=</span> <span class="n">later_conv_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">later_pool_mode</span> <span class="o">=</span> <span class="n">later_pool_mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">later_pool_nonlin</span> <span class="o">=</span> <span class="n">later_pool_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span> <span class="o">=</span> <span class="n">split_first_layer</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span> <span class="o">=</span> <span class="n">batch_norm</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span> <span class="o">=</span> <span class="n">batch_norm_alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">stride_before_pool</span> <span class="o">=</span> <span class="n">stride_before_pool</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride_before_pool</span><span class="p">:</span>
-            <span class="n">conv_stride</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_stride</span>
-            <span class="n">pool_stride</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">conv_stride</span> <span class="o">=</span> <span class="mi">1</span>
-            <span class="n">pool_stride</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-        <span class="n">pool_class_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">AvgPool2dWithConv</span><span class="p">)</span>
-        <span class="n">first_pool_class</span> <span class="o">=</span> <span class="n">pool_class_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">first_pool_mode</span><span class="p">]</span>
-        <span class="n">later_pool_class</span> <span class="o">=</span> <span class="n">pool_class_dict</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">later_pool_mode</span><span class="p">]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;dimshuffle&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">transpose_time_to_spat</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_time&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="mi">1</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_spat&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span><span class="p">,</span>
-                    <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="n">conv_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="n">n_filters_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_time&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="n">conv_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="n">n_filters_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;bnorm&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-                    <span class="n">n_filters_conv</span><span class="p">,</span>
-                    <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span><span class="p">,</span>
-                    <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;conv_nonlin&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_nonlin</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;pool&quot;</span><span class="p">,</span>
-            <span class="n">first_pool_class</span><span class="p">(</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="n">pool_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_nonlin&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_pool_nonlin</span><span class="p">))</span>
-
-        <span class="k">def</span> <span class="nf">add_conv_pool_block</span><span class="p">(</span>
-            <span class="n">model</span><span class="p">,</span> <span class="n">n_filters_before</span><span class="p">,</span> <span class="n">n_filters</span><span class="p">,</span> <span class="n">filter_length</span><span class="p">,</span> <span class="n">block_nr</span>
-        <span class="p">):</span>
-            <span class="n">suffix</span> <span class="o">=</span> <span class="s2">&quot;_</span><span class="si">{:d}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_nr</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="n">n_filters_before</span><span class="p">,</span>
-                    <span class="n">n_filters</span><span class="p">,</span>
-                    <span class="p">(</span><span class="n">filter_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="n">conv_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                    <span class="s2">&quot;bnorm&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span>
-                    <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-                        <span class="n">n_filters</span><span class="p">,</span>
-                        <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span><span class="p">,</span>
-                        <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">,</span>
-                    <span class="p">),</span>
-                <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;nonlin&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">later_conv_nonlin</span><span class="p">))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;pool&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span>
-                <span class="n">later_pool_class</span><span class="p">(</span>
-                    <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="n">pool_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;pool_nonlin&quot;</span> <span class="o">+</span> <span class="n">suffix</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">later_pool_nonlin</span><span class="p">)</span>
-            <span class="p">)</span>
-
-        <span class="n">add_conv_pool_block</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span> <span class="n">n_filters_conv</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_2</span><span class="p">,</span> <span class="mi">2</span>
-        <span class="p">)</span>
-        <span class="n">add_conv_pool_block</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_3</span><span class="p">,</span> <span class="mi">3</span>
-        <span class="p">)</span>
-        <span class="n">add_conv_pool_block</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter_length_4</span><span class="p">,</span> <span class="mi">4</span>
-        <span class="p">)</span>
-
-        <span class="c1"># self.add_module(&#39;drop_classifier&#39;, nn.Dropout(p=self.drop_prob))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
-                <span class="n">np_to_th</span><span class="p">(</span>
-                    <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
-                        <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">n_out_time</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">n_out_time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_classifier&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_4</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;squeeze&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">))</span>
-
-        <span class="c1"># Initialization, xavier is same as in our paper...</span>
-        <span class="c1"># was default from lasagne</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_time</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># maybe no bias in case of no split layer and batch norm</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">):</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_time</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_spat</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-                <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_spat</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bnorm</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bnorm</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">param_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()))</span>
-        <span class="k">for</span> <span class="n">block_nr</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
-            <span class="n">conv_weight</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s2">&quot;conv_</span><span class="si">{:d}</span><span class="s2">.weight&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_nr</span><span class="p">)]</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">conv_weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-                <span class="n">conv_bias</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s2">&quot;conv_</span><span class="si">{:d}</span><span class="s2">.bias&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_nr</span><span class="p">)]</span>
-                <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">conv_bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">bnorm_weight</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s2">&quot;bnorm_</span><span class="si">{:d}</span><span class="s2">.weight&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_nr</span><span class="p">)]</span>
-                <span class="n">bnorm_bias</span> <span class="o">=</span> <span class="n">param_dict</span><span class="p">[</span><span class="s2">&quot;bnorm_</span><span class="si">{:d}</span><span class="s2">.bias&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_nr</span><span class="p">)]</span>
-                <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">bnorm_weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">bnorm_bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_classifier</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_classifier</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="c1"># Start in eval mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span></div>
-</pre></div>
-
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deleted file mode 100644
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-              
-  <h1>Source code for braindecode.models.eeginception</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
-<span class="c1">#          Cedric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">prod</span>
-
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Expression</span><span class="p">,</span> <span class="n">Ensure4d</span>
-<span class="kn">from</span> <span class="nn">.eegnet</span> <span class="kn">import</span> <span class="n">_glorot_weight_zero_bias</span>
-<span class="kn">from</span> <span class="nn">.eegitnet</span> <span class="kn">import</span> <span class="n">_InceptionBlock</span><span class="p">,</span> <span class="n">_DepthwiseConv2d</span>
-
-
-<span class="k">def</span> <span class="nf">_transpose_to_b_1_c_0</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="EEGInception"><a class="viewcode-back" href="../../../generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception">[docs]</a><span class="k">class</span> <span class="nc">EEGInception</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;EEG Inception.</span>
-
-<span class="sd">    EEG Inception for ERP-based classification described in [Santamaria2020]_.</span>
-<span class="sd">    The code for the paper and this model is also available at [Santamaria2020]_</span>
-<span class="sd">    and an adaptation for PyTorch [2]_.</span>
-
-<span class="sd">    The model is strongly based on the original InceptionNet for an image. The main goal is</span>
-<span class="sd">    to extract features in parallel with different scales. The authors extracted three scales</span>
-<span class="sd">    proportional to the window sample size. The network had three parts:</span>
-<span class="sd">    1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck</span>
-<span class="sd">    for classification.</span>
-
-<span class="sd">    One advantage of the EEG-Inception block is that it allows a network</span>
-<span class="sd">    to learn simultaneous components of low and high frequency associated with the signal.</span>
-<span class="sd">    The winners of BEETL Competition/NeurIps 2021 used parts of the model [beetl]_.</span>
-
-<span class="sd">    The model is fully described in [Santamaria2020]_.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This implementation is not guaranteed to be correct, has not been checked</span>
-<span class="sd">    by original authors, only reimplemented from the paper based on [2]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_channels : int</span>
-<span class="sd">        Number of EEG channels.</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes.</span>
-<span class="sd">    input_size_ms : int</span>
-<span class="sd">        Size of the input, in milliseconds. Set to 1000 in [Santamaria2020]_.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        EEG sampling frequency.</span>
-<span class="sd">    drop_prob : float</span>
-<span class="sd">        Dropout rate inside all the network.</span>
-<span class="sd">    scales_time: list(int)</span>
-<span class="sd">        Windows for inception block, must be a list with proportional values of</span>
-<span class="sd">        the input_size_ms.</span>
-<span class="sd">        According to the authors: temporal scale (ms) of the convolutions</span>
-<span class="sd">        on each Inception module.</span>
-<span class="sd">        This parameter determines the kernel sizes of the filters.</span>
-<span class="sd">    n_filters : int</span>
-<span class="sd">        Initial number of convolutional filters. Set to 8 in [Santamaria2020]_.</span>
-<span class="sd">    activation: nn.Module</span>
-<span class="sd">        Activation function, default: ELU activation.</span>
-<span class="sd">    batch_norm_alpha: float</span>
-<span class="sd">        Momentum for BatchNorm2d.</span>
-<span class="sd">    depth_multiplier: int</span>
-<span class="sd">        Depth multiplier for the depthwise convolution.</span>
-<span class="sd">    pooling_sizes: list(int)</span>
-<span class="sd">        Pooling sizes for the inception block.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Santamaria2020] Santamaria-Vazquez, E., Martinez-Cagigal, V.,</span>
-<span class="sd">       Vaquerizo-Villar, F., &amp; Hornero, R. (2020).</span>
-<span class="sd">       EEG-inception: A novel deep convolutional neural network for assistive</span>
-<span class="sd">       ERP-based brain-computer interfaces.</span>
-<span class="sd">       IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.</span>
-<span class="sd">       Online: http://dx.doi.org/10.1109/TNSRE.2020.3048106</span>
-<span class="sd">    .. [2]  Grifcc. Implementation of the EEGInception in torch (2022).</span>
-<span class="sd">       Online: https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022</span>
-<span class="sd">    .. [beetl]_ Wei, X., Faisal, A.A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S.,</span>
-<span class="sd">       Jayaram, V., Jeunet, C., Bakas, S., Ludwig, S., Barmpas, K., Bahri, M., Panagakis,</span>
-<span class="sd">       Y., Laskaris, N., Adamos, D.A., Zafeiriou, S., Duong, W.C., Gordon, S.M.,</span>
-<span class="sd">       Lawhern, V.J., Śliwowski, M., Rouanne, V. &amp;amp; Tempczyk, P.. (2022).</span>
-<span class="sd">       2021 BEETL Competition: Advancing Transfer Learning for Subject Independence &amp;amp;</span>
-<span class="sd">       Heterogenous EEG Data Sets. &lt;i&gt;Proceedings of the NeurIPS 2021 Competitions and</span>
-<span class="sd">       Demonstrations Track&lt;/i&gt;, in &lt;i&gt;Proceedings of Machine Learning Research&lt;/i&gt;</span>
-<span class="sd">       176:205-219 Available from https://proceedings.mlr.press/v176/wei22a.html.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span>
-            <span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">n_classes</span><span class="p">,</span>
-            <span class="n">input_window_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-            <span class="n">sfreq</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-            <span class="n">scales_samples_s</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.125</span><span class="p">),</span>
-            <span class="n">n_filters</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
-            <span class="n">depth_multiplier</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-            <span class="n">pooling_sizes</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">in_channels</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span> <span class="o">=</span> <span class="n">sfreq</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span> <span class="o">=</span> <span class="n">n_filters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">scales_samples_s</span> <span class="o">=</span> <span class="n">scales_samples_s</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
-            <span class="nb">int</span><span class="p">(</span><span class="n">size_s</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sfreq</span><span class="p">)</span> <span class="k">for</span> <span class="n">size_s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scales_samples_s</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">activation</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span> <span class="o">=</span> <span class="n">batch_norm_alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">depth_multiplier</span> <span class="o">=</span> <span class="n">depth_multiplier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span> <span class="o">=</span> <span class="n">pooling_sizes</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;dimshuffle&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">_transpose_to_b_1_c_0</span><span class="p">))</span>
-
-        <span class="c1"># ======== Inception branches ========================</span>
-        <span class="n">block11</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_1</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">,</span>
-            <span class="n">depth_multiplier</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">depth_multiplier</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">block12</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_1</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">,</span>
-            <span class="n">depth_multiplier</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">depth_multiplier</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">block13</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_1</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">,</span>
-            <span class="n">depth_multiplier</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">depth_multiplier</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;inception_block_1&quot;</span><span class="p">,</span> <span class="n">_InceptionBlock</span><span class="p">((</span><span class="n">block11</span><span class="p">,</span> <span class="n">block12</span><span class="p">,</span> <span class="n">block13</span><span class="p">)))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;avg_pool_1&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
-
-        <span class="c1"># ======== Inception branches ========================</span>
-        <span class="n">n_concat_filters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span>
-        <span class="n">n_concat_dw_filters</span> <span class="o">=</span> <span class="n">n_concat_filters</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">depth_multiplier</span>
-        <span class="n">block21</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_2</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">n_concat_dw_filters</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">4</span><span class="p">,</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span>
-        <span class="p">)</span>
-        <span class="n">block22</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_2</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">n_concat_dw_filters</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">4</span><span class="p">,</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span>
-        <span class="p">)</span>
-        <span class="n">block23</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch_2</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">n_concat_dw_filters</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span><span class="p">,</span>
-            <span class="n">kernel_length</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">//</span> <span class="mi">4</span><span class="p">,</span>
-            <span class="n">alpha_momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">,</span>
-            <span class="n">activation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">,</span>
-            <span class="n">drop_prob</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;inception_block_2&quot;</span><span class="p">,</span> <span class="n">_InceptionBlock</span><span class="p">((</span><span class="n">block21</span><span class="p">,</span> <span class="n">block22</span><span class="p">,</span> <span class="n">block23</span><span class="p">)))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;avg_pool_2&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;final_block&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_concat_filters</span><span class="p">,</span>
-                <span class="n">n_concat_filters</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_concat_filters</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
-                           <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span><span class="p">[</span><span class="mi">2</span><span class="p">])),</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_concat_filters</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
-                <span class="n">n_concat_filters</span> <span class="o">//</span> <span class="mi">4</span><span class="p">,</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_concat_filters</span> <span class="o">//</span> <span class="mi">4</span><span class="p">,</span>
-                           <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_momentum</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span><span class="p">[</span><span class="mi">3</span><span class="p">])),</span>
-        <span class="p">))</span>
-
-        <span class="n">spatial_dim_last_layer</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">input_window_samples</span> <span class="o">//</span> <span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pooling_sizes</span><span class="p">))</span>
-        <span class="n">n_channels_last_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scales_samples</span><span class="p">)</span> <span class="o">//</span> <span class="mi">4</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;classification&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span>
-                <span class="n">spatial_dim_last_layer</span> <span class="o">*</span> <span class="n">n_channels_last_layer</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="p">))</span>
-
-        <span class="n">_glorot_weight_zero_bias</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_get_inception_branch_1</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">,</span>
-                                <span class="n">alpha_momentum</span><span class="p">,</span> <span class="n">drop_prob</span><span class="p">,</span> <span class="n">activation</span><span class="p">,</span>
-                                <span class="n">depth_multiplier</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="mi">1</span><span class="p">,</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">),</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">alpha_momentum</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-            <span class="n">_DepthwiseConv2d</span><span class="p">(</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">depth_multiplier</span><span class="o">=</span><span class="n">depth_multiplier</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;valid&quot;</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-                <span class="n">depth_multiplier</span> <span class="o">*</span> <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">momentum</span><span class="o">=</span><span class="n">alpha_momentum</span>
-            <span class="p">),</span>
-            <span class="n">activation</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-        <span class="p">)</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_get_inception_branch_2</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">,</span>
-                                <span class="n">alpha_momentum</span><span class="p">,</span> <span class="n">drop_prob</span><span class="p">,</span> <span class="n">activation</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="p">,</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">),</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">alpha_momentum</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-        <span class="p">)</span></div>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/models/eegitnet.html b/stable/_modules/braindecode/models/eegitnet.html
deleted file mode 100644
index de893c4d..00000000
--- a/stable/_modules/braindecode/models/eegitnet.html
+++ /dev/null
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-              
-  <h1>Source code for braindecode.models.eegitnet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Ghaith Bouallegue &lt;ghaithbouallegue@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD-3</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Ensure4d</span><span class="p">,</span> <span class="n">Expression</span>
-
-
-<span class="k">def</span> <span class="nf">_permute</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Permute data.</span>
-
-<span class="sd">    Input dimensions: (batch, channels, time, 1)</span>
-<span class="sd">    Output dimiensions: (batch, 1, channels, time)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-
-<span class="k">class</span> <span class="nc">_DepthwiseConv2d</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span>
-            <span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">depth_multiplier</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-            <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-            <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">padding_mode</span><span class="o">=</span><span class="s2">&quot;zeros&quot;</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="n">out_channels</span> <span class="o">=</span> <span class="n">in_channels</span> <span class="o">*</span> <span class="n">depth_multiplier</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
-            <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
-            <span class="n">groups</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
-            <span class="n">padding_mode</span><span class="o">=</span><span class="n">padding_mode</span><span class="p">,</span>
-        <span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_InceptionBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">branches</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">branches</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span><span class="n">branches</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">branch</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">branch</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">branches</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_TCBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_ch</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">,</span> <span class="n">dialation</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.4</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="n">padding</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">_DepthwiseConv2d</span><span class="p">(</span>
-                <span class="n">in_ch</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">),</span>
-                <span class="n">depth_multiplier</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dialation</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;valid&quot;</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">in_ch</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">tc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">_DepthwiseConv2d</span><span class="p">(</span>
-                <span class="n">in_ch</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">),</span>
-                <span class="n">depth_multiplier</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">dilation</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">dialation</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;valid&quot;</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">in_ch</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span>
-        <span class="n">paddings</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">paddings</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tc1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">paddings</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">residual</span>
-        <span class="k">return</span> <span class="n">x</span>
-
-
-<div class="viewcode-block" id="EEGITNet"><a class="viewcode-back" href="../../../generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet">[docs]</a><span class="k">class</span> <span class="nc">EEGITNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;EEG-ITNet: An Explainable Inception Temporal</span>
-<span class="sd">     Convolutional Network for motor imagery classification from</span>
-<span class="sd">     Salami et. al 2022.</span>
-
-<span class="sd">    See [Salami2022]_ for details.</span>
-
-<span class="sd">    Code adapted from https://github.com/abbassalami/eeg-itnet</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_classes: int</span>
-<span class="sd">        number of outputs of the decoding task (for example number of classes in</span>
-<span class="sd">        classification)</span>
-<span class="sd">    n_in_chans: int</span>
-<span class="sd">        number of input EEG channels</span>
-<span class="sd">    input_window_samples : int</span>
-<span class="sd">        Number of time samples.</span>
-<span class="sd">    drop_prob: float</span>
-<span class="sd">        Dropout probability.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Salami2022] A. Salami, J. Andreu-Perez and H. Gillmeister, &quot;EEG-ITNet: An Explainable</span>
-<span class="sd">    Inception Temporal Convolutional Network for motor imagery classification,&quot; in IEEE Access,</span>
-<span class="sd">    doi: 10.1109/ACCESS.2022.3161489.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This implementation is not guaranteed to be correct, has not been checked</span>
-<span class="sd">    by original authors, only reimplemented from the paper based on author implementation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.4</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="c1"># ======== Handling EEG input ========================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;input_preprocess&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">Ensure4d</span><span class="p">(),</span> <span class="n">Expression</span><span class="p">(</span><span class="n">_permute</span><span class="p">))</span>
-        <span class="p">)</span>
-        <span class="c1"># ======== Inception branches ========================</span>
-        <span class="n">block11</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">16</span>
-        <span class="p">)</span>
-        <span class="n">block12</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">32</span>
-        <span class="p">)</span>
-        <span class="n">block13</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_inception_branch</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">64</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;inception_block&quot;</span><span class="p">,</span> <span class="n">_InceptionBlock</span><span class="p">((</span><span class="n">block11</span><span class="p">,</span> <span class="n">block12</span><span class="p">,</span> <span class="n">block13</span><span class="p">)))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pooling&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-         <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span>
-         <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)))</span>
-        <span class="c1"># =========== TC blocks =====================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;TC_block1&quot;</span><span class="p">,</span>
-            <span class="n">_TCBlock</span><span class="p">(</span><span class="n">in_ch</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dialation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="c1"># ================================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;TC_block2&quot;</span><span class="p">,</span>
-            <span class="n">_TCBlock</span><span class="p">(</span><span class="n">in_ch</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dialation</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="c1"># ================================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;TC_block3&quot;</span><span class="p">,</span>
-            <span class="n">_TCBlock</span><span class="p">(</span><span class="n">in_ch</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dialation</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="p">)</span>
-        <span class="c1"># ================================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;TC_block4&quot;</span><span class="p">,</span>
-            <span class="n">_TCBlock</span><span class="p">(</span><span class="n">in_ch</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">kernel_length</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dialation</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="p">)</span>
-
-        <span class="c1"># ============= Dimensionality reduction ===================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;dim_reduction&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">28</span><span class="p">),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)))</span>
-        <span class="c1"># ============== Classifier ==================</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;classifier&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">input_window_samples</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="mi">28</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
-
-    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">_get_inception_branch</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">,</span> <span class="n">depth_multiplier</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">kernel_length</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
-            <span class="n">_DepthwiseConv2d</span><span class="p">(</span>
-                <span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">depth_multiplier</span><span class="o">=</span><span class="n">depth_multiplier</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;valid&quot;</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">())</span></div>
-</pre></div>
-
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deleted file mode 100644
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-              
-  <h1>Source code for braindecode.models.eegnet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">elu</span>
-
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Expression</span><span class="p">,</span> <span class="n">Ensure4d</span>
-<span class="kn">from</span> <span class="nn">.functions</span> <span class="kn">import</span> <span class="n">squeeze_final_output</span>
-
-
-<span class="k">class</span> <span class="nc">Conv2dWithConstraint</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">max_norm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span> <span class="o">=</span> <span class="n">max_norm</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Conv2dWithConstraint</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">renorm</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">maxnorm</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">max_norm</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">Conv2dWithConstraint</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="EEGNetv4"><a class="viewcode-back" href="../../../generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4">[docs]</a><span class="k">class</span> <span class="nc">EEGNetv4</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;EEGNet v4 model from Lawhern et al 2018.</span>
-
-<span class="sd">    See details in [EEGNet4]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        XXX</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This implementation is not guaranteed to be correct, has not been checked</span>
-<span class="sd">    by original authors, only reimplemented from the paper description.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [EEGNet4] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,</span>
-<span class="sd">       S. M., Hung, C. P., &amp; Lance, B. J. (2018).</span>
-<span class="sd">       EEGNet: A Compact Convolutional Network for EEG-based</span>
-<span class="sd">       Brain-Computer Interfaces.</span>
-<span class="sd">       arXiv preprint arXiv:1611.08024.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">in_chans</span><span class="p">,</span>
-        <span class="n">n_classes</span><span class="p">,</span>
-        <span class="n">input_window_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
-        <span class="n">pool_mode</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span>
-        <span class="n">F1</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
-        <span class="n">D</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="n">F2</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>  <span class="c1"># usually set to F1*D (?)</span>
-        <span class="n">kernel_length</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
-        <span class="n">third_kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-        <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">input_window_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">final_conv_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span> <span class="o">=</span> <span class="n">pool_mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">=</span> <span class="n">F1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">D</span> <span class="o">=</span> <span class="n">D</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">F2</span> <span class="o">=</span> <span class="n">F2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel_length</span> <span class="o">=</span> <span class="n">kernel_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">third_kernel_size</span> <span class="o">=</span> <span class="n">third_kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
-
-        <span class="n">pool_class</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-        <span class="c1"># b c 0 1</span>
-        <span class="c1"># now to b 1 0 c</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;dimshuffle&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">_transpose_to_b_1_c_0</span><span class="p">))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_temporal&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="mi">1</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span><span class="p">,</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel_length</span><span class="p">),</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel_length</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_temporal&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_spatial&quot;</span><span class="p">,</span>
-            <span class="n">Conv2dWithConstraint</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">max_norm</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">groups</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">F1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_1&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;elu_1&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">elu</span><span class="p">))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_1&quot;</span><span class="p">,</span> <span class="n">pool_class</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop_1&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-
-        <span class="c1"># https://discuss.pytorch.org/t/how-to-modify-a-conv2d-to-depthwise-separable-convolution/15843/7</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_separable_depth&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">groups</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">16</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_separable_point&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">D</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F2</span><span class="p">,</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_2&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">F2</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;elu_2&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">elu</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_2&quot;</span><span class="p">,</span> <span class="n">pool_class</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">)))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop_2&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">n_out_virtual_chans</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="n">n_out_time</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">n_out_time</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_classifier&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">F2</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="p">(</span><span class="n">n_out_virtual_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="c1"># Transpose back to the the logic of braindecode,</span>
-        <span class="c1"># so time in third dimension (axis=2)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;permute_back&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">_transpose_1_0</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;squeeze&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">))</span>
-
-        <span class="n">_glorot_weight_zero_bias</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_transpose_to_b_1_c_0</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_transpose_1_0</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="EEGNetv1"><a class="viewcode-back" href="../../../generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1">[docs]</a><span class="k">class</span> <span class="nc">EEGNetv1</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;EEGNet model from Lawhern et al. 2016.</span>
-
-<span class="sd">    See details in [EEGNet]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        XXX</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    This implementation is not guaranteed to be correct, has not been checked</span>
-<span class="sd">    by original authors, only reimplemented from the paper description.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [EEGNet] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,</span>
-<span class="sd">       S. M., Hung, C. P., &amp; Lance, B. J. (2016).</span>
-<span class="sd">       EEGNet: A Compact Convolutional Network for EEG-based</span>
-<span class="sd">       Brain-Computer Interfaces.</span>
-<span class="sd">       arXiv preprint arXiv:1611.08024.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">in_chans</span><span class="p">,</span>
-        <span class="n">n_classes</span><span class="p">,</span>
-        <span class="n">input_window_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
-        <span class="n">pool_mode</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span>
-        <span class="n">second_kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span>
-        <span class="n">third_kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
-        <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">input_window_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">final_conv_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span> <span class="o">=</span> <span class="n">pool_mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">second_kernel_size</span> <span class="o">=</span> <span class="n">second_kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">third_kernel_size</span> <span class="o">=</span> <span class="n">third_kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
-
-        <span class="n">pool_class</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-        <span class="n">n_filters_1</span> <span class="o">=</span> <span class="mi">16</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_1&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="n">n_filters_1</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_1&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_filters_1</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;elu_1&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">elu</span><span class="p">))</span>
-        <span class="c1"># transpose to examples x 1 x (virtual, not EEG) channels x time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;permute_1&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop_1&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-
-        <span class="n">n_filters_2</span> <span class="o">=</span> <span class="mi">4</span>
-        <span class="c1"># keras padds unequal padding more in front, so padding</span>
-        <span class="c1"># too large should be ok.</span>
-        <span class="c1"># Not padding in time so that cropped training makes sense</span>
-        <span class="c1"># https://stackoverflow.com/questions/43994604/padding-with-even-kernel-size-in-a-convolutional-layer-in-keras-theano</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_2&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="mi">1</span><span class="p">,</span>
-                <span class="n">n_filters_2</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">second_kernel_size</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">second_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_2&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_filters_2</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;elu_2&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">elu</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_2&quot;</span><span class="p">,</span> <span class="n">pool_class</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop_2&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-
-        <span class="n">n_filters_3</span> <span class="o">=</span> <span class="mi">4</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_3&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_filters_2</span><span class="p">,</span>
-                <span class="n">n_filters_3</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">third_kernel_size</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">third_kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;bnorm_3&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_filters_3</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;elu_3&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">elu</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_3&quot;</span><span class="p">,</span> <span class="n">pool_class</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop_3&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
-                <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-        <span class="n">n_out_virtual_chans</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="n">n_out_time</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">n_out_time</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_classifier&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_filters_3</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="p">(</span><span class="n">n_out_virtual_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="c1"># Transpose back to the the logic of braindecode,</span>
-        <span class="c1"># so time in third dimension (axis=2)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;permute_2&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;squeeze&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">))</span>
-        <span class="n">_glorot_weight_zero_bias</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_glorot_weight_zero_bias</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Initalize parameters of all modules by initializing weights with</span>
-<span class="sd">    glorot</span>
-<span class="sd">     uniform/xavier initialization, and setting biases to zero. Weights from</span>
-<span class="sd">     batch norm layers are set to 1.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    model: Module</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s2">&quot;weight&quot;</span><span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="s2">&quot;BatchNorm&quot;</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">):</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s2">&quot;bias&quot;</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">module</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/models/eegresnet.html b/stable/_modules/braindecode/models/eegresnet.html
deleted file mode 100644
index 7622e5d1..00000000
--- a/stable/_modules/braindecode/models/eegresnet.html
+++ /dev/null
@@ -1,665 +0,0 @@
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-        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
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-  <div class="search-button__wrapper">
-    <div class="search-button__overlay"></div>
-    <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
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-            <article class="bd-article" role="main">
-              
-  <h1>Source code for braindecode.models.eegresnet</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Tibor Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Tonio Ball</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD-3</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
-<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">elu</span>
-
-<span class="kn">from</span> <span class="nn">.functions</span> <span class="kn">import</span> <span class="n">transpose_time_to_spat</span><span class="p">,</span> <span class="n">squeeze_final_output</span>
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Expression</span><span class="p">,</span> <span class="n">AvgPool2dWithConv</span><span class="p">,</span> <span class="n">Ensure4d</span>
-
-
-<div class="viewcode-block" id="EEGResNet"><a class="viewcode-back" href="../../../generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet">[docs]</a><span class="k">class</span> <span class="nc">EEGResNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Residual Network for EEG.</span>
-
-<span class="sd">    XXX missing reference</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        XXX</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">in_chans</span><span class="p">,</span>
-                 <span class="n">n_classes</span><span class="p">,</span>
-                 <span class="n">input_window_samples</span><span class="p">,</span>
-                 <span class="n">final_pool_length</span><span class="p">,</span>
-                 <span class="n">n_first_filters</span><span class="p">,</span>
-                 <span class="n">n_layers_per_block</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">first_filter_length</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-                 <span class="n">nonlinearity</span><span class="o">=</span><span class="n">elu</span><span class="p">,</span>
-                 <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">batch_norm_epsilon</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">,</span>
-                 <span class="n">conv_weight_init_fn</span><span class="o">=</span><span class="k">lambda</span> <span class="n">w</span><span class="p">:</span> <span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">0</span><span class="p">)):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="k">if</span> <span class="n">final_pool_length</span> <span class="o">==</span> <span class="s1">&#39;auto&#39;</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">input_window_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="k">assert</span> <span class="n">first_filter_length</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_pool_length</span> <span class="o">=</span> <span class="n">final_pool_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span> <span class="o">=</span> <span class="n">n_first_filters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span> <span class="o">=</span> <span class="n">n_layers_per_block</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">first_filter_length</span> <span class="o">=</span> <span class="n">first_filter_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nonlinearity</span> <span class="o">=</span> <span class="n">nonlinearity</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span> <span class="o">=</span> <span class="n">split_first_layer</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span> <span class="o">=</span> <span class="n">batch_norm_alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_epsilon</span> <span class="o">=</span> <span class="n">batch_norm_epsilon</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv_weight_init_fn</span> <span class="o">=</span> <span class="n">conv_weight_init_fn</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;dimshuffle&#39;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">transpose_time_to_spat</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv_time&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span><span class="p">,</span>
-                                                   <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_filter_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                                                   <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                                                   <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_filter_length</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv_spat&#39;</span><span class="p">,</span>
-                            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span><span class="p">,</span>
-                                      <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">),</span>
-                                      <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                                      <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv_time&#39;</span><span class="p">,</span>
-                            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span><span class="p">,</span>
-                                      <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_filter_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                                      <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                                      <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">first_filter_length</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                                      <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,))</span>
-        <span class="n">n_filters_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_first_filters</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;bnorm&#39;</span><span class="p">,</span>
-                        <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">n_filters_conv</span><span class="p">,</span>
-                                       <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span><span class="p">,</span>
-                                       <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                       <span class="n">eps</span><span class="o">=</span><span class="mf">1e-5</span><span class="p">),)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv_nonlin&#39;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nonlinearity</span><span class="p">))</span>
-        <span class="n">cur_dilation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-        <span class="n">n_cur_filters</span> <span class="o">=</span> <span class="n">n_filters_conv</span>
-        <span class="n">i_block</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="n">n_out_filters</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">n_cur_filters</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_out_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="n">n_cur_filters</span> <span class="o">=</span> <span class="n">n_out_filters</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="n">n_out_filters</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.5</span> <span class="o">*</span> <span class="n">n_cur_filters</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_out_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="n">n_cur_filters</span> <span class="o">=</span> <span class="n">n_out_filters</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-        <span class="n">i_block</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-                        <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                       <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">,))</span>
-        <span class="k">for</span> <span class="n">i_layer</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_layers_per_block</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;res_</span><span class="si">{:d}</span><span class="s1">_</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i_block</span><span class="p">,</span> <span class="n">i_layer</span><span class="p">),</span>
-                            <span class="n">_ResidualBlock</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="n">n_cur_filters</span><span class="p">,</span>
-                                           <span class="n">dilation</span><span class="o">=</span><span class="n">cur_dilation</span><span class="p">))</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_pool_length</span> <span class="o">==</span> <span class="s1">&#39;auto&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;mean_pool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">pool_dilation</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">cur_dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">cur_dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;mean_pool&#39;</span><span class="p">,</span> <span class="n">AvgPool2dWithConv</span><span class="p">(</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_pool_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">dilation</span><span class="o">=</span><span class="n">pool_dilation</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv_classifier&#39;</span><span class="p">,</span>
-                        <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_cur_filters</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-                                  <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;softmax&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;squeeze&#39;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">))</span>
-
-        <span class="c1"># Initialize all weights</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">module</span><span class="p">:</span> <span class="n">_weights_init</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_weight_init_fn</span><span class="p">))</span>
-
-        <span class="c1"># Start in eval mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span></div>
-
-
-<span class="k">def</span> <span class="nf">_weights_init</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">conv_weight_init_fn</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    initialize weights</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">classname</span> <span class="o">=</span> <span class="n">module</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
-    <span class="k">if</span> <span class="s1">&#39;Conv&#39;</span> <span class="ow">in</span> <span class="n">classname</span> <span class="ow">and</span> <span class="n">classname</span> <span class="o">!=</span> <span class="s2">&quot;AvgPool2dWithConv&quot;</span><span class="p">:</span>
-        <span class="n">conv_weight_init_fn</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">module</span><span class="o">.</span><span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="s1">&#39;BatchNorm&#39;</span> <span class="ow">in</span> <span class="n">classname</span><span class="p">:</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_ResidualBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    create a residual learning building block with two stacked 3x3 convlayers as in paper</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_filters</span><span class="p">,</span>
-                 <span class="n">out_num_filters</span><span class="p">,</span>
-                 <span class="n">dilation</span><span class="p">,</span>
-                 <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
-                 <span class="n">nonlinearity</span><span class="o">=</span><span class="n">elu</span><span class="p">,</span>
-                 <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">batch_norm_epsilon</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_ResidualBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="n">time_padding</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">filter_time_length</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="k">assert</span> <span class="n">time_padding</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span>
-        <span class="n">time_padding</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">time_padding</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">dilation</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dilation</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">dilation</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
-        <span class="k">assert</span> <span class="p">(</span><span class="n">out_num_filters</span> <span class="o">-</span> <span class="n">in_filters</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="p">(</span>
-            <span class="s2">&quot;Need even number of extra channels in order to be able to &quot;</span>
-            <span class="s2">&quot;pad correctly&quot;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_pad_chans</span> <span class="o">=</span> <span class="n">out_num_filters</span> <span class="o">-</span> <span class="n">in_filters</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-            <span class="n">in_filters</span><span class="p">,</span> <span class="n">out_num_filters</span><span class="p">,</span> <span class="p">(</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">time_padding</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-            <span class="n">out_num_filters</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">batch_norm_alpha</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">eps</span><span class="o">=</span><span class="n">batch_norm_epsilon</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-            <span class="n">out_num_filters</span><span class="p">,</span> <span class="n">out_num_filters</span><span class="p">,</span> <span class="p">(</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">time_padding</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-            <span class="n">out_num_filters</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="n">batch_norm_alpha</span><span class="p">,</span>
-            <span class="n">affine</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">batch_norm_epsilon</span><span class="p">)</span>
-        <span class="c1"># also see https://mail.google.com/mail/u/0/#search/ilya+joos/1576137dd34c3127</span>
-        <span class="c1"># for resnet options as ilya used them</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nonlinearity</span> <span class="o">=</span> <span class="n">nonlinearity</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">stack_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nonlinearity</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
-        <span class="n">stack_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_2</span><span class="p">(</span><span class="n">stack_1</span><span class="p">))</span>  <span class="c1"># next nonlin after sum</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_pad_chans</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">zeros_for_padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_pad_chans</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">],</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">3</span><span class="p">]))</span>
-            <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
-                <span class="n">zeros_for_padding</span> <span class="o">=</span> <span class="n">zeros_for_padding</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">zeros_for_padding</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">zeros_for_padding</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">nonlinearity</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">stack_2</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">out</span>
-</pre></div>
-
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-  <h1>Source code for braindecode.models.hybrid</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">ConstantPad2d</span>
-
-<span class="kn">from</span> <span class="nn">.deep4</span> <span class="kn">import</span> <span class="n">Deep4Net</span>
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span>
-<span class="kn">from</span> <span class="nn">.shallow_fbcsp</span> <span class="kn">import</span> <span class="n">ShallowFBCSPNet</span>
-
-
-<div class="viewcode-block" id="HybridNet"><a class="viewcode-back" href="../../../generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet">[docs]</a><span class="k">class</span> <span class="nc">HybridNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Hybrid ConvNet model from Schirrmeister et al 2017.</span>
-
-<span class="sd">    See [Schirrmeister2017]_ for details.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,</span>
-<span class="sd">       L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.</span>
-<span class="sd">       &amp; Ball, T. (2017).</span>
-<span class="sd">       Deep learning with convolutional neural networks for EEG decoding and</span>
-<span class="sd">       visualization.</span>
-<span class="sd">       Human Brain Mapping , Aug. 2017.</span>
-<span class="sd">       Online: http://dx.doi.org/10.1002/hbm.23730</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">HybridNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="n">deep_model</span> <span class="o">=</span> <span class="n">Deep4Net</span><span class="p">(</span>
-            <span class="n">in_chans</span><span class="p">,</span>
-            <span class="n">n_classes</span><span class="p">,</span>
-            <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-            <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-            <span class="n">n_filters_2</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-            <span class="n">n_filters_3</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-            <span class="n">n_filters_4</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
-            <span class="n">input_window_samples</span><span class="o">=</span><span class="n">input_window_samples</span><span class="p">,</span>
-            <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">shallow_model</span> <span class="o">=</span> <span class="n">ShallowFBCSPNet</span><span class="p">(</span>
-            <span class="n">in_chans</span><span class="p">,</span>
-            <span class="n">n_classes</span><span class="p">,</span>
-            <span class="n">input_window_samples</span><span class="o">=</span><span class="n">input_window_samples</span><span class="p">,</span>
-            <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-            <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-            <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span>
-            <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">29</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">reduced_deep_model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">deep_model</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;conv_classifier&quot;</span><span class="p">:</span>
-                <span class="n">new_conv_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="n">module</span><span class="o">.</span><span class="n">in_channels</span><span class="p">,</span>
-                    <span class="mi">60</span><span class="p">,</span>
-                    <span class="n">kernel_size</span><span class="o">=</span><span class="n">module</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">,</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">reduced_deep_model</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;deep_final_conv&quot;</span><span class="p">,</span> <span class="n">new_conv_layer</span><span class="p">)</span>
-                <span class="k">break</span>
-            <span class="n">reduced_deep_model</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">module</span><span class="p">)</span>
-
-        <span class="n">reduced_shallow_model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">shallow_model</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;conv_classifier&quot;</span><span class="p">:</span>
-                <span class="n">new_conv_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="n">module</span><span class="o">.</span><span class="n">in_channels</span><span class="p">,</span>
-                    <span class="mi">40</span><span class="p">,</span>
-                    <span class="n">kernel_size</span><span class="o">=</span><span class="n">module</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">,</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="n">reduced_shallow_model</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                    <span class="s2">&quot;shallow_final_conv&quot;</span><span class="p">,</span> <span class="n">new_conv_layer</span>
-                <span class="p">)</span>
-                <span class="k">break</span>
-            <span class="n">reduced_shallow_model</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">module</span><span class="p">)</span>
-
-        <span class="n">to_dense_prediction_model</span><span class="p">(</span><span class="n">reduced_deep_model</span><span class="p">)</span>
-        <span class="n">to_dense_prediction_model</span><span class="p">(</span><span class="n">reduced_shallow_model</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reduced_deep_model</span> <span class="o">=</span> <span class="n">reduced_deep_model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reduced_shallow_model</span> <span class="o">=</span> <span class="n">reduced_shallow_model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-            <span class="mi">100</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span>
-        <span class="p">)</span>
-
-<div class="viewcode-block" id="HybridNet.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">deep_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduced_deep_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">shallow_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduced_shallow_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="n">n_diff_deep_shallow</span> <span class="o">=</span> <span class="n">deep_out</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">shallow_out</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="n">n_diff_deep_shallow</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">deep_out</span> <span class="o">=</span> <span class="n">ConstantPad2d</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="n">n_diff_deep_shallow</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mi">0</span><span class="p">)(</span>
-                <span class="n">deep_out</span>
-            <span class="p">)</span>
-        <span class="k">elif</span> <span class="n">n_diff_deep_shallow</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">shallow_out</span> <span class="o">=</span> <span class="n">ConstantPad2d</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">n_diff_deep_shallow</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="mi">0</span><span class="p">)(</span>
-                <span class="n">shallow_out</span>
-            <span class="p">)</span>
-
-        <span class="n">merged_out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">deep_out</span><span class="p">,</span> <span class="n">shallow_out</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">linear_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv</span><span class="p">(</span><span class="n">merged_out</span><span class="p">)</span>
-        <span class="n">softmaxed</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)(</span><span class="n">linear_out</span><span class="p">)</span>
-        <span class="n">squeezed</span> <span class="o">=</span> <span class="n">softmaxed</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">squeezed</span></div></div>
-</pre></div>
-
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-  <h1>Source code for braindecode.models.modules</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
-
-<span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">np_to_th</span>
-
-
-<span class="k">class</span> <span class="nc">Ensure4d</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span>
-
-
-<span class="k">class</span> <span class="nc">Expression</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute given expression on forward pass.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    expression_fn : callable</span>
-<span class="sd">        Should accept variable number of objects of type</span>
-<span class="sd">        `torch.autograd.Variable` to compute its output.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expression_fn</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Expression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span> <span class="o">=</span> <span class="n">expression_fn</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">x</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="p">,</span> <span class="s2">&quot;func&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="p">,</span> <span class="s2">&quot;kwargs&quot;</span>
-        <span class="p">):</span>
-            <span class="n">expression_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{:s}</span><span class="s2"> </span><span class="si">{:s}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
-            <span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="p">,</span> <span class="s2">&quot;__name__&quot;</span><span class="p">):</span>
-            <span class="n">expression_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="o">.</span><span class="vm">__name__</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">expression_str</span> <span class="o">=</span> <span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">expression_fn</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span>
-            <span class="s2">&quot;(expression=</span><span class="si">%s</span><span class="s2">) &quot;</span> <span class="o">%</span> <span class="n">expression_str</span>
-        <span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">AvgPool2dWithConv</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Compute average pooling using a convolution, to have the dilation parameter.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    kernel_size: (int,int)</span>
-<span class="sd">        Size of the pooling region.</span>
-<span class="sd">    stride: (int,int)</span>
-<span class="sd">        Stride of the pooling operation.</span>
-<span class="sd">    dilation: int or (int,int)</span>
-<span class="sd">        Dilation applied to the pooling filter.</span>
-<span class="sd">    padding: int or (int,int)</span>
-<span class="sd">        Padding applied before the pooling operation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">AvgPool2dWithConv</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span> <span class="o">=</span> <span class="n">dilation</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">=</span> <span class="n">padding</span>
-        <span class="c1"># don&#39;t name them &quot;weights&quot; to</span>
-        <span class="c1"># make sure these are not accidentally used by some procedure</span>
-        <span class="c1"># that initializes parameters or something</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="c1"># Create weights for the convolution on demand:</span>
-        <span class="c1"># size or type of x changed...</span>
-        <span class="n">in_channels</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">weight_shape</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">in_channels</span><span class="p">,</span>
-            <span class="mi">1</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span>
-            <span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">!=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">weight_shape</span><span class="p">))</span> <span class="ow">or</span>
-            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span><span class="o">.</span><span class="n">is_cuda</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">)</span> <span class="ow">or</span>
-            <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">type</span><span class="p">()</span> <span class="o">!=</span> <span class="n">x</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">type</span><span class="p">())</span>
-        <span class="p">):</span>
-            <span class="n">n_pool</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span><span class="p">)</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">np_to_th</span><span class="p">(</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">weight_shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">n_pool</span><span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
-                <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span> <span class="o">=</span> <span class="n">weights</span>
-
-        <span class="n">pooled</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span>
-            <span class="n">x</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_pool_weights</span><span class="p">,</span>
-            <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">padding</span><span class="p">,</span>
-            <span class="n">groups</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">pooled</span>
-
-
-<span class="k">class</span> <span class="nc">IntermediateOutputWrapper</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Wraps network model such that outputs of intermediate layers can be returned.</span>
-<span class="sd">    forward() returns list of intermediate activations in a network during forward pass.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    to_select : list</span>
-<span class="sd">        list of module names for which activation should be returned</span>
-<span class="sd">    model : model object</span>
-<span class="sd">        network model</span>
-
-<span class="sd">    Examples</span>
-<span class="sd">    --------</span>
-<span class="sd">    &gt;&gt;&gt; model = Deep4Net()</span>
-<span class="sd">    &gt;&gt;&gt; select_modules = [&#39;conv_spat&#39;,&#39;conv_2&#39;,&#39;conv_3&#39;,&#39;conv_4&#39;] # Specify intermediate outputs</span>
-<span class="sd">    &gt;&gt;&gt; model_pert = IntermediateOutputWrapper(select_modules,model) # Wrap model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">to_select</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">children</span><span class="p">()))</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">())):</span>
-            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;All modules in model need to have names!&quot;</span><span class="p">)</span>
-
-        <span class="nb">super</span><span class="p">(</span><span class="n">IntermediateOutputWrapper</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="n">modules_list</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">modules_list</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">module</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_to_select</span> <span class="o">=</span> <span class="n">to_select</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="c1"># Call modules individually and append activation to output if module is in to_select</span>
-        <span class="n">o</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_modules</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_to_select</span><span class="p">:</span>
-                <span class="n">o</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">o</span>
-
-
-<div class="viewcode-block" id="TimeDistributed"><a class="viewcode-back" href="../../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed">[docs]</a><span class="k">class</span> <span class="nc">TimeDistributed</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Apply module on multiple windows.</span>
-
-<span class="sd">    Apply the provided module on a sequence of windows and return their</span>
-<span class="sd">    concatenation.</span>
-<span class="sd">    Useful with sequence-to-prediction models (e.g. sleep stager which must map</span>
-<span class="sd">    a sequence of consecutive windows to the label of the middle window in the</span>
-<span class="sd">    sequence).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    module : nn.Module</span>
-<span class="sd">        Module to be applied to the input windows. Must accept an input of</span>
-<span class="sd">        shape (batch_size, n_channels, n_times).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
-
-<div class="viewcode-block" id="TimeDistributed.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x : torch.Tensor</span>
-<span class="sd">            Sequence of windows, of shape (batch_size, seq_len, n_channels,</span>
-<span class="sd">            n_times).</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        torch.Tensor</span>
-<span class="sd">            Shape (batch_size, seq_len, output_size).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span> <span class="o">*</span> <span class="n">s</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span><span class="p">))</span>
-        <span class="k">return</span> <span class="n">out</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span></div></div>
-</pre></div>
-
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
-</div>
-            </footer>
-            
-          </div>
-          
-          
-          
-            <div class="bd-sidebar-secondary bd-toc">
-              
-<div class="toc-item">
-  
-<div id="searchbox"></div>
-</div>
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-<div class="toc-item">
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-          <div class="bd-footer-content__inner">
-            
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-        </footer>
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-  <!-- Scripts loaded after <body> so the DOM is not blocked -->
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-
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diff --git a/stable/_modules/braindecode/models/shallow_fbcsp.html b/stable/_modules/braindecode/models/shallow_fbcsp.html
deleted file mode 100644
index 13c3e0d3..00000000
--- a/stable/_modules/braindecode/models/shallow_fbcsp.html
+++ /dev/null
@@ -1,627 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>braindecode.models.shallow_fbcsp &#8212; Braindecode 0.7 documentation</title>
-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
-    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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-  
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-  <link href="../../../_static/styles/theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
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-    href="../../../_static/vendor/fontawesome/6.1.2/css/all.min.css">
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-  <h1>Source code for braindecode.models.shallow_fbcsp</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
-
-<span class="kn">from</span> <span class="nn">..util</span> <span class="kn">import</span> <span class="n">np_to_th</span>
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Expression</span><span class="p">,</span> <span class="n">Ensure4d</span>
-<span class="kn">from</span> <span class="nn">.functions</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">safe_log</span><span class="p">,</span> <span class="n">square</span><span class="p">,</span> <span class="n">transpose_time_to_spat</span><span class="p">,</span> <span class="n">squeeze_final_output</span>
-<span class="p">)</span>
-
-
-<div class="viewcode-block" id="ShallowFBCSPNet"><a class="viewcode-back" href="../../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet">[docs]</a><span class="k">class</span> <span class="nc">ShallowFBCSPNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Shallow ConvNet model from Schirrmeister et al 2017.</span>
-
-<span class="sd">    Model described in [Schirrmeister2017]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        Number of EEG input channels.</span>
-<span class="sd">    n_classes: int</span>
-<span class="sd">        Number of classes to predict (number of output filters of last layer).</span>
-<span class="sd">    input_window_samples: int | None</span>
-<span class="sd">        Only used to determine the length of the last convolutional kernel if</span>
-<span class="sd">        final_conv_length is &quot;auto&quot;.</span>
-<span class="sd">    n_filters_time: int</span>
-<span class="sd">        Number of temporal filters.</span>
-<span class="sd">    filter_time_length: int</span>
-<span class="sd">        Length of the temporal filter.</span>
-<span class="sd">    n_filters_spat: int</span>
-<span class="sd">        Number of spatial filters.</span>
-<span class="sd">    pool_time_length: int</span>
-<span class="sd">        Length of temporal pooling filter.</span>
-<span class="sd">    pool_time_stride: int</span>
-<span class="sd">        Length of stride between temporal pooling filters.</span>
-<span class="sd">    final_conv_length: int | str</span>
-<span class="sd">        Length of the final convolution layer.</span>
-<span class="sd">        If set to &quot;auto&quot;, input_window_samples must not be None.</span>
-<span class="sd">    conv_nonlin: callable</span>
-<span class="sd">        Non-linear function to be used after convolution layers.</span>
-<span class="sd">    pool_mode: str</span>
-<span class="sd">        Method to use on pooling layers. &quot;max&quot; or &quot;mean&quot;.</span>
-<span class="sd">    pool_nonlin: callable</span>
-<span class="sd">        Non-linear function to be used after pooling layers.</span>
-<span class="sd">    split_first_layer: bool</span>
-<span class="sd">        Split first layer into temporal and spatial layers (True) or just use temporal (False).</span>
-<span class="sd">        There would be no non-linearity between the split layers.</span>
-<span class="sd">    batch_norm: bool</span>
-<span class="sd">        Whether to use batch normalisation.</span>
-<span class="sd">    batch_norm_alpha: float</span>
-<span class="sd">        Momentum for BatchNorm2d.</span>
-<span class="sd">    drop_prob: float</span>
-<span class="sd">        Dropout probability.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,</span>
-<span class="sd">       L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.</span>
-<span class="sd">       &amp; Ball, T. (2017).</span>
-<span class="sd">       Deep learning with convolutional neural networks for EEG decoding and</span>
-<span class="sd">       visualization.</span>
-<span class="sd">       Human Brain Mapping , Aug. 2017.</span>
-<span class="sd">       Online: http://dx.doi.org/10.1002/hbm.23730</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">in_chans</span><span class="p">,</span>
-        <span class="n">n_classes</span><span class="p">,</span>
-        <span class="n">input_window_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-        <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-        <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-        <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span>
-        <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-        <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-        <span class="n">conv_nonlin</span><span class="o">=</span><span class="n">square</span><span class="p">,</span>
-        <span class="n">pool_mode</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span>
-        <span class="n">pool_nonlin</span><span class="o">=</span><span class="n">safe_log</span><span class="p">,</span>
-        <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-        <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="k">assert</span> <span class="n">input_window_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span> <span class="o">=</span> <span class="n">n_filters_time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span> <span class="o">=</span> <span class="n">filter_time_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span> <span class="o">=</span> <span class="n">n_filters_spat</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_length</span> <span class="o">=</span> <span class="n">pool_time_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_time_stride</span> <span class="o">=</span> <span class="n">pool_time_stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">final_conv_length</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv_nonlin</span> <span class="o">=</span> <span class="n">conv_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span> <span class="o">=</span> <span class="n">pool_mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pool_nonlin</span> <span class="o">=</span> <span class="n">pool_nonlin</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span> <span class="o">=</span> <span class="n">split_first_layer</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span> <span class="o">=</span> <span class="n">batch_norm</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span> <span class="o">=</span> <span class="n">batch_norm_alpha</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span> <span class="o">=</span> <span class="n">drop_prob</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;ensuredims&quot;</span><span class="p">,</span> <span class="n">Ensure4d</span><span class="p">())</span>
-        <span class="n">pool_class</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_mode</span><span class="p">]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;dimshuffle&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">transpose_time_to_spat</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_time&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="mi">1</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_spat&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span><span class="p">,</span>
-                    <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                    <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="n">n_filters_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_spat</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;conv_time&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span><span class="p">,</span>
-                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">filter_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                    <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                    <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-            <span class="n">n_filters_conv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_filters_time</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-                <span class="s2">&quot;bnorm&quot;</span><span class="p">,</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span>
-                    <span class="n">n_filters_conv</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_norm_alpha</span><span class="p">,</span> <span class="n">affine</span><span class="o">=</span><span class="kc">True</span>
-                <span class="p">),</span>
-            <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;conv_nonlin_exp&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_nonlin</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;pool&quot;</span><span class="p">,</span>
-            <span class="n">pool_class</span><span class="p">(</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_time_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_time_stride</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;pool_nonlin_exp&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pool_nonlin</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;drop&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_prob</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="p">(</span>
-                <span class="n">np_to_th</span><span class="p">(</span>
-                    <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
-                        <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                        <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">n_out_time</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span> <span class="o">=</span> <span class="n">n_out_time</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span>
-            <span class="s2">&quot;conv_classifier&quot;</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_filters_conv</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_conv_length</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;squeeze&quot;</span><span class="p">,</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">))</span>
-
-        <span class="c1"># Initialization, xavier is same as in paper...</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_time</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># maybe no bias in case of no split layer and batch norm</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">):</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_time</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_first_layer</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_spat</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-                <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_spat</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bnorm</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bnorm</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_classifier</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv_classifier</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></div>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/models/sleep_stager_blanco_2020.html b/stable/_modules/braindecode/models/sleep_stager_blanco_2020.html
deleted file mode 100644
index e53cd261..00000000
--- a/stable/_modules/braindecode/models/sleep_stager_blanco_2020.html
+++ /dev/null
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-  <h1>Source code for braindecode.models.sleep_stager_blanco_2020</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-
-
-<div class="viewcode-block" id="SleepStagerBlanco2020"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020">[docs]</a><span class="k">class</span> <span class="nc">SleepStagerBlanco2020</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sleep staging architecture from Blanco et al 2020.</span>
-
-<span class="sd">    Convolutional neural network for sleep staging described in [Blanco2020]_.</span>
-<span class="sd">    A series of seven convolutional layers with kernel sizes running down from 7 to 3,</span>
-<span class="sd">    in an attempt to extract more general features at the beginning, while more specific</span>
-<span class="sd">    and complex features were extracted in the final stages.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_channels : int</span>
-<span class="sd">        Number of EEG channels.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        EEG sampling frequency.</span>
-<span class="sd">    n_conv_chans : int</span>
-<span class="sd">        Number of convolutional channels. Set to 20 in [Blanco2020]_.</span>
-<span class="sd">    n_groups : int</span>
-<span class="sd">        Number of groups for the convolution. Set to 2 in [Blanco2020]_ for 2 Channel EEG.</span>
-<span class="sd">        controls the connections between inputs and outputs. n_channels and n_conv_chans must be</span>
-<span class="sd">        divisible by n_groups.</span>
-<span class="sd">    input_size_s : float</span>
-<span class="sd">        Size of the input, in seconds.</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes.</span>
-<span class="sd">    dropout : float</span>
-<span class="sd">        Dropout rate before the output dense layer.</span>
-<span class="sd">    apply_batch_norm : bool</span>
-<span class="sd">        If True, apply batch normalization after both temporal convolutional</span>
-<span class="sd">        layers.</span>
-<span class="sd">    return_feats : bool</span>
-<span class="sd">        If True, return the features, i.e. the output of the feature extractor</span>
-<span class="sd">        (before the final linear layer). If False, pass the features through</span>
-<span class="sd">        the final linear layer.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Blanco2020] Fernandez-Blanco, E., Rivero, D. &amp; Pazos, A. Convolutional</span>
-<span class="sd">        neural networks for sleep stage scoring on a two-channel EEG signal.</span>
-<span class="sd">        Soft Comput 24, 4067–4079 (2020). https://doi.org/10.1007/s00500-019-04174-1</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">input_size_s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-                 <span class="n">n_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_groups</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">apply_batch_norm</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">return_feats</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="n">input_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">input_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_channels</span> <span class="o">=</span> <span class="n">n_channels</span>
-
-        <span class="n">batch_norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span> <span class="k">if</span> <span class="n">apply_batch_norm</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_groups</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">n_conv_chans</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">groups</span><span class="o">=</span><span class="n">n_conv_chans</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chans</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">))</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">len_last_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len_last_layer</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">input_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span> <span class="o">=</span> <span class="n">return_feats</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_feats</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">len_last_layer</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_len_last_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">input_size</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">input_size</span><span class="p">))</span>  <span class="c1"># batch_size,n_channels,height,width</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-
-<div class="viewcode-block" id="SleepStagerBlanco2020.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
-
-        <span class="n">feats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">feats</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">feats</span><span class="p">)</span></div></div>
-</pre></div>
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-  <h1>Source code for braindecode.models.sleep_stager_chambon_2018</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="SleepStagerChambon2018"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018">[docs]</a><span class="k">class</span> <span class="nc">SleepStagerChambon2018</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sleep staging architecture from Chambon et al 2018.</span>
-
-<span class="sd">    Convolutional neural network for sleep staging described in [Chambon2018]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_channels : int</span>
-<span class="sd">        Number of EEG channels.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        EEG sampling frequency.</span>
-<span class="sd">    n_conv_chs : int</span>
-<span class="sd">        Number of convolutional channels. Set to 8 in [Chambon2018]_.</span>
-<span class="sd">    time_conv_size_s : float</span>
-<span class="sd">        Size of filters in temporal convolution layers, in seconds. Set to 0.5</span>
-<span class="sd">        in [Chambon2018]_ (64 samples at sfreq=128).</span>
-<span class="sd">    max_pool_size_s : float</span>
-<span class="sd">        Max pooling size, in seconds. Set to 0.125 in [Chambon2018]_ (16</span>
-<span class="sd">        samples at sfreq=128).</span>
-<span class="sd">    pad_size_s : float</span>
-<span class="sd">        Padding size, in seconds. Set to 0.25 in [Chambon2018]_ (half the</span>
-<span class="sd">        temporal convolution kernel size).</span>
-<span class="sd">    input_size_s : float</span>
-<span class="sd">        Size of the input, in seconds.</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes.</span>
-<span class="sd">    dropout : float</span>
-<span class="sd">        Dropout rate before the output dense layer.</span>
-<span class="sd">    apply_batch_norm : bool</span>
-<span class="sd">        If True, apply batch normalization after both temporal convolutional</span>
-<span class="sd">        layers.</span>
-<span class="sd">    return_feats : bool</span>
-<span class="sd">        If True, return the features, i.e. the output of the feature extractor</span>
-<span class="sd">        (before the final linear layer). If False, pass the features through</span>
-<span class="sd">        the final linear layer.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Chambon2018] Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &amp;</span>
-<span class="sd">           Gramfort, A. (2018). A deep learning architecture for temporal sleep</span>
-<span class="sd">           stage classification using multivariate and multimodal time series.</span>
-<span class="sd">           IEEE Transactions on Neural Systems and Rehabilitation Engineering,</span>
-<span class="sd">           26(4), 758-769.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">n_conv_chs</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">time_conv_size_s</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-                 <span class="n">max_pool_size_s</span><span class="o">=</span><span class="mf">0.125</span><span class="p">,</span> <span class="n">pad_size_s</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">input_size_s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-                 <span class="n">n_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">apply_batch_norm</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">return_feats</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="n">time_conv_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">time_conv_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="n">max_pool_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">max_pool_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="n">input_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">input_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="n">pad_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">pad_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_channels</span> <span class="o">=</span> <span class="n">n_channels</span>
-
-        <span class="k">if</span> <span class="n">n_channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">spatial_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-
-        <span class="n">batch_norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span> <span class="k">if</span> <span class="n">apply_batch_norm</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="mi">1</span><span class="p">,</span> <span class="n">n_conv_chs</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">time_conv_size</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_size</span><span class="p">)),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chs</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
-                <span class="n">n_conv_chs</span><span class="p">,</span> <span class="n">n_conv_chs</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">time_conv_size</span><span class="p">),</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">pad_size</span><span class="p">)),</span>
-            <span class="n">batch_norm</span><span class="p">(</span><span class="n">n_conv_chs</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_pool_size</span><span class="p">))</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">len_last_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len_last_layer</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">input_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span> <span class="o">=</span> <span class="n">return_feats</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_feats</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">len_last_layer</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
-            <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_len_last_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">input_size</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_channels</span><span class="p">,</span> <span class="n">input_size</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-
-<div class="viewcode-block" id="SleepStagerChambon2018.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_channels</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-        <span class="n">feats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">feats</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">feats</span><span class="p">)</span></div></div>
-</pre></div>
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diff --git a/stable/_modules/braindecode/models/sleep_stager_eldele_2021.html b/stable/_modules/braindecode/models/sleep_stager_eldele_2021.html
deleted file mode 100644
index 29f89118..00000000
--- a/stable/_modules/braindecode/models/sleep_stager_eldele_2021.html
+++ /dev/null
@@ -1,823 +0,0 @@
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-              
-  <h1>Source code for braindecode.models.sleep_stager_eldele_2021</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
-
-
-<div class="viewcode-block" id="SleepStagerEldele2021"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021">[docs]</a><span class="k">class</span> <span class="nc">SleepStagerEldele2021</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sleep Staging Architecture from Eldele et al 2021.</span>
-
-<span class="sd">    Attention based Neural Net for sleep staging as described in [Eldele2021]_.</span>
-<span class="sd">    The code for the paper and this model is also available at [1]_.</span>
-<span class="sd">    Takes single channel EEG as input.</span>
-<span class="sd">    Feature extraction module based on multi-resolution convolutional neural network (MRCNN)</span>
-<span class="sd">    and adaptive feature recalibration (AFR).</span>
-<span class="sd">    The second module is the temporal context encoder (TCE) that leverages a multi-head attention</span>
-<span class="sd">    mechanism to capture the temporal dependencies among the extracted features.</span>
-
-<span class="sd">    Warning - This model was designed for signals of 30 seconds at 100Hz or 125Hz (in which case</span>
-<span class="sd">    the reference architecture from [1]_ which was validated on SHHS dataset [2]_ will be used)</span>
-<span class="sd">    to use any other input is likely to make the model perform in unintended ways.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        EEG sampling frequency.</span>
-<span class="sd">    n_tce : int</span>
-<span class="sd">        Number of TCE clones.</span>
-<span class="sd">    d_model : int</span>
-<span class="sd">        Input dimension for the TCE.</span>
-<span class="sd">        Also the input dimension of the first FC layer in the feed forward</span>
-<span class="sd">        and the output of the second FC layer in the same.</span>
-<span class="sd">        Increase for higher sampling rate/signal length.</span>
-<span class="sd">        It should be divisible by n_attn_heads</span>
-<span class="sd">    d_ff : int</span>
-<span class="sd">        Output dimension of the first FC layer in the feed forward and the</span>
-<span class="sd">        input dimension of the second FC layer in the same.</span>
-<span class="sd">    n_attn_heads : int</span>
-<span class="sd">        Number of attention heads. It should be a factor of d_model</span>
-<span class="sd">    dropout : float</span>
-<span class="sd">        Dropout rate in the PositionWiseFeedforward layer and the TCE layers.</span>
-<span class="sd">    input_size_s : float</span>
-<span class="sd">        Size of the input, in seconds.</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes.</span>
-<span class="sd">    after_reduced_cnn_size : int</span>
-<span class="sd">        Number of output channels produced by the convolution in the AFR module.</span>
-<span class="sd">    return_feats : bool</span>
-<span class="sd">        If True, return the features, i.e. the output of the feature extractor</span>
-<span class="sd">        (before the final linear layer). If False, pass the features through</span>
-<span class="sd">        the final linear layer.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Eldele2021] E. Eldele et al., &quot;An Attention-Based Deep Learning Approach for Sleep Stage</span>
-<span class="sd">        Classification With Single-Channel EEG,&quot; in IEEE Transactions on Neural Systems and</span>
-<span class="sd">        Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</span>
-
-<span class="sd">    .. [1] https://github.com/emadeldeen24/AttnSleep</span>
-
-<span class="sd">    .. [2] https://sleepdata.org/datasets/shhs</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">n_tce</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">d_model</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">d_ff</span><span class="o">=</span><span class="mi">120</span><span class="p">,</span> <span class="n">n_attn_heads</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">input_size_s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">return_feats</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">SleepStagerEldele2021</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="n">input_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">input_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">input_size_s</span> <span class="o">==</span> <span class="mi">30</span> <span class="ow">and</span> <span class="n">sfreq</span> <span class="o">==</span> <span class="mi">100</span> <span class="ow">and</span> <span class="n">d_model</span> <span class="o">==</span> <span class="mi">80</span><span class="p">)</span> <span class="ow">or</span>
-                <span class="p">(</span><span class="n">input_size_s</span> <span class="o">==</span> <span class="mi">30</span> <span class="ow">and</span> <span class="n">sfreq</span> <span class="o">==</span> <span class="mi">125</span> <span class="ow">and</span> <span class="n">d_model</span> <span class="o">==</span> <span class="mi">100</span><span class="p">)):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This model was designed originally for input windows of 30sec at 100Hz, &quot;</span>
-                          <span class="s2">&quot;with d_model at 80 or at 125Hz, with d_model at 100, to use anything &quot;</span>
-                          <span class="s2">&quot;other than this may cause errors or cause the model to perform in &quot;</span>
-                          <span class="s2">&quot;other ways than intended&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-
-        <span class="c1"># the usual kernel size for the mrcnn, for sfreq 100</span>
-        <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">7</span>
-
-        <span class="k">if</span> <span class="n">sfreq</span> <span class="o">==</span> <span class="mi">125</span><span class="p">:</span>
-            <span class="n">kernel_size</span> <span class="o">=</span> <span class="mi">6</span>
-
-        <span class="n">mrcnn</span> <span class="o">=</span> <span class="n">_MRCNN</span><span class="p">(</span><span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">)</span>
-        <span class="n">attn</span> <span class="o">=</span> <span class="n">_MultiHeadedAttention</span><span class="p">(</span><span class="n">n_attn_heads</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">)</span>
-        <span class="n">ff</span> <span class="o">=</span> <span class="n">_PositionwiseFeedForward</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">d_ff</span><span class="p">,</span> <span class="n">dropout</span><span class="p">)</span>
-        <span class="n">tce</span> <span class="o">=</span> <span class="n">_TCE</span><span class="p">(</span><span class="n">_EncoderLayer</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">attn</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">ff</span><span class="p">),</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span>
-                                 <span class="n">dropout</span><span class="p">),</span> <span class="n">n_tce</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">mrcnn</span><span class="p">,</span> <span class="n">tce</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">len_last_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_len_last_layer</span><span class="p">(</span><span class="n">input_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span> <span class="o">=</span> <span class="n">return_feats</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">return_feats</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span> <span class="o">*</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_len_last_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-            <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">input_size</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-
-<div class="viewcode-block" id="SleepStagerEldele2021.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">encoded_features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_extractor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">encoded_features</span> <span class="o">=</span> <span class="n">encoded_features</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">encoded_features</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_feats</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">encoded_features</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">final_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">encoded_features</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">final_output</span></div></div>
-
-
-<span class="k">class</span> <span class="nc">_SELayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">channel</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="mi">16</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_SELayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool1d</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">channel</span><span class="p">,</span> <span class="n">channel</span> <span class="o">//</span> <span class="n">reduction</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">channel</span> <span class="o">//</span> <span class="n">reduction</span><span class="p">,</span> <span class="n">channel</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avg_pool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="o">.</span><span class="n">expand_as</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_SEBasicBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="n">expansion</span> <span class="o">=</span> <span class="mi">1</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">downsample</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">base_width</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="o">*</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="mi">16</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_SEBasicBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">planes</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">planes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">planes</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">se</span> <span class="o">=</span> <span class="n">_SELayer</span><span class="p">(</span><span class="n">planes</span><span class="p">,</span> <span class="n">reduction</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">downsample</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">,</span>
-                                      <span class="bp">self</span><span class="o">.</span><span class="n">se</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="n">out</span> <span class="o">+=</span> <span class="n">residual</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">out</span>
-
-
-<span class="k">class</span> <span class="nc">_MRCNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_MRCNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="n">drate</span> <span class="o">=</span> <span class="mf">0.5</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">GELU</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">features1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">24</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">64</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drate</span><span class="p">),</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">4</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">features2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">200</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">64</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drate</span><span class="p">),</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="mi">128</span><span class="p">),</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">GELU</span><span class="p">,</span>
-
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">drate</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="mi">128</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">AFR</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="n">_SEBasicBlock</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">blocks</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>  <span class="c1"># makes residual SE block</span>
-        <span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">!=</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">:</span>
-            <span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">,</span>
-                          <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span><span class="p">),</span>
-            <span class="p">)</span>
-
-        <span class="n">layers</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">downsample</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">=</span> <span class="n">planes</span> <span class="o">*</span> <span class="n">block</span><span class="o">.</span><span class="n">expansion</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">blocks</span><span class="p">):</span>
-            <span class="n">layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">block</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">planes</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">layers</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x_concat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">x_concat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">x_concat</span><span class="p">)</span>
-        <span class="n">x_concat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">AFR</span><span class="p">(</span><span class="n">x_concat</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x_concat</span>
-
-
-<span class="c1">##########################################################################################</span>
-
-
-<span class="k">def</span> <span class="nf">_attention</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Implementation of Scaled dot product attention&quot;&quot;&quot;</span>
-    <span class="c1"># d_k - dimension of the query and key vectors</span>
-    <span class="n">d_k</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">scores</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">d_k</span><span class="p">)</span>
-
-    <span class="n">p_attn</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">dropout</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">p_attn</span> <span class="o">=</span> <span class="n">dropout</span><span class="p">(</span><span class="n">p_attn</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">p_attn</span><span class="p">,</span> <span class="n">value</span><span class="p">),</span> <span class="n">p_attn</span>
-
-
-<span class="k">class</span> <span class="nc">_CausalConv1d</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">in_channels</span><span class="p">,</span>
-                 <span class="n">out_channels</span><span class="p">,</span>
-                 <span class="n">kernel_size</span><span class="p">,</span>
-                 <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">__padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dilation</span>
-
-        <span class="nb">super</span><span class="p">(</span><span class="n">_CausalConv1d</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">in_channels</span><span class="p">,</span>
-            <span class="n">out_channels</span><span class="p">,</span>
-            <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
-            <span class="n">padding</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">__padding</span><span class="p">,</span>
-            <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
-            <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
-            <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-        <span class="n">result</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">_CausalConv1d</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">__padding</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">result</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">__padding</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">result</span>
-
-
-<span class="k">class</span> <span class="nc">_MultiHeadedAttention</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Take in model size and number of heads.&quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_MultiHeadedAttention</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">assert</span> <span class="n">d_model</span> <span class="o">%</span> <span class="n">h</span> <span class="o">==</span> <span class="mi">0</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">d_per_head</span> <span class="o">=</span> <span class="n">d_model</span> <span class="o">//</span> <span class="n">h</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">h</span> <span class="o">=</span> <span class="n">h</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">convs</span> <span class="o">=</span> <span class="n">_clones</span><span class="p">(</span><span class="n">_CausalConv1d</span><span class="p">(</span><span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span>
-                                           <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">d_model</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Implements Multi-head attention&quot;&quot;&quot;</span>
-        <span class="n">nbatches</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">query</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">nbatches</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_per_head</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convs</span><span class="p">[</span><span class="mi">1</span><span class="p">](</span><span class="n">key</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">nbatches</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_per_head</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convs</span><span class="p">[</span><span class="mi">2</span><span class="p">](</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">nbatches</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_per_head</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-
-        <span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">attn</span> <span class="o">=</span> <span class="n">_attention</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> \
-            <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">nbatches</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">h</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">d_per_head</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_SublayerOutput</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A residual connection followed by a layer norm.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">dropout</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_SublayerOutput</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">sublayer</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Apply residual connection to any sublayer with the same size.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">sublayer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
-
-
-<span class="k">def</span> <span class="nf">_clones</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Produce n identical layers.&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">([</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">module</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">)])</span>
-
-
-<span class="k">class</span> <span class="nc">_TCE</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transformer Encoder</span>
-<span class="sd">    It is a stack of n layers.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">layer</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_TCE</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">layers</span> <span class="o">=</span> <span class="n">_clones</span><span class="p">(</span><span class="n">layer</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">norm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LayerNorm</span><span class="p">(</span><span class="n">layer</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">layers</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_EncoderLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    An encoder layer</span>
-<span class="sd">    Made up of self-attention and a feed forward layer.</span>
-<span class="sd">    Each of these sublayers have residual and layer norm, implemented by _SublayerOutput.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">self_attn</span><span class="p">,</span> <span class="n">feed_forward</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">dropout</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_EncoderLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span> <span class="o">=</span> <span class="n">self_attn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span> <span class="o">=</span> <span class="n">feed_forward</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sublayer_output</span> <span class="o">=</span> <span class="n">_clones</span><span class="p">(</span><span class="n">_SublayerOutput</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">dropout</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">_CausalConv1d</span><span class="p">(</span><span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">after_reduced_cnn_size</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
-                                  <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x_in</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Transformer Encoder&quot;&quot;&quot;</span>
-        <span class="n">query</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="n">x_in</span><span class="p">)</span>
-        <span class="c1"># Encoder self-attention</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sublayer_output</span><span class="p">[</span><span class="mi">0</span><span class="p">](</span><span class="n">query</span><span class="p">,</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">self_attn</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">x_in</span><span class="p">,</span> <span class="n">x_in</span><span class="p">))</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sublayer_output</span><span class="p">[</span><span class="mi">1</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">feed_forward</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_PositionwiseFeedForward</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Positionwise feed-forward network.&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">d_ff</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">_PositionwiseFeedForward</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">w_1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">d_ff</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">w_2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_ff</span><span class="p">,</span> <span class="n">d_model</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Implements FFN equation.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w_2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">w_1</span><span class="p">(</span><span class="n">x</span><span class="p">))))</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/models/tcn.html b/stable/_modules/braindecode/models/tcn.html
deleted file mode 100644
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-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>braindecode.models.tcn &#8212; Braindecode 0.7 documentation</title>
-  <script>
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-              
-  <h1>Source code for braindecode.models.tcn</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Patryk Chrabaszcz</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD-3</span>
-
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
-<span class="kn">from</span> <span class="nn">torch.nn.utils</span> <span class="kn">import</span> <span class="n">weight_norm</span>
-
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Ensure4d</span><span class="p">,</span> <span class="n">Expression</span>
-<span class="kn">from</span> <span class="nn">.functions</span> <span class="kn">import</span> <span class="n">squeeze_final_output</span>
-
-
-<div class="viewcode-block" id="TCN"><a class="viewcode-back" href="../../../generated/braindecode.models.TCN.html#braindecode.models.TCN">[docs]</a><span class="k">class</span> <span class="nc">TCN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Temporal Convolutional Network (TCN) from Bai et al 2018.</span>
-
-<span class="sd">    See [Bai2018]_ for details.</span>
-
-<span class="sd">    Code adapted from https://github.com/locuslab/TCN/blob/master/TCN/tcn.py</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_in_chans: int</span>
-<span class="sd">        number of input EEG channels</span>
-<span class="sd">    n_outputs: int</span>
-<span class="sd">        number of outputs of the decoding task (for example number of classes in</span>
-<span class="sd">        classification)</span>
-<span class="sd">    n_filters: int</span>
-<span class="sd">        number of output filters of each convolution</span>
-<span class="sd">    n_blocks: int</span>
-<span class="sd">        number of temporal blocks in the network</span>
-<span class="sd">    kernel_size: int</span>
-<span class="sd">        kernel size of the convolutions</span>
-<span class="sd">    drop_prob: float</span>
-<span class="sd">        dropout probability</span>
-<span class="sd">    add_log_softmax: bool</span>
-<span class="sd">        whether to add a log softmax layer</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Bai2018] Bai, S., Kolter, J. Z., &amp; Koltun, V. (2018).</span>
-<span class="sd">       An empirical evaluation of generic convolutional and recurrent networks</span>
-<span class="sd">       for sequence modeling.</span>
-<span class="sd">       arXiv preprint arXiv:1803.01271.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_in_chans</span><span class="p">,</span> <span class="n">n_outputs</span><span class="p">,</span> <span class="n">n_blocks</span><span class="p">,</span> <span class="n">n_filters</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span>
-                 <span class="n">drop_prob</span><span class="p">,</span> <span class="n">add_log_softmax</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ensuredims</span> <span class="o">=</span> <span class="n">Ensure4d</span><span class="p">()</span>
-        <span class="n">t_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_blocks</span><span class="p">):</span>
-            <span class="n">n_inputs</span> <span class="o">=</span> <span class="n">n_in_chans</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">n_filters</span>
-            <span class="n">dilation_size</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="n">i</span>
-            <span class="n">t_blocks</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s2">&quot;temporal_block_</span><span class="si">{:d}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">TemporalBlock</span><span class="p">(</span>
-                <span class="n">n_inputs</span><span class="o">=</span><span class="n">n_inputs</span><span class="p">,</span>
-                <span class="n">n_outputs</span><span class="o">=</span><span class="n">n_filters</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">dilation</span><span class="o">=</span><span class="n">dilation_size</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dilation_size</span><span class="p">,</span>
-                <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span>
-            <span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">temporal_blocks</span> <span class="o">=</span> <span class="n">t_blocks</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_features</span><span class="o">=</span><span class="n">n_filters</span><span class="p">,</span> <span class="n">out_features</span><span class="o">=</span><span class="n">n_outputs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">add_log_softmax</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">log_softmax</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">squeeze</span> <span class="o">=</span> <span class="n">Expression</span><span class="p">(</span><span class="n">squeeze_final_output</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">min_len</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_blocks</span><span class="p">):</span>
-            <span class="n">dilation</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">**</span> <span class="n">i</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">min_len</span> <span class="o">+=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dilation</span>
-
-        <span class="c1"># start in eval mode</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-
-<div class="viewcode-block" id="TCN.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.TCN.html#braindecode.models.TCN.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensuredims</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="c1"># x is in format: B x C x T x 1</span>
-        <span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">time_size</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
-        <span class="k">assert</span> <span class="n">time_size</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_len</span>
-        <span class="c1"># remove empty trailing dimension</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">temporal_blocks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="c1"># Convert to: B x T x C</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
-
-        <span class="n">fc_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">time_size</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)))</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;log_softmax&quot;</span><span class="p">):</span>
-            <span class="n">fc_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">log_softmax</span><span class="p">(</span><span class="n">fc_out</span><span class="p">)</span>
-        <span class="n">fc_out</span> <span class="o">=</span> <span class="n">fc_out</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">time_size</span><span class="p">,</span> <span class="n">fc_out</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-
-        <span class="n">out_size</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">time_size</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_len</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="n">fc_out</span><span class="p">[:,</span> <span class="o">-</span><span class="n">out_size</span><span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="c1"># re-add 4th dimension for compatibility with braindecode</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">out</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="kc">None</span><span class="p">])</span></div></div>
-
-
-<span class="k">class</span> <span class="nc">TemporalBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_inputs</span><span class="p">,</span> <span class="n">n_outputs</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">dilation</span><span class="p">,</span>
-                 <span class="n">padding</span><span class="p">,</span> <span class="n">drop_prob</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">weight_norm</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-            <span class="n">n_inputs</span><span class="p">,</span> <span class="n">n_outputs</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">chomp1</span> <span class="o">=</span> <span class="n">Chomp1d</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">relu1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">weight_norm</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-            <span class="n">n_outputs</span><span class="p">,</span> <span class="n">n_outputs</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span>
-            <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">chomp2</span> <span class="o">=</span> <span class="n">Chomp1d</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">relu2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">,</span> <span class="n">n_outputs</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-                           <span class="k">if</span> <span class="n">n_inputs</span> <span class="o">!=</span> <span class="n">n_outputs</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
-
-        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chomp1</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu1</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">chomp2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">x</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">out</span> <span class="o">+</span> <span class="n">res</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">Chomp1d</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">chomp_size</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">chomp_size</span> <span class="o">=</span> <span class="n">chomp_size</span>
-
-    <span class="k">def</span> <span class="nf">extra_repr</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="s1">&#39;chomp_size=</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">chomp_size</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">x</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">chomp_size</span><span class="p">]</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/models/tidnet.html b/stable/_modules/braindecode/models/tidnet.html
deleted file mode 100644
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-  <h1>Source code for braindecode.models.tidnet</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">ceil</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">init</span>
-<span class="kn">from</span> <span class="nn">torch.nn.utils</span> <span class="kn">import</span> <span class="n">weight_norm</span>
-
-<span class="kn">from</span> <span class="nn">.modules</span> <span class="kn">import</span> <span class="n">Ensure4d</span><span class="p">,</span> <span class="n">Expression</span>
-
-
-<span class="k">class</span> <span class="nc">_BatchNormZG</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">reset_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">track_running_stats</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">running_mean</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">running_var</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">affine</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
-
-
-<span class="k">class</span> <span class="nc">_ConvBlock2D</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Implements Convolution block with order:</span>
-<span class="sd">    Convolution, dropout, activation, batch-norm</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_filters</span><span class="p">,</span> <span class="n">out_filters</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">,</span> <span class="n">residual</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="n">kernel</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">activation</span> <span class="o">=</span> <span class="n">activation</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual</span> <span class="o">=</span> <span class="n">residual</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_filters</span><span class="p">,</span> <span class="n">out_filters</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
-                              <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="ow">not</span> <span class="n">batch_norm</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span> <span class="o">=</span> <span class="n">_BatchNormZG</span><span class="p">(</span><span class="n">out_filters</span><span class="p">)</span> <span class="k">if</span> <span class="n">residual</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">out_filters</span><span class="p">)</span> <span class="k">if</span>\
-            <span class="n">batch_norm</span> <span class="k">else</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="nb">input</span>
-        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv</span><span class="p">(</span><span class="nb">input</span><span class="p">,)</span>
-        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">activation</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-        <span class="nb">input</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_norm</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-        <span class="k">return</span> <span class="nb">input</span> <span class="o">+</span> <span class="n">res</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual</span> <span class="k">else</span> <span class="nb">input</span>
-
-
-<span class="k">class</span> <span class="nc">_DenseFilter</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_features</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">filter_len</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">bottleneck</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="n">dim</span> <span class="o">=</span> <span class="n">dim</span> <span class="k">if</span> <span class="n">dim</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">dim</span> <span class="o">+</span> <span class="mi">4</span>
-        <span class="k">if</span> <span class="n">dim</span> <span class="o">&lt;</span> <span class="mi">2</span> <span class="ow">or</span> <span class="n">dim</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Only last two dimensions supported&#39;</span><span class="p">)</span>
-        <span class="n">kernel</span> <span class="o">=</span> <span class="p">(</span><span class="n">filter_len</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">dim</span> <span class="o">==</span> <span class="mi">2</span> <span class="k">else</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">filter_len</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">in_features</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_features</span><span class="p">,</span> <span class="n">bottleneck</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">bottleneck</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">),</span>
-            <span class="n">activation</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">bottleneck</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span>
-                      <span class="n">padding</span><span class="o">=</span><span class="nb">tuple</span><span class="p">((</span><span class="n">k</span> <span class="o">//</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">kernel</span><span class="p">))),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-
-<span class="k">class</span> <span class="nc">_DenseSpatialFilter</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">growth</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">in_ch</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bottleneck</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
-                 <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">,</span> <span class="n">collapse</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="p">[</span>
-            <span class="n">_DenseFilter</span><span class="p">(</span><span class="n">in_ch</span> <span class="o">+</span> <span class="n">growth</span> <span class="o">*</span> <span class="n">d</span><span class="p">,</span> <span class="n">growth</span><span class="p">,</span> <span class="n">bottleneck</span><span class="o">=</span><span class="n">bottleneck</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">,</span>
-                         <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span><span class="p">)</span>
-        <span class="p">])</span>
-        <span class="n">n_filters</span> <span class="o">=</span> <span class="n">in_ch</span> <span class="o">+</span> <span class="n">growth</span> <span class="o">*</span> <span class="n">depth</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">collapse</span> <span class="o">=</span> <span class="n">collapse</span>
-        <span class="k">if</span> <span class="n">collapse</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">channel_collapse</span> <span class="o">=</span> <span class="n">_ConvBlock2D</span><span class="p">(</span><span class="n">n_filters</span><span class="p">,</span> <span class="n">n_filters</span><span class="p">,</span> <span class="p">(</span><span class="n">in_chans</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">4</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">permute</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">collapse</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">channel_collapse</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span>
-
-
-<span class="k">class</span> <span class="nc">_TemporalFilter</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">depth</span><span class="p">,</span> <span class="n">temp_len</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">,</span>
-                 <span class="n">residual</span><span class="o">=</span><span class="s1">&#39;netwise&#39;</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="n">temp_len</span> <span class="o">=</span> <span class="n">temp_len</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">temp_len</span> <span class="o">%</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residual_style</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
-        <span class="n">net</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span><span class="p">):</span>
-            <span class="n">dil</span> <span class="o">=</span> <span class="n">depth</span> <span class="o">-</span> <span class="n">i</span>
-            <span class="n">conv</span> <span class="o">=</span> <span class="n">weight_norm</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_chans</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">filters</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span>
-                                         <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">temp_len</span><span class="p">),</span> <span class="n">dilation</span><span class="o">=</span><span class="n">dil</span><span class="p">,</span>
-                                         <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dil</span> <span class="o">*</span> <span class="p">(</span><span class="n">temp_len</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)))</span>
-            <span class="n">net</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                <span class="n">conv</span><span class="p">,</span>
-                <span class="n">activation</span><span class="p">(),</span>
-                <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">)</span>
-            <span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_style</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;netwise&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">net</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">residual</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_chans</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="k">elif</span> <span class="n">residual</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;dense&#39;</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_style</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;netwise&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_style</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;dense&#39;</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">:</span>
-                <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">layer</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">x</span>
-
-
-<span class="k">class</span> <span class="nc">_TIDNetFeatures</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s_growth</span><span class="p">,</span> <span class="n">t_filters</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">,</span> <span class="n">drop_prob</span><span class="p">,</span> <span class="n">pooling</span><span class="p">,</span>
-                 <span class="n">temp_layers</span><span class="p">,</span> <span class="n">spat_layers</span><span class="p">,</span> <span class="n">temp_span</span><span class="p">,</span> <span class="n">bottleneck</span><span class="p">,</span> <span class="n">summary</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_windows_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">temp_len</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">(</span><span class="n">temp_span</span> <span class="o">*</span> <span class="n">input_window_samples</span><span class="p">)</span>
-
-        <span class="k">def</span> <span class="nf">_permute</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-            <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">            Permutes data:</span>
-
-<span class="sd">            from dim:</span>
-<span class="sd">            batch, chans, time, 1</span>
-
-<span class="sd">            to dim:</span>
-<span class="sd">            batch, 1, chans, time</span>
-
-<span class="sd">            &quot;&quot;&quot;</span>
-            <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">temporal</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">Ensure4d</span><span class="p">(),</span>
-            <span class="n">Expression</span><span class="p">(</span><span class="n">_permute</span><span class="p">),</span>
-            <span class="n">_TemporalFilter</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">t_filters</span><span class="p">,</span> <span class="n">depth</span><span class="o">=</span><span class="n">temp_layers</span><span class="p">,</span> <span class="n">temp_len</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">temp_len</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">pooling</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">drop_prob</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="n">summary</span> <span class="o">=</span> <span class="n">input_window_samples</span> <span class="o">//</span> <span class="n">pooling</span> <span class="k">if</span> <span class="n">summary</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="n">summary</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">spatial</span> <span class="o">=</span> <span class="n">_DenseSpatialFilter</span><span class="p">(</span><span class="n">in_chans</span><span class="p">,</span> <span class="n">s_growth</span><span class="p">,</span> <span class="n">spat_layers</span><span class="p">,</span> <span class="n">in_ch</span><span class="o">=</span><span class="n">t_filters</span><span class="p">,</span>
-                                           <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">,</span> <span class="n">bottleneck</span><span class="o">=</span><span class="n">bottleneck</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">extract_features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool1d</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">summary</span><span class="p">)),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_num_features</span> <span class="o">=</span> <span class="p">(</span><span class="n">t_filters</span> <span class="o">+</span> <span class="n">s_growth</span> <span class="o">*</span> <span class="n">spat_layers</span><span class="p">)</span> <span class="o">*</span> <span class="n">summary</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">num_features</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_features</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">temporal</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">extract_features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="TIDNet"><a class="viewcode-back" href="../../../generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet">[docs]</a><span class="k">class</span> <span class="nc">TIDNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Thinker Invariance DenseNet model from Kostas et al 2020.</span>
-
-<span class="sd">    See [TIDNet]_ for details.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes.</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        Number of EEG channels.</span>
-<span class="sd">    input_window_samples : int</span>
-<span class="sd">        Number of samples.</span>
-<span class="sd">    s_growth : int</span>
-<span class="sd">        DenseNet-style growth factor (added filters per DenseFilter)</span>
-<span class="sd">    t_filters : int</span>
-<span class="sd">        Number of temporal filters.</span>
-<span class="sd">    drop_prob : float</span>
-<span class="sd">        Dropout probability</span>
-<span class="sd">    pooling : int</span>
-<span class="sd">        Max temporal pooling (width and stride)</span>
-<span class="sd">    temp_layers : int</span>
-<span class="sd">        Number of temporal layers</span>
-<span class="sd">    spat_layers : int</span>
-<span class="sd">        Number of DenseFilters</span>
-<span class="sd">    temp_span : float</span>
-<span class="sd">        Percentage of input_window_samples that defines the temporal filter length:</span>
-<span class="sd">        temp_len = ceil(temp_span * input_window_samples)</span>
-<span class="sd">        e.g A value of 0.05 for temp_span with 1500 input_window_samples will yield a temporal</span>
-<span class="sd">        filter of length 75.</span>
-<span class="sd">    bottleneck : int</span>
-<span class="sd">        Bottleneck factor within Densefilter</span>
-<span class="sd">    summary : int</span>
-<span class="sd">        Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated</span>
-<span class="sd">        automatically (input_window_samples // pooling).</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Code adapted from: https://github.com/SPOClab-ca/ThinkerInvariance/</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [TIDNet] Kostas, D. &amp; Rudzicz, F.</span>
-<span class="sd">        Thinker invariance: enabling deep neural networks for BCI across more</span>
-<span class="sd">        people.</span>
-<span class="sd">        J. Neural Eng. 17, 056008 (2020).</span>
-<span class="sd">        doi: 10.1088/1741-2552/abb7a7.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">,</span> <span class="n">s_growth</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">t_filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
-                 <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">pooling</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">temp_layers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">spat_layers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">temp_span</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
-                 <span class="n">bottleneck</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">summary</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">input_window_samples</span> <span class="o">=</span> <span class="n">input_window_samples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">temp_len</span> <span class="o">=</span> <span class="n">ceil</span><span class="p">(</span><span class="n">temp_span</span> <span class="o">*</span> <span class="n">input_window_samples</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">dscnn</span> <span class="o">=</span> <span class="n">_TIDNetFeatures</span><span class="p">(</span><span class="n">s_growth</span><span class="o">=</span><span class="n">s_growth</span><span class="p">,</span> <span class="n">t_filters</span><span class="o">=</span><span class="n">t_filters</span><span class="p">,</span> <span class="n">in_chans</span><span class="o">=</span><span class="n">in_chans</span><span class="p">,</span>
-                                     <span class="n">input_window_samples</span><span class="o">=</span><span class="n">input_window_samples</span><span class="p">,</span>
-                                     <span class="n">drop_prob</span><span class="o">=</span><span class="n">drop_prob</span><span class="p">,</span> <span class="n">pooling</span><span class="o">=</span><span class="n">pooling</span><span class="p">,</span> <span class="n">temp_layers</span><span class="o">=</span><span class="n">temp_layers</span><span class="p">,</span>
-                                     <span class="n">spat_layers</span><span class="o">=</span><span class="n">spat_layers</span><span class="p">,</span> <span class="n">temp_span</span><span class="o">=</span><span class="n">temp_span</span><span class="p">,</span>
-                                     <span class="n">bottleneck</span><span class="o">=</span><span class="n">bottleneck</span><span class="p">,</span> <span class="n">summary</span><span class="o">=</span><span class="n">summary</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_num_features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dscnn</span><span class="o">.</span><span class="n">num_features</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">classify</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_classifier</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_features</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_create_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">incoming</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
-        <span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">incoming</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
-        <span class="n">init</span><span class="o">.</span><span class="n">xavier_normal_</span><span class="p">(</span><span class="n">classifier</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
-        <span class="n">classifier</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">LogSoftmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-
-<div class="viewcode-block" id="TIDNet.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        x: torch.Tensor</span>
-<span class="sd">            Batch of EEG windows of shape (batch_size, n_channels, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dscnn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">num_features</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_features</span></div>
-</pre></div>
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-              
-  <h1>Source code for braindecode.models.usleep</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Theo Gnassounou &lt;theo.gnassounou@inria.fr&gt;</span>
-<span class="c1">#          Omar Chehab &lt;l-emir-omar.chehab@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-
-
-<span class="k">def</span> <span class="nf">_crop_tensors_to_match</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Crops two tensors to their lowest-common-dimension along an axis.&quot;&quot;&quot;</span>
-    <span class="n">dim_cropped</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">x1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">],</span> <span class="n">x2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">axis</span><span class="p">])</span>
-
-    <span class="n">x1_cropped</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span>
-        <span class="n">x1</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
-        <span class="n">index</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">dim_cropped</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">x1</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="n">x2_cropped</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">index_select</span><span class="p">(</span>
-        <span class="n">x2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span>
-        <span class="n">index</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">dim_cropped</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">x1</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">x1_cropped</span><span class="p">,</span> <span class="n">x2_cropped</span>
-
-
-<span class="k">class</span> <span class="nc">_EncoderBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Encoding block for a timeseries x of shape (B, C, T).&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">in_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">out_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">kernel_size</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
-                 <span class="n">downsample</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">in_channels</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span> <span class="o">=</span> <span class="n">out_channels</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">downsample</span> <span class="o">=</span> <span class="n">downsample</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">block_prepool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-                      <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
-                      <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
-                      <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">num_features</span><span class="o">=</span><span class="n">out_channels</span><span class="p">),</span>
-        <span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConstantPad1d</span><span class="p">(</span><span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool1d</span><span class="p">(</span>
-            <span class="n">kernel_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">downsample</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_prepool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">residual</span> <span class="o">=</span> <span class="n">x</span>
-        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maxpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">residual</span>
-
-
-<span class="k">class</span> <span class="nc">_DecoderBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Decoding block for a timeseries x of shape (B, C, T).&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">in_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">out_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">kernel_size</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
-                 <span class="n">upsample</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">with_skip_connection</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">in_channels</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span> <span class="o">=</span> <span class="n">out_channels</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel_size</span> <span class="o">=</span> <span class="n">kernel_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">upsample</span> <span class="o">=</span> <span class="n">upsample</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">with_skip_connection</span> <span class="o">=</span> <span class="n">with_skip_connection</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">block_preskip</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="n">upsample</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
-                      <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
-                      <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                      <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">num_features</span><span class="o">=</span><span class="n">out_channels</span><span class="p">),</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">block_postskip</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="o">=</span><span class="p">(</span>
-                    <span class="mi">2</span> <span class="o">*</span> <span class="n">out_channels</span> <span class="k">if</span> <span class="n">with_skip_connection</span> <span class="k">else</span> <span class="n">out_channels</span><span class="p">),</span>
-                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">num_features</span><span class="o">=</span><span class="n">out_channels</span><span class="p">),</span>
-        <span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_preskip</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_skip_connection</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">residual</span> <span class="o">=</span> <span class="n">_crop_tensors_to_match</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># in case of mismatch</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">residual</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>  <span class="c1"># (B, 2 * C, T)</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_postskip</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">x</span>
-
-
-<div class="viewcode-block" id="USleep"><a class="viewcode-back" href="../../../generated/braindecode.models.USleep.html#braindecode.models.USleep">[docs]</a><span class="k">class</span> <span class="nc">USleep</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sleep staging architecture from Perslev et al 2021.</span>
-
-<span class="sd">    U-Net (autoencoder with skip connections) feature-extractor for sleep</span>
-<span class="sd">    staging described in [1]_.</span>
-
-<span class="sd">    For the encoder (&#39;down&#39;):</span>
-<span class="sd">        -- the temporal dimension shrinks (via maxpooling in the time-domain)</span>
-<span class="sd">        -- the spatial dimension expands (via more conv1d filters in the</span>
-<span class="sd">           time-domain)</span>
-<span class="sd">    For the decoder (&#39;up&#39;):</span>
-<span class="sd">        -- the temporal dimension expands (via upsampling in the time-domain)</span>
-<span class="sd">        -- the spatial dimension shrinks (via fewer conv1d filters in the</span>
-<span class="sd">           time-domain)</span>
-<span class="sd">    Both do so at exponential rates.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    in_chans : int</span>
-<span class="sd">        Number of EEG or EOG channels. Set to 2 in [1]_ (1 EEG, 1 EOG).</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        EEG sampling frequency. Set to 128 in [1]_.</span>
-<span class="sd">    depth : int</span>
-<span class="sd">        Number of conv blocks in encoding layer (number of 2x2 max pools)</span>
-<span class="sd">        Note: each block halve the spatial dimensions of the features.</span>
-<span class="sd">    n_time_filters : int</span>
-<span class="sd">        Initial number of convolutional filters. Set to 5 in [1]_.</span>
-<span class="sd">    complexity_factor : float</span>
-<span class="sd">        Multiplicative factor for number of channels at each layer of the U-Net.</span>
-<span class="sd">        Set to 2 in [1]_.</span>
-<span class="sd">    with_skip_connection : bool</span>
-<span class="sd">        If True, use skip connections in decoder blocks.</span>
-<span class="sd">    n_classes : int</span>
-<span class="sd">        Number of classes. Set to 5.</span>
-<span class="sd">    input_size_s : float</span>
-<span class="sd">        Size of the input, in seconds. Set to 30 in [1]_.</span>
-<span class="sd">    time_conv_size_s : float</span>
-<span class="sd">        Size of the temporal convolution kernel, in seconds. Set to 9 / 128 in</span>
-<span class="sd">        [1]_.</span>
-<span class="sd">    ensure_odd_conv_size : bool</span>
-<span class="sd">        If True and the size of the convolutional kernel is an even number, one</span>
-<span class="sd">        will be added to it to ensure it is odd, so that the decoder blocks can</span>
-<span class="sd">        work. This can ne useful when using different sampling rates from 128</span>
-<span class="sd">        or 100 Hz.</span>
-<span class="sd">    apply_softmax : bool</span>
-<span class="sd">        If True, apply softmax on output (e.g. when using nn.NLLLoss). Use</span>
-<span class="sd">        False if using nn.CrossEntropyLoss.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.</span>
-<span class="sd">           U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).</span>
-<span class="sd">           https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">in_chans</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
-                 <span class="n">sfreq</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
-                 <span class="n">depth</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-                 <span class="n">n_time_filters</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                 <span class="n">complexity_factor</span><span class="o">=</span><span class="mf">1.67</span><span class="p">,</span>
-                 <span class="n">with_skip_connection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">n_classes</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                 <span class="n">input_size_s</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-                 <span class="n">time_conv_size_s</span><span class="o">=</span><span class="mi">9</span> <span class="o">/</span> <span class="mi">128</span><span class="p">,</span>
-                 <span class="n">ensure_odd_conv_size</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">apply_softmax</span><span class="o">=</span><span class="kc">False</span>
-                 <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">in_chans</span> <span class="o">=</span> <span class="n">in_chans</span>
-        <span class="n">max_pool_size</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Hardcoded to avoid dimensional errors</span>
-        <span class="n">time_conv_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">time_conv_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">time_conv_size</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">ensure_odd_conv_size</span><span class="p">:</span>
-                <span class="n">time_conv_size</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="s1">&#39;time_conv_size must be an odd number to accomodate the &#39;</span>
-                    <span class="s1">&#39;upsampling step in the decoder blocks.&#39;</span><span class="p">)</span>
-
-        <span class="c1"># Convert between units: seconds to time-points (at sfreq)</span>
-        <span class="n">input_size</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">input_size_s</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-
-        <span class="n">channels</span> <span class="o">=</span> <span class="p">[</span><span class="n">in_chans</span><span class="p">]</span>
-        <span class="n">n_filters</span> <span class="o">=</span> <span class="n">n_time_filters</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="n">channels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">n_filters</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">complexity_factor</span><span class="p">)))</span>
-            <span class="n">n_filters</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_filters</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">channels</span> <span class="o">=</span> <span class="n">channels</span>
-
-        <span class="c1"># Instantiate encoder</span>
-        <span class="n">encoder</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span><span class="p">):</span>
-            <span class="n">encoder</span> <span class="o">+=</span> <span class="p">[</span>
-                <span class="n">_EncoderBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
-                              <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-                              <span class="n">kernel_size</span><span class="o">=</span><span class="n">time_conv_size</span><span class="p">,</span>
-                              <span class="n">downsample</span><span class="o">=</span><span class="n">max_pool_size</span><span class="p">)</span>
-            <span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">encoder</span><span class="p">)</span>
-
-        <span class="c1"># Instantiate bottom (channels increase, temporal dim stays the same)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bottom</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-                    <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span>
-                              <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
-                              <span class="n">kernel_size</span><span class="o">=</span><span class="n">time_conv_size</span><span class="p">,</span>
-                              <span class="n">padding</span><span class="o">=</span><span class="p">(</span><span class="n">time_conv_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span>  <span class="c1"># preserves dimension</span>
-                    <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-                    <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm1d</span><span class="p">(</span><span class="n">num_features</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                <span class="p">)</span>
-
-        <span class="c1"># Instantiate decoder</span>
-        <span class="n">decoder</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-        <span class="n">channels_reverse</span> <span class="o">=</span> <span class="n">channels</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">depth</span><span class="p">):</span>
-            <span class="n">decoder</span> <span class="o">+=</span> <span class="p">[</span>
-                <span class="n">_DecoderBlock</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="n">channels_reverse</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
-                              <span class="n">out_channels</span><span class="o">=</span><span class="n">channels_reverse</span><span class="p">[</span><span class="n">idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-                              <span class="n">kernel_size</span><span class="o">=</span><span class="n">time_conv_size</span><span class="p">,</span>
-                              <span class="n">upsample</span><span class="o">=</span><span class="n">max_pool_size</span><span class="p">,</span>
-                              <span class="n">with_skip_connection</span><span class="o">=</span><span class="n">with_skip_connection</span><span class="p">)</span>
-            <span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="n">decoder</span><span class="p">)</span>
-
-        <span class="c1"># The temporal dimension remains unchanged</span>
-        <span class="c1"># (except through the AvgPooling which collapses it to 1)</span>
-        <span class="c1"># The spatial dimension is preserved from the end of the UNet, and is mapped to n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="p">),</span>                         <span class="c1"># output is (B, C, 1, S * T)</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool1d</span><span class="p">(</span><span class="n">input_size</span><span class="p">),</span>  <span class="c1"># output is (B, C, S)</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                <span class="n">out_channels</span><span class="o">=</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="p">),</span>                         <span class="c1"># output is (B, n_classes, S)</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv1d</span><span class="p">(</span>
-                <span class="n">in_channels</span><span class="o">=</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="n">out_channels</span><span class="o">=</span><span class="n">n_classes</span><span class="p">,</span>
-                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Softmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">apply_softmax</span> <span class="k">else</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span>
-            <span class="c1"># output is (B, n_classes, S)</span>
-        <span class="p">)</span>
-
-<div class="viewcode-block" id="USleep.forward"><a class="viewcode-back" href="../../../generated/braindecode.models.USleep.html#braindecode.models.USleep.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;If input x has shape (B, S, C, T), return y_pred of shape (B, n_classes, S).</span>
-<span class="sd">        If input x has shape (B, C, T), return y_pred of shape (B, n_classes).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># reshape input</span>
-        <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>  <span class="c1"># input x has shape (B, S, C, T)</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># (B, C, S, T)</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># (B, C, S * T)</span>
-
-        <span class="c1"># encoder</span>
-        <span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">down</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span><span class="p">:</span>
-            <span class="n">x</span><span class="p">,</span> <span class="n">res</span> <span class="o">=</span> <span class="n">down</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-            <span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-
-        <span class="c1"># bottom</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bottom</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-
-        <span class="c1"># decoder</span>
-        <span class="n">residuals</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># flip order</span>
-        <span class="k">for</span> <span class="n">up</span><span class="p">,</span> <span class="n">res</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">decoder</span><span class="p">,</span> <span class="n">residuals</span><span class="p">):</span>
-            <span class="n">x</span> <span class="o">=</span> <span class="n">up</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">res</span><span class="p">)</span>
-
-        <span class="c1"># classifier</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clf</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>        <span class="c1"># (B, n_classes, seq_length)</span>
-
-        <span class="k">if</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># seq_length of 1</span>
-            <span class="n">y_pred</span> <span class="o">=</span> <span class="n">y_pred</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
-
-        <span class="k">return</span> <span class="n">y_pred</span></div></div>
-</pre></div>
-
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-  <h1>Source code for braindecode.models.util</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">log_softmax</span>
-
-
-<span class="k">def</span> <span class="nf">to_dense_prediction_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transform a sequential model with strides to a model that outputs</span>
-<span class="sd">    dense predictions by removing the strides and instead inserting dilations.</span>
-<span class="sd">    Modifies model in-place.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    model: torch.nn.Module</span>
-<span class="sd">        Model which modules will be modified</span>
-<span class="sd">    axis: int or (int,int)</span>
-<span class="sd">        Axis to transform (in terms of intermediate output axes)</span>
-<span class="sd">        can either be 2, 3, or (2,3).</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    Does not yet work correctly for average pooling.</span>
-<span class="sd">    Prior to version 0.1.7, there had been a bug that could move strides</span>
-<span class="sd">    backwards one layer.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="s2">&quot;__len__&quot;</span><span class="p">):</span>
-        <span class="n">axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">axis</span><span class="p">]</span>
-    <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ax</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="p">]),</span> <span class="s2">&quot;Only 2 and 3 allowed for axis&quot;</span>
-    <span class="n">axis</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">axis</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span>
-    <span class="n">stride_so_far</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s2">&quot;dilation&quot;</span><span class="p">):</span>
-            <span class="k">assert</span> <span class="n">module</span><span class="o">.</span><span class="n">dilation</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">dilation</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="p">(</span>
-                <span class="s2">&quot;Dilation should equal 1 before conversion, maybe the model is &quot;</span>
-                <span class="s2">&quot;already converted?&quot;</span>
-            <span class="p">)</span>
-            <span class="n">new_dilation</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="p">:</span>
-                <span class="n">new_dilation</span><span class="p">[</span><span class="n">ax</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">stride_so_far</span><span class="p">[</span><span class="n">ax</span><span class="p">])</span>
-            <span class="n">module</span><span class="o">.</span><span class="n">dilation</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">new_dilation</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s2">&quot;stride&quot;</span><span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span> <span class="s2">&quot;__len__&quot;</span><span class="p">):</span>
-                <span class="n">module</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span> <span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">)</span>
-            <span class="n">stride_so_far</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">)</span>
-            <span class="n">new_stride</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">stride</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="p">:</span>
-                <span class="n">new_stride</span><span class="p">[</span><span class="n">ax</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-            <span class="n">module</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">new_stride</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="get_output_shape"><a class="viewcode-back" href="../../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape">[docs]</a><span class="k">def</span> <span class="nf">get_output_shape</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Returns shape of neural network output for batch size equal 1.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    output_shape: tuple</span>
-<span class="sd">        shape of the network output for `batch_size==1` (1, ...)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-        <span class="n">dummy_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
-            <span class="mi">1</span><span class="p">,</span> <span class="n">in_chans</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="p">,</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
-            <span class="n">device</span><span class="o">=</span><span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">output_shape</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">dummy_input</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span>
-    <span class="k">return</span> <span class="n">output_shape</span></div>
-
-
-<span class="k">def</span> <span class="nf">_pad_shift_array</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Zero-pad and shift rows of a 3D array.</span>
-
-<span class="sd">    E.g., used to align predictions of corresponding windows in</span>
-<span class="sd">    sequence-to-sequence models.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    x : np.ndarray</span>
-<span class="sd">        Array of shape (n_rows, n_classes, n_windows).</span>
-<span class="sd">    stride : int</span>
-<span class="sd">        Number of non-overlapping elements between two consecutive sequences.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    np.ndarray :</span>
-<span class="sd">        Array of shape (n_rows, n_classes, (n_rows - 1) * stride + n_windows)</span>
-<span class="sd">        where each row is obtained by zero-padding the corresponding row in</span>
-<span class="sd">        ``x`` before and after in the last dimension.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span>
-            <span class="s1">&#39;x must be of shape (n_rows, n_classes, n_windows), got &#39;</span>
-            <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="n">x_padded</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">stride</span><span class="p">)))</span>
-    <span class="n">orig_strides</span> <span class="o">=</span> <span class="n">x_padded</span><span class="o">.</span><span class="n">strides</span>
-    <span class="n">new_strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">orig_strides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">stride</span> <span class="o">*</span> <span class="n">orig_strides</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-                   <span class="n">orig_strides</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                   <span class="n">orig_strides</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">lib</span><span class="o">.</span><span class="n">stride_tricks</span><span class="o">.</span><span class="n">as_strided</span><span class="p">(</span><span class="n">x_padded</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">new_strides</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">aggregate_probas</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">n_windows_stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Aggregate predicted probabilities with self-ensembling.</span>
-
-<span class="sd">    Aggregate window-wise predicted probabilities obtained on overlapping</span>
-<span class="sd">    sequences of windows using multiplicative voting as described in</span>
-<span class="sd">    [Phan2018]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    logits : np.ndarray</span>
-<span class="sd">        Array of shape (n_sequences, n_classes, n_windows) containing the</span>
-<span class="sd">        logits (i.e. the raw unnormalized scores for each class) for each</span>
-<span class="sd">        window of each sequence.</span>
-<span class="sd">    n_windows_stride : int</span>
-<span class="sd">        Number of windows between two consecutive sequences. Default is 1</span>
-<span class="sd">        (maximally overlapping sequences).</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    np.ndarray :</span>
-<span class="sd">        Array of shape ((n_rows - 1) * stride + n_windows, n_classes)</span>
-<span class="sd">        containing the aggregated predicted probabilities for each window</span>
-<span class="sd">        contained in the input sequences.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Phan2018] Phan, H., Andreotti, F., Cooray, N., Chén, O. Y., &amp;</span>
-<span class="sd">        De Vos, M. (2018). Joint classification and prediction CNN framework</span>
-<span class="sd">        for automatic sleep stage classification. IEEE Transactions on</span>
-<span class="sd">        Biomedical Engineering, 66(5), 1285-1296.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">log_probas</span> <span class="o">=</span> <span class="n">log_softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_pad_shift_array</span><span class="p">(</span><span class="n">log_probas</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">n_windows_stride</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-</pre></div>
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-              
-  <h1>Source code for braindecode.preprocessing.preprocess</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Preprocessors that work on Raw or Epochs objects.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#          David Sabbagh &lt;dav.sabbagh@gmail.com&gt;</span>
-<span class="c1">#          Bruno Aristimunha &lt;b.aristimunha@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
-<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Iterable</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <span class="n">create_info</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">deprecated</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets.base</span> <span class="kn">import</span> <span class="n">BaseConcatDataset</span><span class="p">,</span> <span class="n">BaseDataset</span><span class="p">,</span> <span class="n">WindowsDataset</span>
-<span class="kn">from</span> <span class="nn">braindecode.datautil.serialization</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">load_concat_dataset</span><span class="p">,</span> <span class="n">_check_save_dir_empty</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="Preprocessor"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor">[docs]</a><span class="k">class</span> <span class="nc">Preprocessor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Preprocessor for an MNE Raw or Epochs object.</span>
-
-<span class="sd">    Applies the provided preprocessing function to the data of a Raw or Epochs</span>
-<span class="sd">    object.</span>
-<span class="sd">    If the function is provided as a string, the method with that name will be</span>
-<span class="sd">    used (e.g., &#39;pick_channels&#39;, &#39;filter&#39;, etc.).</span>
-<span class="sd">    If it is provided as a callable and `apply_on_array` is True, the</span>
-<span class="sd">    `apply_function` method of Raw and Epochs object will be used to apply the</span>
-<span class="sd">    function on the internal arrays of Raw and Epochs.</span>
-<span class="sd">    If `apply_on_array` is False, the callable must directly modify the Raw or</span>
-<span class="sd">    Epochs object (e.g., by calling its method(s) or modifying its attributes).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    fn: str or callable</span>
-<span class="sd">        If str, the Raw/Epochs object must have a method with that name.</span>
-<span class="sd">        If callable, directly apply the callable to the object.</span>
-<span class="sd">    apply_on_array : bool</span>
-<span class="sd">        Ignored if `fn` is not a callable. If True, the `apply_function` of Raw</span>
-<span class="sd">        and Epochs object will be used to run `fn` on the underlying arrays</span>
-<span class="sd">        directly. If False, `fn` must directly modify the Raw or Epochs object.</span>
-<span class="sd">    kwargs:</span>
-<span class="sd">        Keyword arguments to be forwarded to the MNE function.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="s1">&#39;__name__&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">fn</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;&lt;lambda&gt;&#39;</span><span class="p">:</span>
-            <span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Preprocessing choices with lambda functions cannot be saved.&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span> <span class="ow">and</span> <span class="n">apply_on_array</span><span class="p">:</span>
-            <span class="n">channel_wise</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;channel_wise&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-            <span class="n">picks</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;picks&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-            <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;n_jobs&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-            <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">fun</span><span class="o">=</span><span class="n">partial</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">channel_wise</span><span class="o">=</span><span class="n">channel_wise</span><span class="p">,</span>
-                          <span class="n">picks</span><span class="o">=</span><span class="n">picks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
-            <span class="n">fn</span> <span class="o">=</span> <span class="s1">&#39;apply_function&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fn</span> <span class="o">=</span> <span class="n">fn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
-
-<div class="viewcode-block" id="Preprocessor.apply"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">raw_or_epochs</span><span class="p">):</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_try_apply</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>
-            <span class="c1"># Maybe the function needs the data to be loaded and the data was</span>
-            <span class="c1"># not loaded yet. Not all MNE functions need data to be loaded,</span>
-            <span class="c1"># most importantly the &#39;crop&#39; function can be lazily applied</span>
-            <span class="c1"># without preloading data which can make the overall preprocessing</span>
-            <span class="c1"># pipeline substantially faster.</span>
-            <span class="n">raw_or_epochs</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_try_apply</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">)</span></div>
-
-    <span class="k">def</span> <span class="nf">_try_apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">raw_or_epochs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s1">&#39;MNE object does not have a </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="si">}</span><span class="s1"> method.&#39;</span><span class="p">)</span>
-            <span class="nb">getattr</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fn</span><span class="p">)(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="preprocess"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess">[docs]</a><span class="k">def</span> <span class="nf">preprocess</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-               <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Apply preprocessors to a concat dataset.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    concat_ds: BaseConcatDataset</span>
-<span class="sd">        A concat of BaseDataset or WindowsDataset datasets to be preprocessed.</span>
-<span class="sd">    preprocessors: list(Preprocessor)</span>
-<span class="sd">        List of Preprocessor objects to apply to the dataset.</span>
-<span class="sd">    save_dir : str | None</span>
-<span class="sd">        If a string, the preprocessed data will be saved under the specified</span>
-<span class="sd">        directory and the datasets in ``concat_ds`` will be reloaded with</span>
-<span class="sd">        `preload=False`.</span>
-<span class="sd">    overwrite : bool</span>
-<span class="sd">        When `save_dir` is provided, controls whether to delete the old</span>
-<span class="sd">        subdirectories that will be written to under `save_dir`. If False and</span>
-<span class="sd">        the corresponding subdirectories already exist, a ``FileExistsError``</span>
-<span class="sd">        will be raised.</span>
-<span class="sd">    n_jobs : int | None</span>
-<span class="sd">        Number of jobs for parallel execution.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    BaseConcatDataset:</span>
-<span class="sd">        Preprocessed dataset.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># In case of serialization, make sure directory is available before</span>
-    <span class="c1"># preprocessing</span>
-    <span class="k">if</span> <span class="n">save_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">overwrite</span><span class="p">:</span>
-        <span class="n">_check_save_dir_empty</span><span class="p">(</span><span class="n">save_dir</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">preprocessors</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s1">&#39;preprocessors must be a list of Preprocessor objects.&#39;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">elem</span> <span class="ow">in</span> <span class="n">preprocessors</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">elem</span><span class="p">,</span> <span class="s1">&#39;apply&#39;</span><span class="p">),</span> <span class="p">(</span>
-            <span class="s1">&#39;Preprocessor object needs an `apply` method.&#39;</span><span class="p">)</span>
-
-    <span class="n">list_of_ds</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">_preprocess</span><span class="p">)(</span><span class="n">ds</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">))</span>
-
-    <span class="k">if</span> <span class="n">save_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># Reload datasets and replace in concat_ds</span>
-        <span class="n">concat_ds_reloaded</span> <span class="o">=</span> <span class="n">load_concat_dataset</span><span class="p">(</span>
-            <span class="n">save_dir</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="n">_replace_inplace</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">concat_ds_reloaded</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">n_jobs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># joblib did not make copies, the</span>
-            <span class="c1"># preprocessing happened in-place</span>
-            <span class="c1"># Recompute cumulative sizes as transforms might have changed them</span>
-            <span class="n">concat_ds</span><span class="o">.</span><span class="n">cumulative_sizes</span> <span class="o">=</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>  <span class="c1"># joblib made copies</span>
-            <span class="n">_replace_inplace</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">list_of_ds</span><span class="p">))</span>
-
-    <span class="k">return</span> <span class="n">concat_ds</span></div>
-
-
-<span class="k">def</span> <span class="nf">_replace_inplace</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">new_concat_ds</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Replace subdatasets and preproc_kwargs of a BaseConcatDataset inplace.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    concat_ds : BaseConcatDataset</span>
-<span class="sd">        Dataset to modify inplace.</span>
-<span class="sd">    new_concat_ds : BaseConcatDataset</span>
-<span class="sd">        Dataset to use to modify ``concat_ds``.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Both inputs must have the same length.&#39;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">new_concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)):</span>
-        <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-
-    <span class="n">concat_kind</span> <span class="o">=</span> <span class="s1">&#39;raw&#39;</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">&#39;raw&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;window&#39;</span>
-    <span class="n">preproc_kwargs_attr</span> <span class="o">=</span> <span class="n">concat_kind</span> <span class="o">+</span> <span class="s1">&#39;_preproc_kwargs&#39;</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">new_concat_ds</span><span class="p">,</span> <span class="n">preproc_kwargs_attr</span><span class="p">):</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">preproc_kwargs_attr</span><span class="p">,</span>
-                <span class="nb">getattr</span><span class="p">(</span><span class="n">new_concat_ds</span><span class="p">,</span> <span class="n">preproc_kwargs_attr</span><span class="p">))</span>
-
-
-<span class="k">def</span> <span class="nf">_preprocess</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">ds_index</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Apply preprocessor(s) to Raw or Epochs object.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ds: BaseDataset | WindowsDataset</span>
-<span class="sd">        Dataset object to preprocess.</span>
-<span class="sd">    ds_index : int</span>
-<span class="sd">        Index of the BaseDataset in its BaseConcatDataset. Ignored if save_dir</span>
-<span class="sd">        is None.</span>
-<span class="sd">    preprocessors: list(Preprocessor)</span>
-<span class="sd">        List of preprocessors to apply to the dataset.</span>
-<span class="sd">    save_dir : str | None</span>
-<span class="sd">        If provided, save the preprocessed BaseDataset in the</span>
-<span class="sd">        specified directory.</span>
-<span class="sd">    overwrite : bool</span>
-<span class="sd">        If True, overwrite existing file with the same name.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="nf">_preprocess_raw_or_epochs</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">preproc</span> <span class="ow">in</span> <span class="n">preprocessors</span><span class="p">:</span>
-            <span class="n">preproc</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">raw_or_epochs</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="s1">&#39;raw&#39;</span><span class="p">):</span>
-        <span class="n">_preprocess_raw_or_epochs</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="s1">&#39;windows&#39;</span><span class="p">):</span>
-        <span class="n">_preprocess_raw_or_epochs</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">windows</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s1">&#39;Can only preprocess concatenation of BaseDataset or &#39;</span>
-            <span class="s1">&#39;WindowsDataset, with either a `raw` or `windows` attribute.&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Store preprocessing keyword arguments in the dataset</span>
-    <span class="n">_set_preproc_kwargs</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">save_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">concat_ds</span> <span class="o">=</span> <span class="n">BaseConcatDataset</span><span class="p">([</span><span class="n">ds</span><span class="p">])</span>
-        <span class="n">concat_ds</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">ds_index</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">ds</span>
-
-
-<span class="k">def</span> <span class="nf">_get_preproc_kwargs</span><span class="p">(</span><span class="n">preprocessors</span><span class="p">):</span>
-    <span class="n">preproc_kwargs</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">preprocessors</span><span class="p">:</span>
-        <span class="c1"># in case of a mne function, fn is a str, kwargs is a dict</span>
-        <span class="n">func_name</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">fn</span>
-        <span class="n">func_kwargs</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">kwargs</span>
-        <span class="c1"># in case of another function</span>
-        <span class="c1"># if apply_on_array=False</span>
-        <span class="k">if</span> <span class="n">callable</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">fn</span><span class="p">):</span>
-            <span class="n">func_name</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">fn</span><span class="o">.</span><span class="vm">__name__</span>
-        <span class="c1"># if apply_on_array=True</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="s1">&#39;fun&#39;</span> <span class="ow">in</span> <span class="n">p</span><span class="o">.</span><span class="n">fn</span><span class="p">:</span>
-                <span class="n">func_name</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;fun&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span>
-                <span class="n">func_kwargs</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;fun&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">keywords</span>
-        <span class="n">preproc_kwargs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">func_name</span><span class="p">,</span> <span class="n">func_kwargs</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">preproc_kwargs</span>
-
-
-<span class="k">def</span> <span class="nf">_set_preproc_kwargs</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Record preprocessing keyword arguments in BaseDataset or WindowsDataset.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ds : BaseDataset | WindowsDataset</span>
-<span class="sd">        Dataset in which to record preprocessing keyword arguments.</span>
-<span class="sd">    preprocessors : list</span>
-<span class="sd">        List of preprocessors.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">preproc_kwargs</span> <span class="o">=</span> <span class="n">_get_preproc_kwargs</span><span class="p">(</span><span class="n">preprocessors</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">WindowsDataset</span><span class="p">):</span>
-        <span class="n">kind</span> <span class="o">=</span> <span class="s1">&#39;window&#39;</span>
-    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">BaseDataset</span><span class="p">):</span>
-        <span class="n">kind</span> <span class="o">=</span> <span class="s1">&#39;raw&#39;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s1">&#39;ds must be a BaseDataset or a WindowsDataset, got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="nb">setattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kind</span> <span class="o">+</span> <span class="s1">&#39;_preproc_kwargs&#39;</span><span class="p">,</span> <span class="n">preproc_kwargs</span><span class="p">)</span>
-
-
-<div class="viewcode-block" id="exponential_moving_standardize"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize">[docs]</a><span class="k">def</span> <span class="nf">exponential_moving_standardize</span><span class="p">(</span>
-        <span class="n">data</span><span class="p">,</span> <span class="n">factor_new</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">init_block_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">1e-4</span>
-<span class="p">):</span>
-    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Perform exponential moving standardization.</span>
-
-<span class="sd">    Compute the exponental moving mean :math:`m_t` at time `t` as</span>
-<span class="sd">    :math:`m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}`.</span>
-
-<span class="sd">    Then, compute exponential moving variance :math:`v_t` at time `t` as</span>
-<span class="sd">    :math:`v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}`.</span>
-
-<span class="sd">    Finally, standardize the data point :math:`x_t` at time `t` as:</span>
-<span class="sd">    :math:`x&#39;_t=(x_t - m_t) / max(\sqrt{-&gt;v_t}, eps)`.</span>
-
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data: np.ndarray (n_channels, n_times)</span>
-<span class="sd">    factor_new: float</span>
-<span class="sd">    init_block_size: int</span>
-<span class="sd">        Standardize data before to this index with regular standardization.</span>
-<span class="sd">    eps: float</span>
-<span class="sd">        Stabilizer for division by zero variance.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    standardized: np.ndarray (n_channels, n_times)</span>
-<span class="sd">        Standardized data.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">meaned</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">factor_new</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-    <span class="n">demeaned</span> <span class="o">=</span> <span class="n">df</span> <span class="o">-</span> <span class="n">meaned</span>
-    <span class="n">squared</span> <span class="o">=</span> <span class="n">demeaned</span> <span class="o">*</span> <span class="n">demeaned</span>
-    <span class="n">square_ewmed</span> <span class="o">=</span> <span class="n">squared</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">factor_new</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-    <span class="n">standardized</span> <span class="o">=</span> <span class="n">demeaned</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">square_ewmed</span><span class="p">)))</span>
-    <span class="n">standardized</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">standardized</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">init_block_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">i_time_axis</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">init_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span>
-            <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">i_time_axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="n">init_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span>
-            <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">i_time_axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="n">init_block_standardized</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">]</span> <span class="o">-</span> <span class="n">init_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">eps</span><span class="p">,</span> <span class="n">init_std</span><span class="p">)</span>
-        <span class="n">standardized</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">]</span> <span class="o">=</span> <span class="n">init_block_standardized</span>
-    <span class="k">return</span> <span class="n">standardized</span><span class="o">.</span><span class="n">T</span></div>
-
-
-<div class="viewcode-block" id="exponential_moving_demean"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.exponential_moving_demean.html#braindecode.preprocessing.exponential_moving_demean">[docs]</a><span class="k">def</span> <span class="nf">exponential_moving_demean</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor_new</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">init_block_size</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Perform exponential moving demeanining.</span>
-
-<span class="sd">    Compute the exponental moving mean :math:`m_t` at time `t` as</span>
-<span class="sd">    :math:`m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}`.</span>
-
-<span class="sd">    Deman the data point :math:`x_t` at time `t` as:</span>
-<span class="sd">    :math:`x&#39;_t=(x_t - m_t)`.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data: np.ndarray (n_channels, n_times)</span>
-<span class="sd">    factor_new: float</span>
-<span class="sd">    init_block_size: int</span>
-<span class="sd">        Demean data before to this index with regular demeaning.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    demeaned: np.ndarray (n_channels, n_times)</span>
-<span class="sd">        Demeaned data.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">meaned</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">ewm</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">factor_new</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-    <span class="n">demeaned</span> <span class="o">=</span> <span class="n">df</span> <span class="o">-</span> <span class="n">meaned</span>
-    <span class="n">demeaned</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">demeaned</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">init_block_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">i_time_axis</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">init_mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span>
-            <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="n">i_time_axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="n">demeaned</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">init_block_size</span><span class="p">]</span> <span class="o">-</span> <span class="n">init_mean</span>
-    <span class="k">return</span> <span class="n">demeaned</span><span class="o">.</span><span class="n">T</span></div>
-
-
-<div class="viewcode-block" id="scale"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale">[docs]</a><span class="nd">@deprecated</span><span class="p">(</span><span class="n">extra</span><span class="o">=</span><span class="s1">&#39;will be removed in 0.8.0. Use numpy.multiply inside a lambda function instead.&#39;</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">scale</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Scale continuous or windowed data in-place</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    data: np.ndarray (n_channels x n_times) or (n_windows x n_channels x</span>
-<span class="sd">    n_times)</span>
-<span class="sd">        continuous or windowed signal</span>
-<span class="sd">    factor: float</span>
-<span class="sd">        multiplication factor</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    scaled: np.ndarray (n_channels x n_times) or (n_windows x n_channels x</span>
-<span class="sd">    n_times)</span>
-<span class="sd">        normalized continuous or windowed data</span>
-
-<span class="sd">    ..note:</span>
-<span class="sd">        If this function is supposed to preprocess continuous data, it should be</span>
-<span class="sd">        given to raw.apply_function().</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">scaled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">)</span>
-    <span class="c1"># TODO: the overriding of protected &#39;_data&#39; should be implemented in the</span>
-    <span class="c1"># TODO: dataset when transforms are applied to windows</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="s1">&#39;_data&#39;</span><span class="p">):</span>
-        <span class="n">data</span><span class="o">.</span><span class="n">_data</span> <span class="o">=</span> <span class="n">scaled</span>
-    <span class="k">return</span> <span class="n">scaled</span></div>
-
-
-<div class="viewcode-block" id="filterbank"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank">[docs]</a><span class="k">def</span> <span class="nf">filterbank</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">frequency_bands</span><span class="p">,</span> <span class="n">drop_original_signals</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-               <span class="n">order_by_frequency_band</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">mne_filter_kwargs</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Applies multiple bandpass filters to the signals in raw. The raw will be</span>
-<span class="sd">    modified in-place and number of channels in raw will be updated to</span>
-<span class="sd">    len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if</span>
-<span class="sd">    drop_original_signals).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    raw: mne.io.Raw</span>
-<span class="sd">        The raw signals to be filtered.</span>
-<span class="sd">    frequency_bands: list(tuple)</span>
-<span class="sd">        The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</span>
-<span class="sd">    drop_original_signals: bool</span>
-<span class="sd">        Whether to drop the original unfiltered signals</span>
-<span class="sd">    order_by_frequency_band: bool</span>
-<span class="sd">        If True will return channels odered by frequency bands, so if there</span>
-<span class="sd">        are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned</span>
-<span class="sd">        channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order</span>
-<span class="sd">        will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].</span>
-<span class="sd">    mne_filter_kwargs: dict</span>
-<span class="sd">        Keyword arguments for filtering supported by mne.io.Raw.filter().</span>
-<span class="sd">        Please refer to mne for a detailed explanation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">frequency_bands</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Expected at least one frequency band, got&quot;</span>
-                         <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">frequency_bands</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">ch_name</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">8</span> <span class="k">for</span> <span class="n">ch_name</span> <span class="ow">in</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">]):</span>
-        <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Try to use shorter channel names, since frequency band &quot;</span>
-             <span class="s2">&quot;annotation requires an estimated 4-8 chars depending on the &quot;</span>
-             <span class="s2">&quot;frequency ranges. Will truncate to 15 chars (mne max).&quot;</span><span class="p">)</span>
-    <span class="n">original_ch_names</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span>
-    <span class="n">all_filtered</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="p">(</span><span class="n">l_freq</span><span class="p">,</span> <span class="n">h_freq</span><span class="p">)</span> <span class="ow">in</span> <span class="n">frequency_bands</span><span class="p">:</span>
-        <span class="n">filtered</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">filtered</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">l_freq</span><span class="o">=</span><span class="n">l_freq</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><span class="n">h_freq</span><span class="p">,</span> <span class="o">**</span><span class="n">mne_filter_kwargs</span><span class="p">)</span>
-        <span class="c1"># mne automatically changes the highpass/lowpass info values</span>
-        <span class="c1"># when applying filters and channels cant be added if they have</span>
-        <span class="c1"># different such parameters. Not needed when making picks as</span>
-        <span class="c1"># high pass is not modified by filter if pick is specified</span>
-
-        <span class="n">ch_names</span> <span class="o">=</span> <span class="n">filtered</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">ch_names</span>
-        <span class="n">ch_types</span> <span class="o">=</span> <span class="n">filtered</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">get_channel_types</span><span class="p">()</span>
-        <span class="n">sampling_freq</span> <span class="o">=</span> <span class="n">filtered</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-
-        <span class="n">info</span> <span class="o">=</span> <span class="n">create_info</span><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="n">ch_types</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="n">sampling_freq</span><span class="p">)</span>
-
-        <span class="n">filtered</span><span class="o">.</span><span class="n">info</span> <span class="o">=</span> <span class="n">info</span>
-
-        <span class="c1"># add frequency band annotation to channel names</span>
-        <span class="c1"># truncate to a max of 15 characters, since mne does not allow for more</span>
-        <span class="n">filtered</span><span class="o">.</span><span class="n">rename_channels</span><span class="p">({</span>
-            <span class="n">old_name</span><span class="p">:</span> <span class="p">(</span><span class="n">old_name</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;_</span><span class="si">{</span><span class="n">l_freq</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">h_freq</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">15</span><span class="p">:]</span>
-            <span class="k">for</span> <span class="n">old_name</span> <span class="ow">in</span> <span class="n">filtered</span><span class="o">.</span><span class="n">ch_names</span><span class="p">})</span>
-        <span class="n">all_filtered</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">filtered</span><span class="p">)</span>
-    <span class="n">raw</span><span class="o">.</span><span class="n">add_channels</span><span class="p">(</span><span class="n">all_filtered</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">order_by_frequency_band</span><span class="p">:</span>
-        <span class="c1"># order channels by name and not by frequency band:</span>
-        <span class="c1"># index the list with a stepsize of the number of channels for each of</span>
-        <span class="c1"># the original channels</span>
-        <span class="n">chs_by_freq_band</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">original_ch_names</span><span class="p">)):</span>
-            <span class="n">chs_by_freq_band</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="n">i</span><span class="p">::</span><span class="nb">len</span><span class="p">(</span><span class="n">original_ch_names</span><span class="p">)])</span>
-        <span class="n">raw</span><span class="o">.</span><span class="n">reorder_channels</span><span class="p">(</span><span class="n">chs_by_freq_band</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">drop_original_signals</span><span class="p">:</span>
-        <span class="n">raw</span><span class="o">.</span><span class="n">drop_channels</span><span class="p">(</span><span class="n">original_ch_names</span><span class="p">)</span></div>
-</pre></div>
-
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
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-              
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-  <div class="footer-item">
-    
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diff --git a/stable/_modules/braindecode/preprocessing/windowers.html b/stable/_modules/braindecode/preprocessing/windowers.html
deleted file mode 100644
index ccd8b699..00000000
--- a/stable/_modules/braindecode/preprocessing/windowers.html
+++ /dev/null
@@ -1,1062 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>braindecode.preprocessing.windowers &#8212; Braindecode 0.7 documentation</title>
-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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-  <h1>Source code for braindecode.preprocessing.windowers</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;Get epochs from mne.Raw</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#          David Sabbagh &lt;dav.sabbagh@gmail.com&gt;</span>
-<span class="c1">#          Henrik Bonsmann &lt;henrikbons@gmail.com&gt;</span>
-<span class="c1">#          Ann-Kathrin Kiessner &lt;ann-kathrin.kiessner@gmx.de&gt;</span>
-<span class="c1">#          Vytautas Jankauskas &lt;vytauto.jankausko@gmail.com&gt;</span>
-<span class="c1">#          Dan Wilson &lt;dan.c.wil@gmail.com&gt;</span>
-<span class="c1">#          Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-
-<span class="kn">from</span> <span class="nn">..datasets.base</span> <span class="kn">import</span> <span class="n">WindowsDataset</span><span class="p">,</span> <span class="n">BaseConcatDataset</span>
-
-
-<span class="c1"># XXX it&#39;s called concat_ds...</span>
-<div class="viewcode-block" id="create_windows_from_events"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events">[docs]</a><span class="k">def</span> <span class="nf">create_windows_from_events</span><span class="p">(</span>
-        <span class="n">concat_ds</span><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">,</span> <span class="n">accepted_bads_ratio</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create windows based on events in mne.Raw.</span>
-
-<span class="sd">    This function extracts windows of size window_size_samples in the interval</span>
-<span class="sd">    [trial onset + trial_start_offset_samples, trial onset + trial duration +</span>
-<span class="sd">    trial_stop_offset_samples] around each trial, with a separation of</span>
-<span class="sd">    window_stride_samples between consecutive windows. If the last window</span>
-<span class="sd">    around an event does not end at trial_stop_offset_samples and</span>
-<span class="sd">    drop_last_window is set to False, an additional overlapping window that</span>
-<span class="sd">    ends at trial_stop_offset_samples is created.</span>
-
-<span class="sd">    Windows are extracted from the interval defined by the following::</span>
-
-<span class="sd">                                                trial onset +</span>
-<span class="sd">                        trial onset                duration</span>
-<span class="sd">        |--------------------|------------------------|-----------------------|</span>
-<span class="sd">        trial onset -                                             trial onset +</span>
-<span class="sd">        trial_start_offset_samples                                   duration +</span>
-<span class="sd">                                                    trial_stop_offset_samples</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    concat_ds: BaseConcatDataset</span>
-<span class="sd">        A concat of base datasets each holding raw and description.</span>
-<span class="sd">    trial_start_offset_samples: int</span>
-<span class="sd">        Start offset from original trial onsets, in samples. Defaults to zero.</span>
-<span class="sd">    trial_stop_offset_samples: int</span>
-<span class="sd">        Stop offset from original trial stop, in samples. Defaults to zero.</span>
-<span class="sd">    window_size_samples: int | None</span>
-<span class="sd">        Window size. If None, the window size is inferred from the original</span>
-<span class="sd">        trial size of the first trial and trial_start_offset_samples and</span>
-<span class="sd">        trial_stop_offset_samples.</span>
-<span class="sd">    window_stride_samples: int | None</span>
-<span class="sd">        Stride between windows, in samples. If None, the window stride is</span>
-<span class="sd">        inferred from the original trial size of the first trial and</span>
-<span class="sd">        trial_start_offset_samples and trial_stop_offset_samples.</span>
-<span class="sd">    drop_last_window: bool</span>
-<span class="sd">        If False, an additional overlapping window that ends at</span>
-<span class="sd">        trial_stop_offset_samples will be extracted around each event when the</span>
-<span class="sd">        last window does not end exactly at trial_stop_offset_samples.</span>
-<span class="sd">    mapping: dict(str: int)</span>
-<span class="sd">        Mapping from event description to numerical target value.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        If True, preload the data of the Epochs objects. This is useful to</span>
-<span class="sd">        reduce disk reading overhead when returning windows in a training</span>
-<span class="sd">        scenario, however very large data might not fit into memory.</span>
-<span class="sd">    drop_bad_windows: bool</span>
-<span class="sd">        If True, call `.drop_bad()` on the resulting mne.Epochs object. This</span>
-<span class="sd">        step allows identifying e.g., windows that fall outside of the</span>
-<span class="sd">        continuous recording. It is suggested to run this step here as otherwise</span>
-<span class="sd">        the BaseConcatDataset has to be updated as well.</span>
-<span class="sd">    picks: str | list | slice | None</span>
-<span class="sd">        Channels to include. If None, all available channels are used. See</span>
-<span class="sd">        mne.Epochs.</span>
-<span class="sd">    reject: dict | None</span>
-<span class="sd">        Epoch rejection parameters based on peak-to-peak amplitude. If None, no</span>
-<span class="sd">        rejection is done based on peak-to-peak amplitude. See mne.Epochs.</span>
-<span class="sd">    flat: dict | None</span>
-<span class="sd">        Epoch rejection parameters based on flatness of signals. If None, no</span>
-<span class="sd">        rejection based on flatness is done. See mne.Epochs.</span>
-<span class="sd">    on_missing: str</span>
-<span class="sd">        What to do if one or several event ids are not found in the recording.</span>
-<span class="sd">        Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</span>
-<span class="sd">    accepted_bads_ratio: float, optional</span>
-<span class="sd">        Acceptable proportion of trials with inconsistent length in a raw. If</span>
-<span class="sd">        the number of trials whose length is exceeded by the window size is</span>
-<span class="sd">        smaller than this, then only the corresponding trials are dropped, but</span>
-<span class="sd">        the computation continues. Otherwise, an error is raised. Defaults to</span>
-<span class="sd">        0.0 (raise an error).</span>
-<span class="sd">    n_jobs: int</span>
-<span class="sd">        Number of jobs to use to parallelize the windowing.</span>
-<span class="sd">    verbose: bool | str | int | None</span>
-<span class="sd">        Control verbosity of the logging output when calling mne.Epochs.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    windows_ds: WindowsDataset</span>
-<span class="sd">        Dataset containing the extracted windows.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_check_windowing_arguments</span><span class="p">(</span>
-        <span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">)</span>
-
-    <span class="c1"># If user did not specify mapping, we extract all events from all datasets</span>
-    <span class="c1"># and map them to increasing integers starting from 0</span>
-    <span class="n">infer_mapping</span> <span class="o">=</span> <span class="n">mapping</span> <span class="ow">is</span> <span class="kc">None</span>
-    <span class="n">mapping</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span> <span class="k">if</span> <span class="n">infer_mapping</span> <span class="k">else</span> <span class="n">mapping</span>
-    <span class="n">infer_window_size_stride</span> <span class="o">=</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span>
-
-    <span class="n">list_of_windows_ds</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">_create_windows_from_events</span><span class="p">)(</span>
-            <span class="n">ds</span><span class="p">,</span> <span class="n">infer_mapping</span><span class="p">,</span> <span class="n">infer_window_size_stride</span><span class="p">,</span>
-            <span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-            <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span>
-            <span class="n">mapping</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="p">,</span> <span class="n">picks</span><span class="p">,</span> <span class="n">reject</span><span class="p">,</span> <span class="n">flat</span><span class="p">,</span>
-            <span class="n">on_missing</span><span class="p">,</span> <span class="n">accepted_bads_ratio</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">list_of_windows_ds</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="create_fixed_length_windows"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows">[docs]</a><span class="k">def</span> <span class="nf">create_fixed_length_windows</span><span class="p">(</span>
-        <span class="n">concat_ds</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;metadata&#39;</span><span class="p">,</span> <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Windower that creates sliding windows.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    concat_ds: ConcatDataset</span>
-<span class="sd">        A concat of base datasets each holding raw and description.</span>
-<span class="sd">    start_offset_samples: int</span>
-<span class="sd">        Start offset from beginning of recording in samples.</span>
-<span class="sd">    stop_offset_samples: int | None</span>
-<span class="sd">        Stop offset from beginning of recording in samples. If None, set to be</span>
-<span class="sd">        the end of the recording.</span>
-<span class="sd">    window_size_samples: int | None</span>
-<span class="sd">        Window size in samples. If None, set to be the maximum possible window size, ie length of</span>
-<span class="sd">        the recording, once offsets are accounted for.</span>
-<span class="sd">    window_stride_samples: int | None</span>
-<span class="sd">        Stride between windows in samples. If None, set to be equal to winddow_size_samples, so</span>
-<span class="sd">        windows will not overlap.</span>
-<span class="sd">    drop_last_window: bool | None</span>
-<span class="sd">        Whether or not have a last overlapping window, when windows do not</span>
-<span class="sd">        equally divide the continuous signal. Must be set to a bool if window size and stride are</span>
-<span class="sd">        not None.</span>
-<span class="sd">    mapping: dict(str: int)</span>
-<span class="sd">        Mapping from event description to target value.</span>
-<span class="sd">    preload: bool</span>
-<span class="sd">        If True, preload the data of the Epochs objects.</span>
-<span class="sd">    drop_bad_windows: bool</span>
-<span class="sd">        If True, call `.drop_bad()` on the resulting mne.Epochs object. This</span>
-<span class="sd">        step allows identifying e.g., windows that fall outside of the</span>
-<span class="sd">        continuous recording. It is suggested to run this step here as</span>
-<span class="sd">        otherwise the BaseConcatDataset has to be updated as well.</span>
-<span class="sd">    picks: str | list | slice | None</span>
-<span class="sd">        Channels to include. If None, all available channels are used. See</span>
-<span class="sd">        mne.Epochs.</span>
-<span class="sd">    reject: dict | None</span>
-<span class="sd">        Epoch rejection parameters based on peak-to-peak amplitude. If None, no</span>
-<span class="sd">        rejection is done based on peak-to-peak amplitude. See mne.Epochs.</span>
-<span class="sd">    flat: dict | None</span>
-<span class="sd">        Epoch rejection parameters based on flatness of signals. If None, no</span>
-<span class="sd">        rejection based on flatness is done. See mne.Epochs.</span>
-<span class="sd">    on_missing: str</span>
-<span class="sd">        What to do if one or several event ids are not found in the recording.</span>
-<span class="sd">        Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</span>
-<span class="sd">    n_jobs: int</span>
-<span class="sd">        Number of jobs to use to parallelize the windowing.</span>
-<span class="sd">    verbose: bool | str | int | None</span>
-<span class="sd">        Control verbosity of the logging output when calling mne.Epochs.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    windows_ds: WindowsDataset</span>
-<span class="sd">        Dataset containing the extracted windows.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">stop_offset_samples</span><span class="p">,</span> <span class="n">drop_last_window</span> <span class="o">=</span> <span class="n">_check_and_set_fixed_length_window_arguments</span><span class="p">(</span>
-        <span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="p">)</span>
-
-    <span class="c1"># check if recordings are of different lengths</span>
-    <span class="n">lengths</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">])</span>
-    <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">and</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Recordings have different lengths, they will not be batch-able!&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">window_size_samples</span> <span class="o">&gt;</span> <span class="n">lengths</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Window size </span><span class="si">{</span><span class="n">window_size_samples</span><span class="si">}</span><span class="s1"> exceeds trial &#39;</span>
-                         <span class="sa">f</span><span class="s1">&#39;duration </span><span class="si">{</span><span class="n">lengths</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
-
-    <span class="n">list_of_windows_ds</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">_create_fixed_length_windows</span><span class="p">)(</span>
-            <span class="n">ds</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span>
-            <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span> <span class="n">mapping</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span>
-            <span class="n">drop_bad_windows</span><span class="p">,</span> <span class="n">picks</span><span class="p">,</span> <span class="n">reject</span><span class="p">,</span> <span class="n">flat</span><span class="p">,</span> <span class="n">targets_from</span><span class="p">,</span> <span class="n">last_target_only</span><span class="p">,</span>
-            <span class="n">on_missing</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">list_of_windows_ds</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_create_windows_from_events</span><span class="p">(</span>
-        <span class="n">ds</span><span class="p">,</span> <span class="n">infer_mapping</span><span class="p">,</span> <span class="n">infer_window_size_stride</span><span class="p">,</span>
-        <span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">,</span> <span class="n">accepted_bads_ratio</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create WindowsDataset from BaseDataset based on events.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ds : BaseDataset</span>
-<span class="sd">        Dataset containing continuous data and description.</span>
-<span class="sd">    infer_mapping : bool</span>
-<span class="sd">        If True, extract all events from all datasets and map them to</span>
-<span class="sd">        increasing integers starting from 0.</span>
-<span class="sd">    infer_window_size_stride : bool</span>
-<span class="sd">        If True, infer the stride from the original trial size of the first</span>
-<span class="sd">        trial and trial_start_offset_samples and trial_stop_offset_samples.</span>
-
-<span class="sd">    See `create_windows_from_events` for description of other parameters.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    WindowsDataset :</span>
-<span class="sd">        Windowed dataset.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># catch window_kwargs to store to dataset</span>
-    <span class="n">window_kwargs</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">create_windows_from_events</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">_get_windowing_kwargs</span><span class="p">(</span><span class="nb">locals</span><span class="p">())),</span>
-    <span class="p">]</span>
-    <span class="k">if</span> <span class="n">infer_mapping</span><span class="p">:</span>
-        <span class="n">unique_events</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">annotations</span><span class="o">.</span><span class="n">description</span><span class="p">)</span>
-        <span class="n">new_unique_events</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">unique_events</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">mapping</span><span class="p">]</span>
-        <span class="c1"># mapping event descriptions to integers from 0 on</span>
-        <span class="n">max_id_mapping</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mapping</span><span class="p">)</span>
-        <span class="n">mapping</span><span class="o">.</span><span class="n">update</span><span class="p">(</span>
-            <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="o">+</span> <span class="n">max_id_mapping</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_unique_events</span><span class="p">)}</span>
-        <span class="p">)</span>
-
-    <span class="n">events</span><span class="p">,</span> <span class="n">events_id</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">events_from_annotations</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="p">,</span> <span class="n">mapping</span><span class="p">)</span>
-    <span class="n">onsets</span> <span class="o">=</span> <span class="n">events</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
-    <span class="c1"># Onsets are relative to the beginning of the recording</span>
-    <span class="n">filtered_durations</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">a</span><span class="p">[</span><span class="s1">&#39;duration&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">annotations</span>
-            <span class="k">if</span> <span class="n">a</span><span class="p">[</span><span class="s1">&#39;description&#39;</span><span class="p">]</span> <span class="ow">in</span> <span class="n">events_id</span><span class="p">]</span>
-    <span class="p">)</span>
-    <span class="n">stops</span> <span class="o">=</span> <span class="n">onsets</span> <span class="o">+</span> <span class="p">(</span><span class="n">filtered_durations</span> <span class="o">*</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="c1"># XXX This could probably be simplified by using chunk_duration in</span>
-    <span class="c1">#     `events_from_annotations`</span>
-
-    <span class="n">last_samp</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">first_samp</span> <span class="o">+</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span>
-    <span class="k">if</span> <span class="n">stops</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">trial_stop_offset_samples</span> <span class="o">&gt;</span> <span class="n">last_samp</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-            <span class="s1">&#39;&quot;trial_stop_offset_samples&quot; too large. Stop of last trial &#39;</span>
-            <span class="sa">f</span><span class="s1">&#39;(</span><span class="si">{</span><span class="n">stops</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">) + &quot;trial_stop_offset_samples&quot; &#39;</span>
-            <span class="sa">f</span><span class="s1">&#39;(</span><span class="si">{</span><span class="n">trial_stop_offset_samples</span><span class="si">}</span><span class="s1">) must be smaller than length of&#39;</span>
-            <span class="sa">f</span><span class="s1">&#39; recording (</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span><span class="si">}</span><span class="s1">).&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">infer_window_size_stride</span><span class="p">:</span>
-        <span class="c1"># window size is trial size</span>
-        <span class="k">if</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">window_size_samples</span> <span class="o">=</span> <span class="n">stops</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">trial_stop_offset_samples</span> <span class="o">-</span> <span class="p">(</span>
-                <span class="n">onsets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">trial_start_offset_samples</span><span class="p">)</span>
-            <span class="n">window_stride_samples</span> <span class="o">=</span> <span class="n">window_size_samples</span>
-        <span class="n">this_trial_sizes</span> <span class="o">=</span> <span class="p">(</span><span class="n">stops</span> <span class="o">+</span> <span class="n">trial_stop_offset_samples</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span>
-            <span class="n">onsets</span> <span class="o">+</span> <span class="n">trial_start_offset_samples</span><span class="p">)</span>
-        <span class="c1"># Maybe actually this is not necessary?</span>
-        <span class="c1"># We could also just say we just assume window size=trial size</span>
-        <span class="c1"># in case not given, without this condition...</span>
-        <span class="c1"># but then would have to change functions overall</span>
-        <span class="c1"># to deal with varying window sizes hmmhmh</span>
-        <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">this_trial_sizes</span> <span class="o">==</span> <span class="n">window_size_samples</span><span class="p">),</span> <span class="p">(</span>
-            <span class="s1">&#39;All trial sizes should be the same if you do not supply a window &#39;</span>
-            <span class="s1">&#39;size.&#39;</span><span class="p">)</span>
-
-    <span class="n">description</span> <span class="o">=</span> <span class="n">events</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">i_trials</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="p">,</span> <span class="n">starts</span><span class="p">,</span> <span class="n">stops</span> <span class="o">=</span> <span class="n">_compute_window_inds</span><span class="p">(</span>
-        <span class="n">onsets</span><span class="p">,</span> <span class="n">stops</span><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="p">,</span>
-        <span class="n">trial_stop_offset_samples</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span> <span class="n">accepted_bads_ratio</span><span class="p">)</span>
-
-    <span class="n">events</span> <span class="o">=</span> <span class="p">[[</span><span class="n">start</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">description</span><span class="p">[</span><span class="n">i_trials</span><span class="p">[</span><span class="n">i_start</span><span class="p">]]]</span>
-              <span class="k">for</span> <span class="n">i_start</span><span class="p">,</span> <span class="n">start</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">starts</span><span class="p">)]</span>
-    <span class="n">events</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">events</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">events</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Trial overlap not implemented.&#39;</span><span class="p">)</span>
-
-    <span class="n">description</span> <span class="o">=</span> <span class="n">events</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="n">metadata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-        <span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">:</span> <span class="n">i_window_in_trials</span><span class="p">,</span>
-        <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">:</span> <span class="n">starts</span><span class="p">,</span>
-        <span class="s1">&#39;i_stop_in_trial&#39;</span><span class="p">:</span> <span class="n">stops</span><span class="p">,</span>
-        <span class="s1">&#39;target&#39;</span><span class="p">:</span> <span class="n">description</span><span class="p">})</span>
-
-    <span class="c1"># window size - 1, since tmax is inclusive</span>
-    <span class="n">mne_epochs</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span><span class="p">(</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="p">,</span> <span class="n">events</span><span class="p">,</span> <span class="n">events_id</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">tmax</span><span class="o">=</span><span class="p">(</span><span class="n">window_size_samples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span>
-        <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="n">picks</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="n">reject</span><span class="p">,</span>
-        <span class="n">flat</span><span class="o">=</span><span class="n">flat</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="n">on_missing</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">drop_bad_windows</span><span class="p">:</span>
-        <span class="n">mne_epochs</span><span class="o">.</span><span class="n">drop_bad</span><span class="p">()</span>
-
-    <span class="n">windows_ds</span> <span class="o">=</span> <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">mne_epochs</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">)</span>
-    <span class="c1"># add window_kwargs and raw_preproc_kwargs to windows dataset</span>
-    <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="n">window_kwargs</span><span class="p">)</span>
-    <span class="n">kwargs_name</span> <span class="o">=</span> <span class="s1">&#39;raw_preproc_kwargs&#39;</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">):</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">windows_ds</span>
-
-
-<span class="k">def</span> <span class="nf">_create_fixed_length_windows</span><span class="p">(</span>
-        <span class="n">ds</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;metadata&#39;</span><span class="p">,</span>
-        <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create WindowsDataset from BaseDataset with sliding windows.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ds : BaseDataset</span>
-<span class="sd">        Dataset containing continuous data and description.</span>
-
-<span class="sd">    See `create_fixed_length_windows` for description of other parameters.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    WindowsDataset :</span>
-<span class="sd">        Windowed dataset.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># catch window_kwargs to store to dataset</span>
-    <span class="n">window_kwargs</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">create_fixed_length_windows</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">_get_windowing_kwargs</span><span class="p">(</span><span class="nb">locals</span><span class="p">())),</span>
-    <span class="p">]</span>
-    <span class="n">stop</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> \
-        <span class="k">if</span> <span class="n">stop_offset_samples</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">stop_offset_samples</span>
-
-    <span class="c1"># assume window should be whole recording</span>
-    <span class="k">if</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">window_size_samples</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">start_offset_samples</span>
-    <span class="k">if</span> <span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">window_stride_samples</span> <span class="o">=</span> <span class="n">window_size_samples</span>
-
-    <span class="n">stop</span> <span class="o">=</span> <span class="n">stop</span> <span class="o">-</span> <span class="n">window_size_samples</span> <span class="o">+</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">first_samp</span>
-    <span class="c1"># already includes last incomplete window start</span>
-    <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">first_samp</span> <span class="o">+</span> <span class="n">start_offset_samples</span><span class="p">,</span>
-        <span class="n">stop</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
-        <span class="n">window_stride_samples</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">drop_last_window</span> <span class="ow">and</span> <span class="n">starts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">stop</span><span class="p">:</span>
-        <span class="c1"># if last window does not end at trial stop, make it stop there</span>
-        <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">starts</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span>
-
-    <span class="c1"># get targets from dataset description if they exist</span>
-    <span class="n">target</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">if</span> <span class="n">ds</span><span class="o">.</span><span class="n">target_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="n">ds</span><span class="o">.</span><span class="n">target_name</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">mapping</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="c1"># in case of multiple targets</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
-            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">mapping</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span>
-        <span class="c1"># in case of single value target</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">target</span> <span class="o">=</span> <span class="n">mapping</span><span class="p">[</span><span class="n">target</span><span class="p">]</span>
-
-    <span class="n">fake_events</span> <span class="o">=</span> <span class="p">[[</span><span class="n">start</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">start</span> <span class="ow">in</span> <span class="n">starts</span><span class="p">]</span>
-    <span class="n">metadata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-        <span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)),</span>
-        <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">:</span> <span class="n">starts</span><span class="p">,</span>
-        <span class="s1">&#39;i_stop_in_trial&#39;</span><span class="p">:</span> <span class="n">starts</span> <span class="o">+</span> <span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="s1">&#39;target&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)</span> <span class="o">*</span> <span class="p">[</span><span class="n">target</span><span class="p">]</span>
-    <span class="p">})</span>
-
-    <span class="c1"># window size - 1, since tmax is inclusive</span>
-    <span class="n">mne_epochs</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span><span class="p">(</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="p">,</span> <span class="n">fake_events</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-        <span class="n">tmax</span><span class="o">=</span><span class="p">(</span><span class="n">window_size_samples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span>
-        <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="n">picks</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="n">reject</span><span class="p">,</span>
-        <span class="n">flat</span><span class="o">=</span><span class="n">flat</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="n">on_missing</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">drop_bad_windows</span><span class="p">:</span>
-        <span class="n">mne_epochs</span><span class="o">.</span><span class="n">drop_bad</span><span class="p">()</span>
-
-    <span class="n">window_kwargs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">WindowsDataset</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;targets_from&#39;</span><span class="p">:</span> <span class="n">targets_from</span><span class="p">,</span>
-                                   <span class="s1">&#39;last_target_only&#39;</span><span class="p">:</span> <span class="n">last_target_only</span><span class="p">})</span>
-    <span class="p">)</span>
-    <span class="n">windows_ds</span> <span class="o">=</span> <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">mne_epochs</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">,</span> <span class="n">targets_from</span><span class="o">=</span><span class="n">targets_from</span><span class="p">,</span>
-                                <span class="n">last_target_only</span><span class="o">=</span><span class="n">last_target_only</span><span class="p">)</span>
-    <span class="c1"># add window_kwargs and raw_preproc_kwargs to windows dataset</span>
-    <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="n">window_kwargs</span><span class="p">)</span>
-    <span class="n">kwargs_name</span> <span class="o">=</span> <span class="s1">&#39;raw_preproc_kwargs&#39;</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">):</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">windows_ds</span>
-
-
-<div class="viewcode-block" id="create_windows_from_target_channels"><a class="viewcode-back" href="../../../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels">[docs]</a><span class="k">def</span> <span class="nf">create_windows_from_target_channels</span><span class="p">(</span>
-        <span class="n">concat_ds</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="n">list_of_windows_ds</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">_create_windows_from_target_channels</span><span class="p">)(</span>
-            <span class="n">ds</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="p">,</span> <span class="n">picks</span><span class="p">,</span> <span class="n">reject</span><span class="p">,</span>
-            <span class="n">flat</span><span class="p">,</span> <span class="n">last_target_only</span><span class="p">,</span> <span class="s1">&#39;error&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">BaseConcatDataset</span><span class="p">(</span><span class="n">list_of_windows_ds</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_create_windows_from_target_channels</span><span class="p">(</span>
-        <span class="n">ds</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">reject</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">,</span>
-        <span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;error&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create WindowsDataset from BaseDataset using targets `misc` channels from mne.Raw.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    ds : BaseDataset</span>
-<span class="sd">        Dataset containing continuous data and description.</span>
-
-<span class="sd">    See `create_fixed_length_windows` for description of other parameters.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    WindowsDataset :</span>
-<span class="sd">        Windowed dataset.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">window_kwargs</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="p">(</span><span class="n">create_windows_from_target_channels</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">_get_windowing_kwargs</span><span class="p">(</span><span class="nb">locals</span><span class="p">())),</span>
-    <span class="p">]</span>
-    <span class="n">stop</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">+</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">first_samp</span>
-
-    <span class="n">target</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">picks</span><span class="o">=</span><span class="s1">&#39;misc&#39;</span><span class="p">)</span>
-    <span class="c1"># TODO: handle multi targets present only for some events</span>
-    <span class="n">stops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nonzero</span><span class="p">((</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:])))[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">stops</span> <span class="o">=</span> <span class="n">stops</span><span class="p">[(</span><span class="n">stops</span> <span class="o">&lt;</span> <span class="n">stop</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">stops</span> <span class="o">&gt;=</span> <span class="n">window_size_samples</span><span class="p">)]</span>
-    <span class="n">stops</span> <span class="o">=</span> <span class="n">stops</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="c1"># TODO: Make sure that indices are correct</span>
-    <span class="n">fake_events</span> <span class="o">=</span> <span class="p">[[</span><span class="n">stop</span><span class="p">,</span> <span class="n">window_size_samples</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">stop</span> <span class="ow">in</span> <span class="n">stops</span><span class="p">]</span>
-    <span class="n">metadata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span>
-        <span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)),</span>
-        <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">:</span> <span class="n">stops</span> <span class="o">-</span> <span class="n">window_size_samples</span><span class="p">,</span>
-        <span class="s1">&#39;i_stop_in_trial&#39;</span><span class="p">:</span> <span class="n">stops</span><span class="p">,</span>
-        <span class="s1">&#39;target&#39;</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">fake_events</span><span class="p">)</span> <span class="o">*</span> <span class="p">[</span><span class="n">target</span><span class="p">]</span>
-    <span class="p">})</span>
-
-    <span class="c1"># window size - 1, since tmax is inclusive</span>
-    <span class="n">mne_epochs</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span><span class="p">(</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="p">,</span> <span class="n">fake_events</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">tmin</span><span class="o">=-</span><span class="p">(</span><span class="n">window_size_samples</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span>
-        <span class="n">tmax</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="n">metadata</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="n">picks</span><span class="p">,</span>
-        <span class="n">reject</span><span class="o">=</span><span class="n">reject</span><span class="p">,</span> <span class="n">flat</span><span class="o">=</span><span class="n">flat</span><span class="p">,</span> <span class="n">on_missing</span><span class="o">=</span><span class="n">on_missing</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">drop_bad_windows</span><span class="p">:</span>
-        <span class="n">mne_epochs</span><span class="o">.</span><span class="n">drop_bad</span><span class="p">()</span>
-
-    <span class="n">window_kwargs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">WindowsDataset</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;targets_from&#39;</span><span class="p">:</span> <span class="s1">&#39;channels&#39;</span><span class="p">,</span>
-                                   <span class="s1">&#39;last_target_only&#39;</span><span class="p">:</span> <span class="n">last_target_only</span><span class="p">})</span>
-    <span class="p">)</span>
-    <span class="n">windows_ds</span> <span class="o">=</span> <span class="n">WindowsDataset</span><span class="p">(</span><span class="n">mne_epochs</span><span class="p">,</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">,</span> <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;channels&#39;</span><span class="p">,</span>
-                                <span class="n">last_target_only</span><span class="o">=</span><span class="n">last_target_only</span><span class="p">)</span>
-    <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="s1">&#39;window_kwargs&#39;</span><span class="p">,</span> <span class="n">window_kwargs</span><span class="p">)</span>
-    <span class="n">kwargs_name</span> <span class="o">=</span> <span class="s1">&#39;raw_preproc_kwargs&#39;</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">):</span>
-        <span class="nb">setattr</span><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">,</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">kwargs_name</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">windows_ds</span>
-
-
-<span class="k">def</span> <span class="nf">_compute_window_inds</span><span class="p">(</span>
-        <span class="n">starts</span><span class="p">,</span> <span class="n">stops</span><span class="p">,</span> <span class="n">start_offset</span><span class="p">,</span> <span class="n">stop_offset</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span>
-        <span class="n">drop_last_window</span><span class="p">,</span> <span class="n">accepted_bads_ratio</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute window start and stop indices.</span>
-
-<span class="sd">    Create window starts from trial onsets (shifted by start_offset) to trial</span>
-<span class="sd">    end (shifted by stop_offset) separated by stride, as long as window size</span>
-<span class="sd">    fits into trial.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    starts: array-like</span>
-<span class="sd">        Trial starts in samples.</span>
-<span class="sd">    stops: array-like</span>
-<span class="sd">        Trial stops in samples.</span>
-<span class="sd">    start_offset: int</span>
-<span class="sd">        Start offset from original trial onsets in samples.</span>
-<span class="sd">    stop_offset: int</span>
-<span class="sd">        Stop offset from original trial stop in samples.</span>
-<span class="sd">    size: int</span>
-<span class="sd">        Window size.</span>
-<span class="sd">    stride: int</span>
-<span class="sd">        Stride between windows.</span>
-<span class="sd">    drop_last_window: bool</span>
-<span class="sd">        Toggles of shifting last window within range or dropping last samples.</span>
-<span class="sd">    accepted_bads_ratio: float</span>
-<span class="sd">        Acceptable proportion of bad trials within a raw. If the number of</span>
-<span class="sd">        trials whose length is exceeded by the window size is smaller than</span>
-<span class="sd">        this, then only the corresponding trials are dropped, but the</span>
-<span class="sd">        computation continues. Otherwise, an error is raised.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    result_lists: (list, list, list, list)</span>
-<span class="sd">        Trial, i_window_in_trial, start sample and stop sample of windows.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">starts</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">starts</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">else</span> <span class="n">starts</span>
-    <span class="n">stops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">stops</span><span class="p">])</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">stops</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="k">else</span> <span class="n">stops</span>
-
-    <span class="n">starts</span> <span class="o">+=</span> <span class="n">start_offset</span>
-    <span class="n">stops</span> <span class="o">+=</span> <span class="n">stop_offset</span>
-    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">size</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">stops</span><span class="o">-</span><span class="n">starts</span><span class="p">)):</span>
-        <span class="n">bads_mask</span> <span class="o">=</span> <span class="n">size</span> <span class="o">&gt;</span> <span class="p">(</span><span class="n">stops</span><span class="o">-</span><span class="n">starts</span><span class="p">)</span>
-        <span class="n">min_duration</span> <span class="o">=</span> <span class="p">(</span><span class="n">stops</span><span class="o">-</span><span class="n">starts</span><span class="p">)</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-        <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">bads_mask</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">accepted_bads_ratio</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">starts</span><span class="p">):</span>
-            <span class="n">starts</span> <span class="o">=</span> <span class="n">starts</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span><span class="p">(</span><span class="n">bads_mask</span><span class="p">)]</span>
-            <span class="n">stops</span> <span class="o">=</span> <span class="n">stops</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">logical_not</span><span class="p">(</span><span class="n">bads_mask</span><span class="p">)]</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s1">&#39;Trials </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">bads_mask</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s1"> are being dropped as the &#39;</span>
-                <span class="sa">f</span><span class="s1">&#39;window size (</span><span class="si">{</span><span class="n">size</span><span class="si">}</span><span class="s1">) exceeds their duration </span><span class="si">{</span><span class="n">min_duration</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">current_ratio</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">bads_mask</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">starts</span><span class="p">)</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Window size </span><span class="si">{</span><span class="n">size</span><span class="si">}</span><span class="s1"> exceeds trial duration &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;(</span><span class="si">{</span><span class="n">min_duration</span><span class="si">}</span><span class="s1">) for too many trials &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;(</span><span class="si">{</span><span class="n">current_ratio</span> <span class="o">*</span> <span class="mi">100</span><span class="si">}</span><span class="s1">%). Set &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;accepted_bads_ratio to at least </span><span class="si">{</span><span class="n">current_ratio</span><span class="si">}</span><span class="s1">&#39;</span>
-                             <span class="s1">&#39;and restart training to be able to continue.&#39;</span><span class="p">)</span>
-
-    <span class="n">i_window_in_trials</span><span class="p">,</span> <span class="n">i_trials</span><span class="p">,</span> <span class="n">window_starts</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">start_i</span><span class="p">,</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">starts</span><span class="p">,</span> <span class="n">stops</span><span class="p">)):</span>
-        <span class="c1"># Generate possible window starts with given stride between original</span>
-        <span class="c1"># trial onsets (shifted by start_offset) and stops</span>
-        <span class="n">possible_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">stop</span><span class="p">,</span> <span class="n">stride</span><span class="p">)</span>
-
-        <span class="c1"># Possible window start is actually a start, if window size fits in</span>
-        <span class="c1"># trial start and stop</span>
-        <span class="k">for</span> <span class="n">i_window</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">possible_starts</span><span class="p">):</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">s</span> <span class="o">+</span> <span class="n">size</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">stop</span><span class="p">:</span>
-                <span class="n">window_starts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
-                <span class="n">i_window_in_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i_window</span><span class="p">)</span>
-                <span class="n">i_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">start_i</span><span class="p">)</span>
-
-        <span class="c1"># If the last window start + window size is not the same as</span>
-        <span class="c1"># stop + stop_offset, create another window that overlaps and stops</span>
-        <span class="c1"># at onset + stop_offset</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">drop_last_window</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">window_starts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span> <span class="o">!=</span> <span class="n">stop</span><span class="p">:</span>
-                <span class="n">window_starts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">stop</span> <span class="o">-</span> <span class="n">size</span><span class="p">)</span>
-                <span class="n">i_window_in_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-                <span class="n">i_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">start_i</span><span class="p">)</span>
-
-    <span class="c1"># Update stops to now be event stops instead of trial stops</span>
-    <span class="n">window_stops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">window_starts</span><span class="p">)</span> <span class="o">+</span> <span class="n">size</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">window_starts</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">window_stops</span><span class="p">)):</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span><span class="si">}</span><span class="s1"> == &#39;</span>
-                         <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">window_starts</span><span class="p">)</span><span class="si">}</span><span class="s1"> == </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">window_stops</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">i_trials</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="p">,</span> <span class="n">window_starts</span><span class="p">,</span> <span class="n">window_stops</span>
-
-
-<span class="k">def</span> <span class="nf">_check_windowing_arguments</span><span class="p">(</span>
-        <span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">):</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">trial_start_offset_samples</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">))</span>
-    <span class="k">assert</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">trial_stop_offset_samples</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">))</span> <span class="ow">or</span>
-           <span class="p">(</span><span class="n">trial_stop_offset_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">))</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window_size_samples</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window_stride_samples</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">integer</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="kc">None</span><span class="p">)))</span>
-    <span class="k">assert</span> <span class="p">(</span><span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="n">window_size_samples</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">(</span>
-            <span class="s2">&quot;window size has to be larger than 0&quot;</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="n">window_stride_samples</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">(</span>
-            <span class="s2">&quot;window stride has to be larger than 0&quot;</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_check_and_set_fixed_length_window_arguments</span><span class="p">(</span><span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">,</span>
-                                                 <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">,</span>
-                                                 <span class="n">drop_last_window</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Raises warnings for incorrect input arguments and will set correct default values for</span>
-<span class="sd">    stop_offset_samples &amp; drop_last_window, if necessary.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">_check_windowing_arguments</span><span class="p">(</span>
-        <span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">,</span>
-        <span class="n">window_size_samples</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">stop_offset_samples</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-            <span class="s1">&#39;Meaning of `trial_stop_offset_samples`=0 has changed, use `None` &#39;</span>
-            <span class="s1">&#39;to indicate end of trial/recording. Using `None`.&#39;</span><span class="p">)</span>
-        <span class="n">stop_offset_samples</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">if</span> <span class="n">start_offset_samples</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">stop_offset_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Usage of offset_sample args in create_fixed_length_windows is deprecated and&#39;</span>
-                      <span class="s1">&#39; will be removed in future versions. Please use &#39;</span>
-                      <span class="s1">&#39;braindecode.preprocessing.preprocess.Preprocessor(&quot;crop&quot;, tmin, tmax)&#39;</span>
-                      <span class="s1">&#39; instead.&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> \
-            <span class="n">drop_last_window</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;drop_last_window must be set if both window_size_samples &amp;&#39;</span>
-                         <span class="s1">&#39; window_stride_samples have also been set&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span>\
-            <span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span>\
-            <span class="n">drop_last_window</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-        <span class="c1"># necessary for following assertion</span>
-        <span class="n">drop_last_window</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">assert</span> <span class="p">(</span><span class="n">window_size_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> \
-           <span class="p">(</span><span class="n">window_stride_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> \
-           <span class="p">(</span><span class="n">drop_last_window</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">stop_offset_samples</span><span class="p">,</span> <span class="n">drop_last_window</span>
-
-
-<span class="k">def</span> <span class="nf">_get_windowing_kwargs</span><span class="p">(</span><span class="n">windowing_func_locals</span><span class="p">):</span>
-    <span class="n">input_kwargs</span> <span class="o">=</span> <span class="n">windowing_func_locals</span>
-    <span class="n">input_kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;ds&#39;</span><span class="p">)</span>
-    <span class="n">windowing_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">input_kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-    <span class="k">return</span> <span class="n">windowing_kwargs</span>
-</pre></div>
-
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diff --git a/stable/_modules/braindecode/regressor.html b/stable/_modules/braindecode/regressor.html
deleted file mode 100644
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-    <div class="search-button__overlay"></div>
-    <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="../../search.html" method="get">
-  <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
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-              
-  <h1>Source code for braindecode.regressor</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">get_scorer</span>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <span class="n">EpochTimer</span><span class="p">,</span> <span class="n">BatchScoring</span><span class="p">,</span> <span class="n">PrintLog</span><span class="p">,</span> <span class="n">EpochScoring</span>
-<span class="kn">from</span> <span class="nn">skorch.classifier</span> <span class="kn">import</span> <span class="n">NeuralNet</span>
-<span class="kn">from</span> <span class="nn">skorch.regressor</span> <span class="kn">import</span> <span class="n">NeuralNetRegressor</span>
-<span class="kn">from</span> <span class="nn">skorch.utils</span> <span class="kn">import</span> <span class="n">train_loss_score</span><span class="p">,</span> <span class="n">valid_loss_score</span><span class="p">,</span> <span class="n">noop</span><span class="p">,</span> <span class="n">to_numpy</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">.training.scoring</span> <span class="kn">import</span> <span class="p">(</span><span class="n">PostEpochTrainScoring</span><span class="p">,</span>
-                               <span class="n">CroppedTrialEpochScoring</span><span class="p">,</span>
-                               <span class="n">CroppedTimeSeriesEpochScoring</span><span class="p">,</span>
-                               <span class="n">predict_trials</span><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">.util</span> <span class="kn">import</span> <span class="n">ThrowAwayIndexLoader</span><span class="p">,</span> <span class="n">update_estimator_docstring</span>
-
-
-<div class="viewcode-block" id="EEGRegressor"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor">[docs]</a><span class="k">class</span> <span class="nc">EEGRegressor</span><span class="p">(</span><span class="n">NeuralNetRegressor</span><span class="p">):</span>
-    <span class="n">doc</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;Regressor that calls loss function directly.</span>
-
-<span class="s2">    Parameters</span>
-<span class="s2">    ----------</span>
-<span class="s2">    cropped: bool (default=False)</span>
-<span class="s2">        Defines whether torch model passed to this class is cropped or not.</span>
-<span class="s2">        Currently used for callbacks definition.</span>
-
-<span class="s2">    callbacks: None or list of strings or list of Callback instances (default=None)</span>
-<span class="s2">        More callbacks, in addition to those returned by</span>
-<span class="s2">        ``get_default_callbacks``. Each callback should inherit from</span>
-<span class="s2">        :class:`skorch.callbacks.Callback`. If not ``None``, callbacks can be a</span>
-<span class="s2">        list of strings specifying `sklearn` scoring functions (for scoring</span>
-<span class="s2">        functions names see: https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter)</span>
-<span class="s2">        or a list of callbacks where the callback names are inferred from the</span>
-<span class="s2">        class name. Name conflicts are resolved by appending a count suffix</span>
-<span class="s2">        starting with 1, e.g. ``EpochScoring_1``. Alternatively,</span>
-<span class="s2">        a tuple ``(name, callback)`` can be passed, where ``name``</span>
-<span class="s2">        should be unique. Callbacks may or may not be instantiated.</span>
-<span class="s2">        The callback name can be used to set parameters on specific</span>
-<span class="s2">        callbacks (e.g., for the callback with name ``&#39;print_log&#39;``, use</span>
-<span class="s2">        ``net.set_params(callbacks__print_log__keys_ignored=[&#39;epoch&#39;,</span>
-<span class="s2">        &#39;train_loss&#39;])``).</span>
-
-<span class="s2">    iterator_train__shuffle: bool (default=True)</span>
-<span class="s2">        Defines whether train dataset will be shuffled. As skorch does not</span>
-<span class="s2">        shuffle the train dataset by default this one overwrites this option.</span>
-
-<span class="s2">    aggregate_predictions: bool (default=True)</span>
-<span class="s2">        Whether to average cropped predictions to obtain window predictions. Used only in the</span>
-<span class="s2">        cropped mode.</span>
-
-<span class="s2">    &quot;&quot;&quot;</span>  <span class="c1"># noqa: E501</span>
-    <span class="vm">__doc__</span> <span class="o">=</span> <span class="n">update_estimator_docstring</span><span class="p">(</span><span class="n">NeuralNetRegressor</span><span class="p">,</span> <span class="n">doc</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">cropped</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">iterator_train__drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">aggregate_predictions</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span> <span class="o">=</span> <span class="n">cropped</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_predictions</span> <span class="o">=</span> <span class="n">aggregate_predictions</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span>
-                         <span class="n">callbacks</span><span class="o">=</span><span class="n">callbacks</span><span class="p">,</span>
-                         <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="n">iterator_train__shuffle</span><span class="p">,</span>
-                         <span class="n">iterator_train__drop_last</span><span class="o">=</span><span class="n">iterator_train__drop_last</span><span class="p">,</span>
-                         <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_yield_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="c1"># Here we parse the callbacks supplied as strings,</span>
-        <span class="c1"># e.g. &#39;accuracy&#39;, to the callbacks skorch expects</span>
-        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span><span class="p">,</span> <span class="n">named_by_user</span> <span class="ow">in</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_yield_callbacks</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s1">&#39;str&#39;</span><span class="p">:</span>
-                <span class="n">train_cb</span><span class="p">,</span> <span class="n">valid_cb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_str_callback</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
-                <span class="k">yield</span> <span class="n">train_cb</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="k">yield</span> <span class="n">valid_cb</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">yield</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span><span class="p">,</span> <span class="n">named_by_user</span>
-
-    <span class="k">def</span> <span class="nf">_parse_str_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cb_supplied_name</span><span class="p">):</span>
-        <span class="n">scoring</span> <span class="o">=</span> <span class="n">get_scorer</span><span class="p">(</span><span class="n">cb_supplied_name</span><span class="p">)</span>
-        <span class="n">scoring_name</span> <span class="o">=</span> <span class="n">scoring</span><span class="o">.</span><span class="n">_score_func</span><span class="o">.</span><span class="vm">__name__</span>
-        <span class="k">assert</span> <span class="n">scoring_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span>
-                        <span class="p">(</span><span class="s1">&#39;_score&#39;</span><span class="p">,</span> <span class="s1">&#39;_error&#39;</span><span class="p">,</span> <span class="s1">&#39;_deviance&#39;</span><span class="p">,</span> <span class="s1">&#39;_loss&#39;</span><span class="p">))</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">scoring_name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;_score&#39;</span><span class="p">)</span> <span class="ow">or</span>
-                <span class="n">cb_supplied_name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;neg_&#39;</span><span class="p">)):</span>
-            <span class="n">lower_is_better</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">lower_is_better</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">train_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;train_</span><span class="si">{</span><span class="n">cb_supplied_name</span><span class="si">}</span><span class="s1">&#39;</span>
-        <span class="n">valid_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;valid_</span><span class="si">{</span><span class="n">cb_supplied_name</span><span class="si">}</span><span class="s1">&#39;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span><span class="p">:</span>
-            <span class="c1"># TODO: use CroppedTimeSeriesEpochScoring when time series target</span>
-            <span class="c1"># In case of cropped decoding we are using braindecode</span>
-            <span class="c1"># specific scoring created for cropped decoding</span>
-            <span class="n">train_scoring</span> <span class="o">=</span> <span class="n">CroppedTrialEpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">train_name</span>
-            <span class="p">)</span>
-            <span class="n">valid_scoring</span> <span class="o">=</span> <span class="n">CroppedTrialEpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">valid_name</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">train_scoring</span> <span class="o">=</span> <span class="n">PostEpochTrainScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">train_name</span>
-            <span class="p">)</span>
-            <span class="n">valid_scoring</span> <span class="o">=</span> <span class="n">EpochScoring</span><span class="p">(</span>
-                <span class="n">cb_supplied_name</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">valid_name</span>
-            <span class="p">)</span>
-        <span class="n">named_by_user</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="n">train_valid_callbacks</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="p">(</span><span class="n">train_name</span><span class="p">,</span> <span class="n">train_scoring</span><span class="p">,</span> <span class="n">named_by_user</span><span class="p">),</span>
-            <span class="p">(</span><span class="n">valid_name</span><span class="p">,</span> <span class="n">valid_scoring</span><span class="p">,</span> <span class="n">named_by_user</span><span class="p">)</span>
-        <span class="p">]</span>
-        <span class="k">return</span> <span class="n">train_valid_callbacks</span>
-
-    <span class="c1"># pylint: disable=arguments-differ</span>
-<div class="viewcode-block" id="EEGRegressor.get_loss"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.get_loss">[docs]</a>    <span class="k">def</span> <span class="nf">get_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return the loss for this batch by calling NeuralNet get_loss.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        y_pred : torch tensor</span>
-<span class="sd">            Predicted target values</span>
-<span class="sd">        y_true : torch tensor</span>
-<span class="sd">            True target values.</span>
-<span class="sd">        X : input data, compatible with skorch.dataset.Dataset</span>
-<span class="sd">            By default, you should be able to pass:</span>
-
-<span class="sd">                * numpy arrays</span>
-<span class="sd">                * torch tensors</span>
-<span class="sd">                * pandas DataFrame or Series</span>
-<span class="sd">                * scipy sparse CSR matrices</span>
-<span class="sd">                * a dictionary of the former three</span>
-<span class="sd">                * a list/tuple of the former three</span>
-<span class="sd">                * a Dataset</span>
-
-<span class="sd">            If this doesn&#39;t work with your data, you have to pass a</span>
-<span class="sd">            ``Dataset`` that can deal with the data.</span>
-<span class="sd">        training : bool (default=False)</span>
-<span class="sd">            Whether train mode should be used or not.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        loss : float</span>
-<span class="sd">            The loss value.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">NeuralNet</span><span class="o">.</span><span class="n">get_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">y_true</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="EEGRegressor.get_iterator"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.get_iterator">[docs]</a>    <span class="k">def</span> <span class="nf">get_iterator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">drop_index</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">iterator</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="n">training</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">drop_index</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">ThrowAwayIndexLoader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterator</span><span class="p">,</span> <span class="n">is_regression</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">iterator</span></div>
-
-<div class="viewcode-block" id="EEGRegressor.on_batch_end"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.on_batch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_batch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="o">*</span><span class="n">batch</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># If training is false, assume that our loader has indices for this</span>
-        <span class="c1"># batch</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">training</span><span class="p">:</span>
-            <span class="n">epoch_cbs</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">callbacks_</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="p">(</span><span class="n">CroppedTrialEpochScoring</span><span class="p">,</span> <span class="n">CroppedTimeSeriesEpochScoring</span><span class="p">))</span> <span class="ow">and</span> <span class="p">(</span>
-                        <span class="nb">hasattr</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="s1">&#39;window_inds_&#39;</span><span class="p">))</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">cb</span><span class="o">.</span><span class="n">on_train</span><span class="p">):</span>
-                    <span class="n">epoch_cbs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cb</span><span class="p">)</span>
-            <span class="c1"># for trialwise decoding stuffs it might also be we don&#39;t have</span>
-            <span class="c1"># cropped loader, so no indices there</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">epoch_cbs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                <span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">epoch_cbs</span><span class="p">:</span>
-                    <span class="n">cb</span><span class="o">.</span><span class="n">window_inds_</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="o">=</span> <span class="kc">None</span></div>
-
-<div class="viewcode-block" id="EEGRegressor.predict_with_window_inds_and_ys"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys">[docs]</a>    <span class="k">def</span> <span class="nf">predict_with_window_inds_and_ys</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">i_window_in_trials</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">i_window_stops</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">window_ys</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">drop_index</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-            <span class="n">i_window_in_trials</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-            <span class="n">i_window_stops</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-                <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">to_numpy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">))))</span>
-            <span class="n">window_ys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span>
-        <span class="n">i_window_in_trials</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span>
-        <span class="n">i_window_stops</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">i_window_stops</span><span class="p">)</span>
-        <span class="n">window_ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">window_ys</span><span class="p">)</span>
-        <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span>
-            <span class="n">preds</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="o">=</span><span class="n">i_window_in_trials</span><span class="p">,</span>
-            <span class="n">i_window_stops</span><span class="o">=</span><span class="n">i_window_stops</span><span class="p">,</span> <span class="n">window_ys</span><span class="o">=</span><span class="n">window_ys</span><span class="p">)</span></div>
-
-    <span class="c1"># Removes default EpochScoring callback computing &#39;accuracy&#39; to work properly</span>
-    <span class="c1"># with cropped decoding.</span>
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">_default_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">[</span>
-            <span class="p">(</span><span class="s2">&quot;epoch_timer&quot;</span><span class="p">,</span> <span class="n">EpochTimer</span><span class="p">()),</span>
-            <span class="p">(</span>
-                <span class="s2">&quot;train_loss&quot;</span><span class="p">,</span>
-                <span class="n">BatchScoring</span><span class="p">(</span>
-                    <span class="n">train_loss_score</span><span class="p">,</span>
-                    <span class="n">name</span><span class="o">=</span><span class="s2">&quot;train_loss&quot;</span><span class="p">,</span>
-                    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="n">target_extractor</span><span class="o">=</span><span class="n">noop</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">),</span>
-            <span class="p">(</span>
-                <span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span>
-                <span class="n">BatchScoring</span><span class="p">(</span>
-                    <span class="n">valid_loss_score</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;valid_loss&quot;</span><span class="p">,</span> <span class="n">target_extractor</span><span class="o">=</span><span class="n">noop</span><span class="p">,</span>
-                <span class="p">),</span>
-            <span class="p">),</span>
-            <span class="p">(</span><span class="s2">&quot;print_log&quot;</span><span class="p">,</span> <span class="n">PrintLog</span><span class="p">()),</span>
-        <span class="p">]</span>
-
-    <span class="c1"># Method added to fix `predict_proba` behavior to return proper values</span>
-    <span class="c1"># in cropped mode even if regressor does not return probabilities.</span>
-    <span class="c1"># We implement `predict_proba` because it exists in skorch.NeuralNetRegressor.</span>
-<div class="viewcode-block" id="EEGRegressor.predict_proba"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_proba">[docs]</a>    <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return the output of the module&#39;s forward method as a numpy</span>
-<span class="sd">        array. In case of cropped decoding returns averaged values for</span>
-<span class="sd">        each trial.</span>
-
-<span class="sd">        If the module&#39;s forward method returns multiple outputs as a</span>
-<span class="sd">        tuple, it is assumed that the first output contains the</span>
-<span class="sd">        relevant information and the other values are ignored.</span>
-<span class="sd">        If all values are relevant or module&#39;s output for each crop</span>
-<span class="sd">        is needed, consider using :func:`~skorch.NeuralNet.forward`</span>
-<span class="sd">        instead.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X : input data, compatible with skorch.dataset.Dataset</span>
-<span class="sd">          By default, you should be able to pass:</span>
-
-<span class="sd">            * numpy arrays</span>
-<span class="sd">            * torch tensors</span>
-<span class="sd">            * pandas DataFrame or Series</span>
-<span class="sd">            * scipy sparse CSR matrices</span>
-<span class="sd">            * a dictionary of the former three</span>
-<span class="sd">            * a list/tuple of the former three</span>
-<span class="sd">            * a Dataset</span>
-
-<span class="sd">          If this doesn&#39;t work with your data, you have to pass a</span>
-<span class="sd">          ``Dataset`` that can deal with the data.</span>
-
-<span class="sd">        Warnings</span>
-<span class="sd">        --------</span>
-<span class="sd">        Regressors predict regression targets, so output of this method</span>
-<span class="sd">        can&#39;t be interpreted as probabilities. We advise you to use</span>
-<span class="sd">        `predict` method instead of `predict_proba`.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        y_proba : numpy ndarray</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">y_pred</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="c1"># Normally, we have to average the predictions across crops/timesteps</span>
-        <span class="c1"># to get one prediction per window/trial</span>
-        <span class="c1"># Predictions may be already averaged in CroppedTrialEpochScoring (y_pred.shape==2).</span>
-        <span class="c1"># However, when predictions are computed outside of CroppedTrialEpochScoring</span>
-        <span class="c1"># we have to average predictions, hence the check if len(y_pred.shape) == 3</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_predictions</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">y_pred</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">y_pred</span></div>
-
-<div class="viewcode-block" id="EEGRegressor.predict_trials"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_trials">[docs]</a>    <span class="k">def</span> <span class="nf">predict_trials</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Create trialwise predictions and optionally also return trialwise</span>
-<span class="sd">        labels from cropped dataset.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        X: braindecode.datasets.BaseConcatDataset</span>
-<span class="sd">            A braindecode dataset to be predicted.</span>
-<span class="sd">        return_targets: bool</span>
-<span class="sd">            If True, additionally returns the trial targets.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        trial_predictions: np.ndarray</span>
-<span class="sd">            3-dimensional array (n_trials x n_classes x n_predictions), where</span>
-<span class="sd">            the number of predictions depend on the chosen window size and the</span>
-<span class="sd">            receptive field of the network.</span>
-<span class="sd">        trial_labels: np.ndarray</span>
-<span class="sd">            2-dimensional array (n_trials x n_targets) where the number of</span>
-<span class="sd">            targets depends on the decoding paradigm and can be either a single</span>
-<span class="sd">            value, multiple values, or a sequence.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">cropped</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
-                <span class="s2">&quot;This method was designed to predict trials in cropped mode. &quot;</span>
-                <span class="s2">&quot;Calling it when cropped is False will give the same result as &quot;</span>
-                <span class="s2">&quot;&#39;.predict&#39;.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-            <span class="n">preds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">return_targets</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">preds</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))])</span>
-            <span class="k">return</span> <span class="n">preds</span>
-        <span class="k">return</span> <span class="n">predict_trials</span><span class="p">(</span>
-            <span class="n">module</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">,</span>
-            <span class="n">dataset</span><span class="o">=</span><span class="n">X</span><span class="p">,</span>
-            <span class="n">return_targets</span><span class="o">=</span><span class="n">return_targets</span><span class="p">,</span>
-            <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
-            <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">loader</span><span class="o">.</span><span class="n">num_workers</span><span class="p">,</span>
-        <span class="p">)</span></div>
-
-<div class="viewcode-block" id="EEGRegressor.fit"><a class="viewcode-back" href="../../generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">y</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
-</pre></div>
-
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
-</div>
-            </footer>
-            
-          </div>
-          
-          
-          
-            <div class="bd-sidebar-secondary bd-toc">
-              
-<div class="toc-item">
-  
-<div id="searchbox"></div>
-</div>
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-        <footer class="bd-footer-content">
-          <div class="bd-footer-content__inner">
-            
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-        </footer>
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-  <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script src="../../_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536"></script>
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-  <footer class="bd-footer"><div class="bd-footer__inner container">
-  
-  <div class="footer-item">
-    
-<p class="copyright">
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\ No newline at end of file
diff --git a/stable/_modules/braindecode/samplers/base.html b/stable/_modules/braindecode/samplers/base.html
deleted file mode 100644
index f96558b7..00000000
--- a/stable/_modules/braindecode/samplers/base.html
+++ /dev/null
@@ -1,704 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>braindecode.samplers.base &#8212; Braindecode 0.7 documentation</title>
-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
-    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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-  <link rel="stylesheet"
-    href="../../../_static/vendor/fontawesome/6.1.2/css/all.min.css">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2">
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-    href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2">
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-        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
-        </script>
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-  <meta name="viewport" content="width=device-width, initial-scale=1" />
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-  <label class="overlay overlay-secondary" for="__secondary"></label>
-
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-  <div class="search-button__wrapper">
-    <div class="search-button__overlay"></div>
-    <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
-  <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
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-</form>
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-  <nav class="bd-header navbar navbar-expand-lg bd-navbar" id="navbar-main"><div class="bd-header__inner bd-page-width">
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-  <h1>Source code for braindecode.samplers.base</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Sampler classes.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Theo Gnassounou &lt;&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data.sampler</span> <span class="kn">import</span> <span class="n">Sampler</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_random_state</span>
-
-
-<div class="viewcode-block" id="RecordingSampler"><a class="viewcode-back" href="../../../generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler">[docs]</a><span class="k">class</span> <span class="nc">RecordingSampler</span><span class="p">(</span><span class="n">Sampler</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Base sampler simplifying sampling from recordings.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    metadata : pd.DataFrame</span>
-<span class="sd">        DataFrame with at least one of {subject, session, run} columns for each</span>
-<span class="sd">        window in the BaseConcatDataset to sample examples from. Normally</span>
-<span class="sd">        obtained with `BaseConcatDataset.get_metadata()`. For instance,</span>
-<span class="sd">        `metadata.head()` might look like this:</span>
-
-<span class="sd">           i_window_in_trial  i_start_in_trial  i_stop_in_trial  target  subject    session    run</span>
-<span class="sd">        0                  0                 0              500      -1        4  session_T  run_0</span>
-<span class="sd">        1                  1               500             1000      -1        4  session_T  run_0</span>
-<span class="sd">        2                  2              1000             1500      -1        4  session_T  run_0</span>
-<span class="sd">        3                  3              1500             2000      -1        4  session_T  run_0</span>
-<span class="sd">        4                  4              2000             2500      -1        4  session_T  run_0</span>
-
-<span class="sd">    random_state : np.RandomState | int | None</span>
-<span class="sd">        Random state.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    info : pd.DataFrame</span>
-<span class="sd">        Series with MultiIndex index which contains the subject, session, run</span>
-<span class="sd">        and window indices information in an easily accessible structure for</span>
-<span class="sd">        quick sampling of windows.</span>
-<span class="sd">    n_recordings : int</span>
-<span class="sd">        Number of recordings available.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">info</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_info</span><span class="p">(</span><span class="n">metadata</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_init_info</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">required_keys</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Initialize ``info`` DataFrame.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        required_keys : list(str) | None</span>
-<span class="sd">            List of additional columns of the metadata DataFrame that we should</span>
-<span class="sd">            groupby when creating ``info``.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">            See class attributes.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">keys</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">,</span> <span class="s1">&#39;session&#39;</span><span class="p">,</span> <span class="s1">&#39;run&#39;</span><span class="p">]</span>
-                <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">keys</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="s1">&#39;metadata must contain at least one of the following columns: &#39;</span>
-                <span class="s1">&#39;subject, session or run.&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">required_keys</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">missing_keys</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">required_keys</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">columns</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">missing_keys</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s1">&#39;Columns </span><span class="si">{</span><span class="n">missing_keys</span><span class="si">}</span><span class="s1"> were not found in metadata.&#39;</span><span class="p">)</span>
-            <span class="n">keys</span> <span class="o">+=</span> <span class="n">required_keys</span>
-
-        <span class="n">metadata</span> <span class="o">=</span> <span class="n">metadata</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-            <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;index&#39;</span><span class="p">:</span> <span class="s1">&#39;window_index&#39;</span><span class="p">})</span>
-        <span class="n">info</span> <span class="o">=</span> <span class="n">metadata</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="n">keys</span><span class="p">)[</span>
-            <span class="p">[</span><span class="s1">&#39;index&#39;</span><span class="p">,</span> <span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">&#39;unique&#39;</span><span class="p">])</span>
-        <span class="n">info</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">info</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_level_values</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">info</span>
-
-<div class="viewcode-block" id="RecordingSampler.sample_recording"><a class="viewcode-back" href="../../../generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_recording">[docs]</a>    <span class="k">def</span> <span class="nf">sample_recording</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return a random recording index.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># XXX docstring missing</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_recordings</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="RecordingSampler.sample_window"><a class="viewcode-back" href="../../../generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_window">[docs]</a>    <span class="k">def</span> <span class="nf">sample_window</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rec_ind</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return a specific window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># XXX docstring missing</span>
-        <span class="k">if</span> <span class="n">rec_ind</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">rec_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_recording</span><span class="p">()</span>
-        <span class="n">win_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind</span><span class="p">][</span><span class="s1">&#39;index&#39;</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">win_ind</span><span class="p">,</span> <span class="n">rec_ind</span></div>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">n_recordings</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="SequenceSampler"><a class="viewcode-back" href="../../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler">[docs]</a><span class="k">class</span> <span class="nc">SequenceSampler</span><span class="p">(</span><span class="n">RecordingSampler</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sample sequences of consecutive windows.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    metadata : pd.DataFrame</span>
-<span class="sd">        See RecordingSampler.</span>
-<span class="sd">    n_windows : int</span>
-<span class="sd">        Number of consecutive windows in a sequence.</span>
-<span class="sd">    n_windows_stride : int</span>
-<span class="sd">        Number of windows between two consecutive sequences.</span>
-<span class="sd">    random_state : np.random.RandomState | int | None</span>
-<span class="sd">        Random state.</span>
-
-<span class="sd">    Attributes</span>
-<span class="sd">    ----------</span>
-<span class="sd">    info : pd.DataFrame</span>
-<span class="sd">        See RecordingSampler.</span>
-<span class="sd">    file_ids : np.ndarray of ints</span>
-<span class="sd">        Array of shape (n_sequences,) that indicates from which file each</span>
-<span class="sd">        sequence comes from. Useful e.g. to do self-ensembling.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">n_windows</span><span class="p">,</span> <span class="n">n_windows_stride</span><span class="p">,</span>
-                 <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">metadata</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span> <span class="o">=</span> <span class="n">n_windows</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_windows_stride</span> <span class="o">=</span> <span class="n">n_windows_stride</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">start_inds</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">file_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_seq_start_inds</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">_compute_seq_start_inds</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Compute sequence start indices.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        np.ndarray :</span>
-<span class="sd">            Array of shape (n_sequences,) containing the indices of the first</span>
-<span class="sd">            windows of possible sequences.</span>
-<span class="sd">        np.ndarray :</span>
-<span class="sd">            Array of shape (n_sequences,) containing the unique file number of</span>
-<span class="sd">            each sequence. Useful e.g. to do self-ensembling.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">end_offset</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="kc">None</span>
-        <span class="n">start_inds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;index&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
-            <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[:</span><span class="n">end_offset</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">n_windows_stride</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
-        <span class="n">file_ids</span> <span class="o">=</span> <span class="p">[[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">inds</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">inds</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">start_inds</span><span class="p">)]</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">start_inds</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">file_ids</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">start_inds</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">start_ind</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">start_inds</span><span class="p">:</span>
-            <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">start_ind</span><span class="p">,</span> <span class="n">start_ind</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="BalancedSequenceSampler"><a class="viewcode-back" href="../../../generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler">[docs]</a><span class="k">class</span> <span class="nc">BalancedSequenceSampler</span><span class="p">(</span><span class="n">RecordingSampler</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Balanced sampling of sequences of consecutive windows with categorical</span>
-<span class="sd">    targets.</span>
-
-<span class="sd">    Balanced sampling of sequences inspired by the approach of [Perslev2021]_:</span>
-<span class="sd">    1. Uniformly sample a recording out of the available ones.</span>
-<span class="sd">    2. Uniformly sample one of the classes.</span>
-<span class="sd">    3. Sample a window of the corresponding class in the selected recording.</span>
-<span class="sd">    4. Extract a sequence of windows around the sampled window.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    metadata : pd.DataFrame</span>
-<span class="sd">        See RecordingSampler.</span>
-<span class="sd">        Must contain a column `target` with categorical targets.</span>
-<span class="sd">    n_windows : int</span>
-<span class="sd">        Number of consecutive windows in a sequence.</span>
-<span class="sd">    n_sequences : int</span>
-<span class="sd">        Number of sequences to sample.</span>
-<span class="sd">    random_state : np.random.RandomState | int | None</span>
-<span class="sd">        Random state.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Perslev2021] Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,</span>
-<span class="sd">           Igel C. U-Sleep: resilient high-frequency sleep staging. npj Digit.</span>
-<span class="sd">           Med. 4, 72 (2021).</span>
-<span class="sd">           https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">n_windows</span><span class="p">,</span> <span class="n">n_sequences</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">metadata</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span> <span class="o">=</span> <span class="n">n_windows</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_sequences</span> <span class="o">=</span> <span class="n">n_sequences</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">info_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_info</span><span class="p">(</span><span class="n">metadata</span><span class="p">,</span> <span class="n">required_keys</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;target&#39;</span><span class="p">])</span>
-
-<div class="viewcode-block" id="BalancedSequenceSampler.sample_class"><a class="viewcode-back" href="../../../generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler.sample_class">[docs]</a>    <span class="k">def</span> <span class="nf">sample_class</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rec_ind</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Return a random class.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        rec_ind : int | None</span>
-<span class="sd">            Index to the recording to sample from. If None, the recording will</span>
-<span class="sd">            be uniformly sampled across available recordings.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        int</span>
-<span class="sd">            Sampled class.</span>
-<span class="sd">        int</span>
-<span class="sd">            Index to the recording the class was sampled from.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">rec_ind</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">rec_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_recording</span><span class="p">()</span>
-        <span class="n">available_classes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info_class</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">index</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">available_classes</span><span class="p">),</span> <span class="n">rec_ind</span></div>
-
-    <span class="k">def</span> <span class="nf">_sample_seq_start_ind</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rec_ind</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">class_ind</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Sample a sequence and return its start index.</span>
-
-<span class="sd">        Sample a window associated with a random recording and a random class</span>
-<span class="sd">        and randomly sample a sequence with it inside. The function returns the</span>
-<span class="sd">        index of the beginning of the sequence.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        rec_ind : int | None</span>
-<span class="sd">            Index to the recording to sample from. If None, the recording will</span>
-<span class="sd">            be uniformly sampled across available recordings.</span>
-<span class="sd">        class_ind : int | None</span>
-<span class="sd">            If provided as int, sample a window of the corresponding class. If</span>
-<span class="sd">            None, the class will be uniformly sampled across available classes.</span>
-
-<span class="sd">        Returns</span>
-<span class="sd">        -------</span>
-<span class="sd">        int</span>
-<span class="sd">            Index of the first window of the sequence.</span>
-<span class="sd">        int</span>
-<span class="sd">            Corresponding recording index.</span>
-<span class="sd">        int</span>
-<span class="sd">            Class of the sampled window.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">class_ind</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">class_ind</span><span class="p">,</span> <span class="n">rec_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_class</span><span class="p">(</span><span class="n">rec_ind</span><span class="p">)</span>
-
-        <span class="n">rec_inds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind</span><span class="p">][</span><span class="s1">&#39;index&#39;</span><span class="p">]</span>
-        <span class="n">len_rec_inds</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rec_inds</span><span class="p">)</span>
-
-        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
-            <span class="c1"># Theres&#39;s only one category, e.g. &quot;subject&quot;</span>
-            <span class="n">row</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">row</span><span class="p">])</span>
-        <span class="n">available_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info_class</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-            <span class="n">row</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">class_ind</span><span class="p">]),</span> <span class="s1">&#39;index&#39;</span><span class="p">]</span>
-        <span class="n">win_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">available_indices</span><span class="p">)</span>
-        <span class="n">win_ind_in_rec</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">rec_inds</span> <span class="o">==</span> <span class="n">win_ind</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Minimum and maximum start indices in the sequence</span>
-        <span class="n">min_pos</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">win_ind_in_rec</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">max_pos</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">len_rec_inds</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span><span class="p">,</span> <span class="n">win_ind_in_rec</span><span class="p">)</span>
-        <span class="n">start_ind</span> <span class="o">=</span> <span class="n">rec_inds</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">min_pos</span><span class="p">,</span> <span class="n">max_pos</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
-
-        <span class="k">return</span> <span class="n">start_ind</span><span class="p">,</span> <span class="n">rec_ind</span><span class="p">,</span> <span class="n">class_ind</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_sequences</span>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_sequences</span><span class="p">):</span>
-            <span class="n">start_ind</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_seq_start_ind</span><span class="p">()</span>
-            <span class="k">yield</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">start_ind</span><span class="p">,</span> <span class="n">start_ind</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_windows</span><span class="p">))</span></div>
-</pre></div>
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-  <h1>Source code for braindecode.samplers.ssl</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Self-supervised learning samplers.</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">RecordingSampler</span>
-
-
-<div class="viewcode-block" id="RelativePositioningSampler"><a class="viewcode-back" href="../../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler">[docs]</a><span class="k">class</span> <span class="nc">RelativePositioningSampler</span><span class="p">(</span><span class="n">RecordingSampler</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Sample examples for the relative positioning task from [Banville2020]_.</span>
-
-<span class="sd">    Sample examples as tuples of two window indices, with a label indicating</span>
-<span class="sd">    whether the windows are close or far, as defined by tau_pos and tau_neg.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    metadata : pd.DataFrame</span>
-<span class="sd">        See RecordingSampler.</span>
-<span class="sd">    tau_pos : int</span>
-<span class="sd">        Size of the positive context, in samples. A positive pair contains two</span>
-<span class="sd">        windows x1 and x2 which are separated by at most `tau_pos` samples.</span>
-<span class="sd">    tau_neg : int</span>
-<span class="sd">        Size of the negative context, in samples. A negative pair contains two</span>
-<span class="sd">        windows x1 and x2 which are separated by at least `tau_neg` samples and</span>
-<span class="sd">        at most `tau_max` samples. Ignored if `same_rec_neg` is False.</span>
-<span class="sd">    n_examples : int</span>
-<span class="sd">        Number of pairs to extract.</span>
-<span class="sd">    tau_max : int | None</span>
-<span class="sd">        See `tau_neg`.</span>
-<span class="sd">    same_rec_neg : bool</span>
-<span class="sd">        If True, sample negative pairs from within the same recording. If</span>
-<span class="sd">        False, sample negative pairs from two different recordings.</span>
-<span class="sd">    random_state : None | np.RandomState | int</span>
-<span class="sd">        Random state.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [Banville2020] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,</span>
-<span class="sd">           &amp; Gramfort, A. (2020). Uncovering the structure of clinical EEG</span>
-<span class="sd">           signals with self-supervised learning.</span>
-<span class="sd">           arXiv preprint arXiv:2007.16104.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">metadata</span><span class="p">,</span> <span class="n">tau_pos</span><span class="p">,</span> <span class="n">tau_neg</span><span class="p">,</span> <span class="n">n_examples</span><span class="p">,</span> <span class="n">tau_max</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">metadata</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_pos</span> <span class="o">=</span> <span class="n">tau_pos</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_neg</span> <span class="o">=</span> <span class="n">tau_neg</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="k">if</span> <span class="n">tau_max</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">tau_max</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_examples</span> <span class="o">=</span> <span class="n">n_examples</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">same_rec_neg</span> <span class="o">=</span> <span class="n">same_rec_neg</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">same_rec_neg</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_recordings</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;More than one recording must be available when &#39;</span>
-                             <span class="s1">&#39;using across-recording negative sampling.&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">_sample_pair</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Sample a pair of two windows.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Sample first window</span>
-        <span class="n">win_ind1</span><span class="p">,</span> <span class="n">rec_ind1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_window</span><span class="p">()</span>
-        <span class="n">ts1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">win_ind1</span><span class="p">][</span><span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">]</span>
-        <span class="n">ts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind1</span><span class="p">][</span><span class="s1">&#39;i_start_in_trial&#39;</span><span class="p">]</span>
-
-        <span class="c1"># Decide whether the pair will be positive or negative</span>
-        <span class="n">pair_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
-        <span class="n">win_ind2</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">pair_type</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># Negative example</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">same_rec_neg</span><span class="p">:</span>
-                <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="p">((</span><span class="n">ts</span> <span class="o">&lt;=</span> <span class="n">ts1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_neg</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ts</span> <span class="o">&gt;=</span> <span class="n">ts1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">))</span> <span class="o">|</span>
-                    <span class="p">((</span><span class="n">ts</span> <span class="o">&gt;=</span> <span class="n">ts1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_neg</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ts</span> <span class="o">&lt;=</span> <span class="n">ts1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_max</span><span class="p">))</span>
-                <span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">rec_ind2</span> <span class="o">=</span> <span class="n">rec_ind1</span>
-                <span class="k">while</span> <span class="n">rec_ind2</span> <span class="o">==</span> <span class="n">rec_ind1</span><span class="p">:</span>
-                    <span class="n">win_ind2</span><span class="p">,</span> <span class="n">rec_ind2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample_window</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="n">pair_type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># Positive example</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="p">(</span><span class="n">ts</span> <span class="o">&gt;=</span> <span class="n">ts1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_pos</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">ts</span> <span class="o">&lt;=</span> <span class="n">ts1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">tau_pos</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">win_ind2</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">mask</span><span class="p">[</span><span class="n">ts</span> <span class="o">==</span> <span class="n">ts1</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># same window cannot be sampled twice</span>
-            <span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-            <span class="n">win_ind2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">rec_ind1</span><span class="p">][</span><span class="s1">&#39;index&#39;</span><span class="p">][</span><span class="n">mask</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="n">win_ind1</span><span class="p">,</span> <span class="n">win_ind2</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">pair_type</span><span class="p">)</span>
-
-<div class="viewcode-block" id="RelativePositioningSampler.presample"><a class="viewcode-back" href="../../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler.presample">[docs]</a>    <span class="k">def</span> <span class="nf">presample</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Presample examples.</span>
-
-<span class="sd">        Once presampled, the examples are the same from one epoch to another.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">examples</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_sample_pair</span><span class="p">()</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_examples</span><span class="p">)]</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Iterate over pairs.</span>
-
-<span class="sd">        Yields</span>
-<span class="sd">        ------</span>
-<span class="sd">            (int): position of the first window in the dataset.</span>
-<span class="sd">            (int): position of the second window in the dataset.</span>
-<span class="sd">            (float): 0 for negative pair, 1 for positive pair.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_examples</span><span class="p">):</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;examples&#39;</span><span class="p">):</span>
-                <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">examples</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_pair</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_examples</span></div>
-</pre></div>
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-  <h1>Source code for braindecode.training.losses</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-
-
-<div class="viewcode-block" id="CroppedLoss"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss">[docs]</a><span class="k">class</span> <span class="nc">CroppedLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute Loss after averaging predictions across time.</span>
-<span class="sd">    Assumes predictions are in shape:</span>
-<span class="sd">    n_batch size x n_classes x n_predictions (in time)&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss_function</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">loss_function</span> <span class="o">=</span> <span class="n">loss_function</span>
-
-<div class="viewcode-block" id="CroppedLoss.forward"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        preds: torch.Tensor</span>
-<span class="sd">            Model&#39;s prediction with shape (batch_size, n_classes, n_times).</span>
-<span class="sd">        targets: torch.Tensor</span>
-<span class="sd">            Target labels with shape (batch_size, n_classes, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">avg_preds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">avg_preds</span> <span class="o">=</span> <span class="n">avg_preds</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_function</span><span class="p">(</span><span class="n">avg_preds</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="TimeSeriesLoss"><a class="viewcode-back" href="../../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss">[docs]</a><span class="k">class</span> <span class="nc">TimeSeriesLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Compute Loss between timeseries targets and predictions.</span>
-<span class="sd">    Assumes predictions are in shape:</span>
-<span class="sd">    n_batch size x n_classes x n_predictions (in time)</span>
-<span class="sd">    Assumes targets are in shape:</span>
-<span class="sd">    n_batch size x n_classes x window_len (in time)</span>
-<span class="sd">    If the targets contain NaNs, the NaNs will be masked out and the loss will be only computed for</span>
-<span class="sd">    predictions valid corresponding to valid target values.&quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss_function</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">loss_function</span> <span class="o">=</span> <span class="n">loss_function</span>
-
-<div class="viewcode-block" id="TimeSeriesLoss.forward"><a class="viewcode-back" href="../../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss.forward">[docs]</a>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Forward pass.</span>
-
-<span class="sd">        Parameters</span>
-<span class="sd">        ----------</span>
-<span class="sd">        preds: torch.Tensor</span>
-<span class="sd">            Model&#39;s prediction with shape (batch_size, n_classes, n_times).</span>
-<span class="sd">        targets: torch.Tensor</span>
-<span class="sd">            Target labels with shape (batch_size, n_classes, n_times).</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">n_preds</span> <span class="o">=</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="c1"># slice the targets to fit preds shape</span>
-        <span class="n">targets</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[:,</span> <span class="p">:,</span> <span class="o">-</span><span class="n">n_preds</span><span class="p">:]</span>
-        <span class="c1"># create valid targets mask</span>
-        <span class="n">mask</span> <span class="o">=</span> <span class="o">~</span><span class="n">torch</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-        <span class="c1"># select valid targets that have a matching predictions</span>
-        <span class="n">masked_targets</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-        <span class="n">masked_preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_function</span><span class="p">(</span><span class="n">masked_preds</span><span class="p">,</span> <span class="n">masked_targets</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="mixup_criterion"><a class="viewcode-back" href="../../../generated/braindecode.training.mixup_criterion.html#braindecode.training.mixup_criterion">[docs]</a><span class="k">def</span> <span class="nf">mixup_criterion</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Implements loss for Mixup for EEG data. See [1]_.</span>
-
-<span class="sd">    Implementation based on [2]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    preds : torch.Tensor</span>
-<span class="sd">        Predictions from the model.</span>
-<span class="sd">    target : torch.Tensor | list of torch.Tensor</span>
-<span class="sd">        For predictions without mixup, the targets as a tensor. If mixup has</span>
-<span class="sd">        been applied, a list containing the targets of the two mixed</span>
-<span class="sd">        samples and the mixing coefficients as tensors.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    loss : float</span>
-<span class="sd">        The loss value.</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-<span class="sd">    .. [1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz</span>
-<span class="sd">       mixup: Beyond Empirical Risk Minimization</span>
-<span class="sd">       Online: https://arxiv.org/abs/1710.09412</span>
-<span class="sd">    .. [2] https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">target</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="c1"># unpack target</span>
-        <span class="n">y_a</span><span class="p">,</span> <span class="n">y_b</span><span class="p">,</span> <span class="n">lam</span> <span class="o">=</span> <span class="n">target</span>
-        <span class="c1"># compute loss per sample</span>
-        <span class="n">loss_a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span>
-                                              <span class="n">y_a</span><span class="p">,</span>
-                                              <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
-        <span class="n">loss_b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span>
-                                              <span class="n">y_b</span><span class="p">,</span>
-                                              <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span>
-        <span class="c1"># compute weighted mean</span>
-        <span class="n">ret</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">lam</span><span class="p">,</span> <span class="n">loss_a</span><span class="p">)</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">lam</span><span class="p">,</span> <span class="n">loss_b</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ret</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span>
-                                            <span class="n">target</span><span class="p">)</span></div>
-</pre></div>
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-  <h1>Source code for braindecode.training.scoring</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
-<span class="c1">#          Robin Tibor Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#          Alexandre Gramfort &lt;alexandre.gramfort@inria.fr&gt;</span>
-<span class="c1">#          Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD-3</span>
-
-<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">mne.utils.check</span> <span class="kn">import</span> <span class="n">check_version</span>
-<span class="kn">from</span> <span class="nn">skorch.callbacks.scoring</span> <span class="kn">import</span> <span class="n">EpochScoring</span>
-<span class="kn">from</span> <span class="nn">skorch.utils</span> <span class="kn">import</span> <span class="n">to_numpy</span>
-<span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <span class="n">unpack_data</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
-
-
-<div class="viewcode-block" id="trial_preds_from_window_preds"><a class="viewcode-back" href="../../../generated/braindecode.training.trial_preds_from_window_preds.html#braindecode.training.trial_preds_from_window_preds">[docs]</a><span class="k">def</span> <span class="nf">trial_preds_from_window_preds</span><span class="p">(</span>
-        <span class="n">preds</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="p">,</span> <span class="n">i_stop_in_trials</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Assigning window predictions to trials  while removing duplicate</span>
-<span class="sd">    predictions.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    preds: list of ndarrays (atleast 2darrays)</span>
-<span class="sd">        List of window predictions, in each window prediction</span>
-<span class="sd">         time is in axis=1</span>
-<span class="sd">    i_window_in_trials: list</span>
-<span class="sd">        Index/number of window in trial</span>
-<span class="sd">    i_stop_in_trials: list</span>
-<span class="sd">        stop position of window in trial</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    preds_per_trial: list of ndarrays</span>
-<span class="sd">        Predictions in each trial, duplicates removed</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">i_stop_in_trials</span><span class="p">),</span> <span class="p">(</span>
-        <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span><span class="si">}</span><span class="s1">, </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">i_window_in_trials</span><span class="p">)</span><span class="si">}</span><span class="s1">, </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">i_stop_in_trials</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Algorithm for assigning window predictions to trials</span>
-    <span class="c1"># while removing duplicate predictions:</span>
-    <span class="c1"># Loop through windows:</span>
-    <span class="c1"># In each iteration you have predictions (assumed: #classes x #timesteps,</span>
-    <span class="c1"># or at least #timesteps must be in axis=1)</span>
-    <span class="c1"># and you have i_window_in_trial, i_stop_in_trial</span>
-    <span class="c1"># (i_trial removed from variable names for brevity)</span>
-    <span class="c1"># You first check if the i_window_in_trial is 1 larger</span>
-    <span class="c1"># than in last iteration, then you are still in the same trial</span>
-    <span class="c1"># Otherwise you are in a new trial</span>
-    <span class="c1"># If you are in the same trial, you check for duplicate predictions</span>
-    <span class="c1"># Only take predictions that are after (inclusive)</span>
-    <span class="c1"># the stop of the last iteration (i.e., the index of final prediction</span>
-    <span class="c1"># in the last iteration)</span>
-    <span class="c1"># Then add the duplicate-removed predictions from this window</span>
-    <span class="c1"># to predictions for current trial</span>
-    <span class="n">preds_per_trial</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">cur_trial_preds</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">i_last_stop</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">i_last_window</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-    <span class="k">for</span> <span class="n">window_preds</span><span class="p">,</span> <span class="n">i_window</span><span class="p">,</span> <span class="n">i_stop</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
-            <span class="n">preds</span><span class="p">,</span> <span class="n">i_window_in_trials</span><span class="p">,</span> <span class="n">i_stop_in_trials</span><span class="p">):</span>
-        <span class="n">window_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">window_preds</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">i_window</span> <span class="o">!=</span> <span class="p">(</span><span class="n">i_last_window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
-            <span class="k">assert</span> <span class="n">i_window</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> <span class="p">(</span>
-                <span class="s2">&quot;window numbers in new trial should start from 0&quot;</span><span class="p">)</span>
-            <span class="n">preds_per_trial</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">cur_trial_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-            <span class="n">cur_trial_preds</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">i_last_stop</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">if</span> <span class="n">i_last_stop</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># Remove duplicates</span>
-            <span class="n">n_needed_preds</span> <span class="o">=</span> <span class="n">i_stop</span> <span class="o">-</span> <span class="n">i_last_stop</span>
-            <span class="n">window_preds</span> <span class="o">=</span> <span class="n">window_preds</span><span class="p">[:,</span> <span class="o">-</span><span class="n">n_needed_preds</span><span class="p">:]</span>
-        <span class="n">cur_trial_preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">window_preds</span><span class="p">)</span>
-        <span class="n">i_last_window</span> <span class="o">=</span> <span class="n">i_window</span>
-        <span class="n">i_last_stop</span> <span class="o">=</span> <span class="n">i_stop</span>
-    <span class="c1"># add last trial preds</span>
-    <span class="n">preds_per_trial</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">cur_trial_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">preds_per_trial</span></div>
-
-
-<span class="nd">@contextmanager</span>
-<span class="k">def</span> <span class="nf">_cache_net_forward_iter</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">use_caching</span><span class="p">,</span> <span class="n">y_preds</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Caching context for ``skorch.NeuralNet`` instance.</span>
-<span class="sd">    Returns a modified version of the net whose ``forward_iter``</span>
-<span class="sd">    method will subsequently return cached predictions. Leaving the</span>
-<span class="sd">    context will undo the overwrite of the ``forward_iter`` method.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">use_caching</span><span class="p">:</span>
-        <span class="k">yield</span> <span class="n">net</span>
-        <span class="k">return</span>
-    <span class="n">y_preds</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="n">y_preds</span><span class="p">)</span>
-
-    <span class="c1"># pylint: disable=unused-argument</span>
-    <span class="k">def</span> <span class="nf">cached_forward_iter</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">net</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">yp</span> <span class="ow">in</span> <span class="n">y_preds</span><span class="p">:</span>
-            <span class="k">yield</span> <span class="n">yp</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-
-    <span class="n">net</span><span class="o">.</span><span class="n">forward_iter</span> <span class="o">=</span> <span class="n">cached_forward_iter</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="k">yield</span> <span class="n">net</span>
-    <span class="k">finally</span><span class="p">:</span>
-        <span class="c1"># By setting net.forward_iter we define an attribute</span>
-        <span class="c1"># `forward_iter` that precedes the bound method</span>
-        <span class="c1"># `forward_iter`. By deleting the entry from the attribute</span>
-        <span class="c1"># dict we undo this.</span>
-        <span class="k">del</span> <span class="n">net</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s2">&quot;forward_iter&quot;</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="CroppedTrialEpochScoring"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring">[docs]</a><span class="k">class</span> <span class="nc">CroppedTrialEpochScoring</span><span class="p">(</span><span class="n">EpochScoring</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Class to compute scores for trials from a model that predicts (super)crops.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># XXX needs a docstring !!!</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-            <span class="bp">self</span><span class="p">,</span>
-            <span class="n">scoring</span><span class="p">,</span>
-            <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-            <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-            <span class="n">target_extractor</span><span class="o">=</span><span class="n">to_numpy</span><span class="p">,</span>
-            <span class="n">use_caching</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span>
-            <span class="n">lower_is_better</span><span class="o">=</span><span class="n">lower_is_better</span><span class="p">,</span>
-            <span class="n">on_train</span><span class="o">=</span><span class="n">on_train</span><span class="p">,</span>
-            <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">target_extractor</span><span class="o">=</span><span class="n">target_extractor</span><span class="p">,</span>
-            <span class="n">use_caching</span><span class="o">=</span><span class="n">use_caching</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span> <span class="o">=</span> <span class="p">[]</span>
-
-    <span class="k">def</span> <span class="nf">_initialize_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">_initialize_cache</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">crops_to_trials_computed</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span> <span class="o">=</span> <span class="p">[]</span>
-
-<div class="viewcode-block" id="CroppedTrialEpochScoring.on_batch_end"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_batch_end</span><span class="p">(</span>
-             <span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="c1"># Skorch saves the predictions without moving them from GPU</span>
-        <span class="c1"># https://github.com/skorch-dev/skorch/blob/fe71e3d55a4ae5f5f94ef7bdfc00fca3b3fd267f/skorch/callbacks/scoring.py#L385</span>
-        <span class="c1"># This can cause memory issues in case of a large number of predictions</span>
-        <span class="c1"># Therefore here we move them to CPU already</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">on_batch_end</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_caching</span> <span class="ow">and</span> <span class="n">training</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="CroppedTrialEpochScoring.on_epoch_end"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_epoch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">dataset_train</span><span class="p">,</span> <span class="n">dataset_valid</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_caching</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">crops_to_trials_computed</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">:</span>
-                <span class="c1"># Prevent that rng state of torch is changed by</span>
-                <span class="c1"># creation+usage of iterator</span>
-                <span class="n">rng_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span>
-                <span class="n">pred_results</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict_with_window_inds_and_ys</span><span class="p">(</span>
-                    <span class="n">dataset_train</span><span class="p">)</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">rng_state</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">pred_results</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span><span class="p">]</span>
-                <span class="p">)</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_stops&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span><span class="p">]</span>
-                <span class="p">)</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;preds&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">y_pred</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">y_pred</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span><span class="p">])</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span><span class="p">])</span>
-
-            <span class="c1"># A new trial starts</span>
-            <span class="c1"># when the index of the window in trials</span>
-            <span class="c1"># does not increment by 1</span>
-            <span class="c1"># Add dummy infinity at start</span>
-            <span class="n">window_0_per_trial_mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">],</span> <span class="n">prepend</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">1</span>
-            <span class="n">trial_ys</span> <span class="o">=</span> <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">][</span><span class="n">window_0_per_trial_mask</span><span class="p">]</span>
-            <span class="n">trial_preds</span> <span class="o">=</span> <span class="n">trial_preds_from_window_preds</span><span class="p">(</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;preds&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_stops&#39;</span><span class="p">])</span>
-
-            <span class="c1"># Average across the timesteps of each trial so we have per-trial</span>
-            <span class="c1"># predictions already, these will be just passed through the forward</span>
-            <span class="c1"># method of the classifier/regressor to the skorch scoring function.</span>
-            <span class="c1"># trial_preds is a list, each item is a 2d array classes x time</span>
-            <span class="n">y_preds_per_trial</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-                <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">trial_preds</span><span class="p">]</span>
-            <span class="p">)</span>
-            <span class="c1"># Move into format expected by skorch (list of torch tensors)</span>
-            <span class="n">y_preds_per_trial</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">y_preds_per_trial</span><span class="p">)]</span>
-
-            <span class="c1"># Store the computed trial preds for all Cropped Callbacks</span>
-            <span class="c1"># that are also on same set</span>
-            <span class="n">cbs</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">callbacks_</span>
-            <span class="n">epoch_cbs</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">cb</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">cbs</span> <span class="k">if</span>
-                <span class="nb">isinstance</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="n">CroppedTrialEpochScoring</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span>
-                        <span class="n">cb</span><span class="o">.</span><span class="n">on_train</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">)</span>
-            <span class="p">]</span>
-            <span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">epoch_cbs</span><span class="p">:</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_preds_</span> <span class="o">=</span> <span class="n">y_preds_per_trial</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_trues_</span> <span class="o">=</span> <span class="n">trial_ys</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">crops_to_trials_computed</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset_train</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span> <span class="k">else</span> <span class="n">dataset_valid</span>
-
-        <span class="k">with</span> <span class="n">_cache_net_forward_iter</span><span class="p">(</span>
-                <span class="n">net</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_caching</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span>
-        <span class="p">)</span> <span class="k">as</span> <span class="n">cached_net</span><span class="p">:</span>
-            <span class="n">current_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scoring</span><span class="p">(</span><span class="n">cached_net</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_record_score</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">current_score</span><span class="p">)</span>
-
-        <span class="k">return</span></div></div>
-
-
-<div class="viewcode-block" id="CroppedTimeSeriesEpochScoring"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring">[docs]</a><span class="k">class</span> <span class="nc">CroppedTimeSeriesEpochScoring</span><span class="p">(</span><span class="n">CroppedTrialEpochScoring</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Class to compute scores for trials from a model that predicts (super)crops with</span>
-<span class="sd">    time series target.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-<div class="viewcode-block" id="CroppedTimeSeriesEpochScoring.on_epoch_end"><a class="viewcode-back" href="../../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">dataset_train</span><span class="p">,</span> <span class="n">dataset_valid</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_caching</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">crops_to_trials_computed</span><span class="p">:</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">:</span>
-                <span class="c1"># Prevent that rng state of torch is changed by</span>
-                <span class="c1"># creation+usage of iterator</span>
-                <span class="n">rng_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span>
-                <span class="n">pred_results</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict_with_window_inds_and_ys</span><span class="p">(</span>
-                    <span class="n">dataset_train</span><span class="p">)</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">rng_state</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">pred_results</span> <span class="o">=</span> <span class="p">{}</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span><span class="p">]</span>
-                <span class="p">)</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_stops&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_inds_</span><span class="p">]</span>
-                <span class="p">)</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;preds&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">y_pred</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">y_pred</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span><span class="p">])</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                    <span class="p">[</span><span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span><span class="p">])</span>
-
-            <span class="n">num_preds</span> <span class="o">=</span> <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;preds&#39;</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-            <span class="c1"># slice the targets to fit preds shape</span>
-            <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">targets</span><span class="p">[:,</span> <span class="o">-</span><span class="n">num_preds</span><span class="p">:]</span> <span class="k">for</span> <span class="n">targets</span> <span class="ow">in</span> <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">]</span>
-            <span class="p">]</span>
-
-            <span class="n">trial_preds</span> <span class="o">=</span> <span class="n">trial_preds_from_window_preds</span><span class="p">(</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;preds&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_stops&#39;</span><span class="p">])</span>
-
-            <span class="n">trial_ys</span> <span class="o">=</span> <span class="n">trial_preds_from_window_preds</span><span class="p">(</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;window_ys&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_in_trials&#39;</span><span class="p">],</span>
-                <span class="n">pred_results</span><span class="p">[</span><span class="s1">&#39;i_window_stops&#39;</span><span class="p">])</span>
-
-            <span class="c1"># the output is a list of predictions/targets per trial where each item is a</span>
-            <span class="c1"># timeseries of predictions/targets of shape (n_classes x timesteps)</span>
-
-            <span class="c1"># mask NaNs form targets</span>
-            <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">trial_preds</span><span class="p">)</span>  <span class="c1"># n_classes x timesteps in all trials</span>
-            <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">(</span><span class="n">trial_ys</span><span class="p">)</span>
-            <span class="c1"># create valid targets mask</span>
-            <span class="n">mask</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
-            <span class="c1"># select valid targets that have a matching predictions</span>
-            <span class="n">masked_targets</span> <span class="o">=</span> <span class="n">targets</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-            <span class="c1"># For classification there is only one row in targets and n_classes rows in preds</span>
-            <span class="k">if</span> <span class="n">mask</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">masked_preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[:,</span> <span class="n">mask</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">masked_preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[</span><span class="n">mask</span><span class="p">]</span>
-
-            <span class="c1"># Store the computed trial preds for all Cropped Callbacks</span>
-            <span class="c1"># that are also on same set</span>
-            <span class="n">cbs</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">callbacks_</span>
-            <span class="n">epoch_cbs</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">cb</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">cbs</span> <span class="k">if</span>
-                <span class="nb">isinstance</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="n">CroppedTimeSeriesEpochScoring</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span>
-                    <span class="n">cb</span><span class="o">.</span><span class="n">on_train</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span><span class="p">)</span>
-            <span class="p">]</span>
-            <span class="n">masked_preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">masked_preds</span><span class="o">.</span><span class="n">T</span><span class="p">)]</span>
-            <span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">epoch_cbs</span><span class="p">:</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_preds_</span> <span class="o">=</span> <span class="n">masked_preds</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_trues_</span> <span class="o">=</span> <span class="n">masked_targets</span><span class="o">.</span><span class="n">T</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">crops_to_trials_computed</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset_train</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">on_train</span> <span class="k">else</span> <span class="n">dataset_valid</span>
-
-        <span class="k">with</span> <span class="n">_cache_net_forward_iter</span><span class="p">(</span>
-            <span class="n">net</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_caching</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span>
-        <span class="p">)</span> <span class="k">as</span> <span class="n">cached_net</span><span class="p">:</span>
-            <span class="n">current_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scoring</span><span class="p">(</span><span class="n">cached_net</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_record_score</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">current_score</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="PostEpochTrainScoring"><a class="viewcode-back" href="../../../generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring">[docs]</a><span class="k">class</span> <span class="nc">PostEpochTrainScoring</span><span class="p">(</span><span class="n">EpochScoring</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Epoch Scoring class that recomputes predictions after the epoch</span>
-<span class="sd">    on the training in validation mode.</span>
-
-<span class="sd">    Note: For unknown reasons, this affects global random generator and</span>
-<span class="sd">    therefore all results may change slightly if you add this scoring callback.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    scoring : None, str, or callable (default=None)</span>
-<span class="sd">      If None, use the ``score`` method of the model. If str, it</span>
-<span class="sd">      should be a valid sklearn scorer (e.g. &quot;f1&quot;, &quot;accuracy&quot;). If a</span>
-<span class="sd">      callable, it should have the signature (model, X, y), and it</span>
-<span class="sd">      should return a scalar. This works analogously to the</span>
-<span class="sd">      ``scoring`` parameter in sklearn&#39;s ``GridSearchCV`` et al.</span>
-<span class="sd">    lower_is_better : bool (default=True)</span>
-<span class="sd">      Whether lower scores should be considered better or worse.</span>
-<span class="sd">    name : str or None (default=None)</span>
-<span class="sd">      If not an explicit string, tries to infer the name from the</span>
-<span class="sd">      ``scoring`` argument.</span>
-<span class="sd">    target_extractor : callable (default=to_numpy)</span>
-<span class="sd">      This is called on y before it is passed to scoring.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">scoring</span><span class="p">,</span>
-        <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-        <span class="n">target_extractor</span><span class="o">=</span><span class="n">to_numpy</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">scoring</span><span class="o">=</span><span class="n">scoring</span><span class="p">,</span>
-            <span class="n">lower_is_better</span><span class="o">=</span><span class="n">lower_is_better</span><span class="p">,</span>
-            <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-            <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
-            <span class="n">target_extractor</span><span class="o">=</span><span class="n">target_extractor</span><span class="p">,</span>
-            <span class="n">use_caching</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">)</span>
-
-<div class="viewcode-block" id="PostEpochTrainScoring.on_epoch_end"><a class="viewcode-back" href="../../../generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring.on_epoch_end">[docs]</a>    <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">dataset_train</span><span class="p">,</span> <span class="n">dataset_valid</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">dataset</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">get_dataset</span><span class="p">(</span><span class="n">dataset_train</span><span class="p">)</span>
-            <span class="c1"># Prevent that rng state of torch is changed by</span>
-            <span class="c1"># creation+usage of iterator</span>
-            <span class="c1"># Unfortunatenly calling __iter__() of a pytorch</span>
-            <span class="c1"># DataLoader will change the random state</span>
-            <span class="c1"># Note line below setting rng state back</span>
-            <span class="n">rng_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span>
-            <span class="n">iterator</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">get_iterator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-            <span class="n">y_preds</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="n">y_test</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">iterator</span><span class="p">:</span>
-                <span class="n">batch_X</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">unpack_data</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
-                <span class="c1"># TODO: remove after skorch 0.10 release</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">check_version</span><span class="p">(</span><span class="s1">&#39;skorch&#39;</span><span class="p">,</span> <span class="n">min_version</span><span class="o">=</span><span class="s1">&#39;0.10.1&#39;</span><span class="p">):</span>
-                    <span class="n">yp</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">evaluation_step</span><span class="p">(</span><span class="n">batch_X</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                <span class="c1"># X, y unpacking has been pushed downstream in skorch 0.10</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">yp</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">evaluation_step</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                <span class="n">yp</span> <span class="o">=</span> <span class="n">yp</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
-                <span class="n">y_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_extractor</span><span class="p">(</span><span class="n">batch_y</span><span class="p">))</span>
-                <span class="n">y_preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">yp</span><span class="p">)</span>
-            <span class="n">y_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">y_test</span><span class="p">)</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">rng_state</span><span class="p">)</span>
-
-            <span class="c1"># Adding the recomputed preds to all other</span>
-            <span class="c1"># instances of PostEpochTrainScoring of this</span>
-            <span class="c1"># Skorch-Net (NeuralNet, BraindecodeClassifier etc.)</span>
-            <span class="c1"># (They will be reinitialized to empty lists by skorch</span>
-            <span class="c1"># each epoch)</span>
-            <span class="n">cbs</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">callbacks_</span>
-            <span class="n">epoch_cbs</span> <span class="o">=</span> <span class="p">[</span>
-                <span class="n">cb</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">cbs</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cb</span><span class="p">,</span> <span class="n">PostEpochTrainScoring</span><span class="p">)</span>
-            <span class="p">]</span>
-            <span class="k">for</span> <span class="n">cb</span> <span class="ow">in</span> <span class="n">epoch_cbs</span><span class="p">:</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_preds_</span> <span class="o">=</span> <span class="n">y_preds</span>
-                <span class="n">cb</span><span class="o">.</span><span class="n">y_trues_</span> <span class="o">=</span> <span class="n">y_test</span>
-        <span class="c1"># y pred should be same as self.y_preds_</span>
-        <span class="c1"># Unclear if this also leads to any</span>
-        <span class="c1"># random generator call?</span>
-        <span class="k">with</span> <span class="n">_cache_net_forward_iter</span><span class="p">(</span>
-            <span class="n">net</span><span class="p">,</span> <span class="n">use_caching</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">y_preds</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">y_preds_</span>
-        <span class="p">)</span> <span class="k">as</span> <span class="n">cached_net</span><span class="p">:</span>
-            <span class="n">current_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_scoring</span><span class="p">(</span>
-                <span class="n">cached_net</span><span class="p">,</span> <span class="n">dataset_train</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">y_trues_</span>
-            <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_record_score</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">history</span><span class="p">,</span> <span class="n">current_score</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="predict_trials"><a class="viewcode-back" href="../../../generated/braindecode.training.predict_trials.html#braindecode.training.predict_trials">[docs]</a><span class="k">def</span> <span class="nf">predict_trials</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create trialwise predictions and optionally also return trialwise</span>
-<span class="sd">    labels from cropped dataset given module.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    module: torch.nn.Module</span>
-<span class="sd">        A pytorch model implementing forward.</span>
-<span class="sd">    dataset: braindecode.datasets.BaseConcatDataset</span>
-<span class="sd">        A braindecode dataset to be predicted.</span>
-<span class="sd">    return_targets: bool</span>
-<span class="sd">        If True, additionally returns the trial targets.</span>
-<span class="sd">    batch_size: int</span>
-<span class="sd">        The batch size used to iterate the dataset.</span>
-<span class="sd">    num_workers: int</span>
-<span class="sd">        Number of workers used in DataLoader to iterate the dataset.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">        trial_predictions: np.ndarray</span>
-<span class="sd">            3-dimensional array (n_trials x n_classes x n_predictions), where</span>
-<span class="sd">            the number of predictions depend on the chosen window size and the</span>
-<span class="sd">            receptive field of the network.</span>
-<span class="sd">        trial_labels: np.ndarray</span>
-<span class="sd">            2-dimensional array (n_trials x n_targets) where the number of</span>
-<span class="sd">            targets depends on the decoding paradigm and can be either a single</span>
-<span class="sd">            value, multiple values, or a sequence.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># we have a cropped dataset if there exists at least one trial with more</span>
-    <span class="c1"># than one compute window</span>
-    <span class="n">more_than_one_window</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">()[</span><span class="s1">&#39;i_window_in_trial&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">more_than_one_window</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;This function was designed to predict trials from &#39;</span>
-                      <span class="s1">&#39;cropped datasets, which typically have multiple compute &#39;</span>
-                      <span class="s1">&#39;windows per trial. The given dataset has exactly one &#39;</span>
-                      <span class="s1">&#39;window per trial.&#39;</span><span class="p">)</span>
-    <span class="n">loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
-        <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
-        <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
-        <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="n">device</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">all_preds</span><span class="p">,</span> <span class="n">all_ys</span><span class="p">,</span> <span class="n">all_inds</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-        <span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">loader</span><span class="p">:</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-            <span class="n">preds</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-            <span class="n">all_preds</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">preds</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
-            <span class="n">all_ys</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
-            <span class="n">all_inds</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span>
-    <span class="n">preds_per_trial</span> <span class="o">=</span> <span class="n">trial_preds_from_window_preds</span><span class="p">(</span>
-        <span class="n">preds</span><span class="o">=</span><span class="n">all_preds</span><span class="p">,</span>
-        <span class="n">i_window_in_trials</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_inds</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="mi">3</span><span class="p">]),</span>
-        <span class="n">i_stop_in_trials</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_inds</span><span class="p">[</span><span class="mi">2</span><span class="p">::</span><span class="mi">3</span><span class="p">]),</span>
-    <span class="p">)</span>
-    <span class="n">preds_per_trial</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">preds_per_trial</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">return_targets</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">all_ys</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">():</span>
-            <span class="n">all_ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">all_ys</span><span class="p">)</span>
-            <span class="n">ys_per_trial</span> <span class="o">=</span> <span class="n">all_ys</span><span class="p">[</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_inds</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="mi">3</span><span class="p">]),</span> <span class="n">prepend</span><span class="o">=</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ys_per_trial</span> <span class="o">=</span> <span class="n">trial_preds_from_window_preds</span><span class="p">(</span>
-                <span class="n">preds</span><span class="o">=</span><span class="n">all_ys</span><span class="p">,</span>
-                <span class="n">i_window_in_trials</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_inds</span><span class="p">[</span><span class="mi">0</span><span class="p">::</span><span class="mi">3</span><span class="p">]),</span>
-                <span class="n">i_stop_in_trials</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">all_inds</span><span class="p">[</span><span class="mi">2</span><span class="p">::</span><span class="mi">3</span><span class="p">]),</span>
-            <span class="p">)</span>
-            <span class="n">ys_per_trial</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ys_per_trial</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">preds_per_trial</span><span class="p">,</span> <span class="n">ys_per_trial</span>
-    <span class="k">return</span> <span class="n">preds_per_trial</span></div>
-</pre></div>
-
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-              
-  <h1>Source code for braindecode.util</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">glob</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">random</span>
-<span class="kn">from</span> <span class="nn">warnings</span> <span class="kn">import</span> <span class="n">warn</span>
-
-<span class="kn">import</span> <span class="nn">h5py</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">check_random_state</span>
-
-
-<div class="viewcode-block" id="set_random_seeds"><a class="viewcode-back" href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds">[docs]</a><span class="k">def</span> <span class="nf">set_random_seeds</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">cuda</span><span class="p">,</span> <span class="n">cudnn_benchmark</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Set seeds for python random module numpy.random and torch.</span>
-
-<span class="sd">    For more details about reproducibility in pytorch see</span>
-<span class="sd">    https://pytorch.org/docs/stable/notes/randomness.html</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    seed: int</span>
-<span class="sd">        Random seed.</span>
-<span class="sd">    cuda: bool</span>
-<span class="sd">        Whether to set cuda seed with torch.</span>
-<span class="sd">    cudnn_benchmark: bool (default=None)</span>
-<span class="sd">        Whether pytorch will use cudnn benchmark. When set to `None` it will not modify</span>
-<span class="sd">        torch.backends.cudnn.benchmark (displays warning in the case of possible lack of</span>
-<span class="sd">        reproducibility). When set to True, results may not be reproducible (no warning displayed).</span>
-<span class="sd">        When set to False it may slow down computations.</span>
-
-<span class="sd">    Notes</span>
-<span class="sd">    -----</span>
-<span class="sd">    In some cases setting environment variable `PYTHONHASHSEED` may be needed before running a</span>
-<span class="sd">    script to ensure full reproducibility. See</span>
-<span class="sd">    https://forums.fast.ai/t/solved-reproducibility-where-is-the-randomness-coming-in/31628/14</span>
-
-<span class="sd">    Using this function may not ensure full reproducibility of the results as we do not set</span>
-<span class="sd">    `torch.use_deterministic_algorithms(True)`.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">cuda</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">cudnn_benchmark</span><span class="p">,</span> <span class="nb">bool</span><span class="p">):</span>
-            <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span> <span class="o">=</span> <span class="n">cudnn_benchmark</span>
-        <span class="k">elif</span> <span class="n">cudnn_benchmark</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span><span class="p">:</span>
-                <span class="n">warn</span><span class="p">(</span>
-                    <span class="s2">&quot;torch.backends.cudnn.benchmark was set to True which may results in lack of &quot;</span>
-                    <span class="s2">&quot;reproducibility. In some cases to ensure reproducibility you may need to &quot;</span>
-                    <span class="s2">&quot;set torch.backends.cudnn.benchmark to False.&quot;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s2">&quot;cudnn_benchmark expected to be bool or None, got &#39;</span><span class="si">{</span><span class="n">cudnn_benchmark</span><span class="si">}</span><span class="s2">&#39;&quot;</span>
-            <span class="p">)</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed_all</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">np_to_var</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">tensor_kwargs</span>
-<span class="p">):</span>
-    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;np_to_var has been renamed np_to_th, please use np_to_th instead&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">np_to_th</span><span class="p">(</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">requires_grad</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="n">pin_memory</span><span class="p">,</span>
-        <span class="o">**</span><span class="n">tensor_kwargs</span>
-    <span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">np_to_th</span><span class="p">(</span>
-    <span class="n">X</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">tensor_kwargs</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Convenience function to transform numpy array to `torch.Tensor`.</span>
-
-<span class="sd">    Converts `X` to ndarray using asarray if necessary.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    X: ndarray or list or number</span>
-<span class="sd">        Input arrays</span>
-<span class="sd">    requires_grad: bool</span>
-<span class="sd">        passed on to Variable constructor</span>
-<span class="sd">    dtype: numpy dtype, optional</span>
-<span class="sd">    var_kwargs:</span>
-<span class="sd">        passed on to Variable constructor</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    var: `torch.Tensor`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="s2">&quot;__len__&quot;</span><span class="p">):</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">]</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">X_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">requires_grad</span><span class="p">,</span> <span class="o">**</span><span class="n">tensor_kwargs</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">pin_memory</span><span class="p">:</span>
-        <span class="n">X_tensor</span> <span class="o">=</span> <span class="n">X_tensor</span><span class="o">.</span><span class="n">pin_memory</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">X_tensor</span>
-
-
-<span class="k">def</span> <span class="nf">var_to_np</span><span class="p">(</span><span class="n">var</span><span class="p">):</span>
-    <span class="n">warn</span><span class="p">(</span><span class="s2">&quot;var_to_np has been renamed th_to_np, please use th_to_np instead&quot;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">th_to_np</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">th_to_np</span><span class="p">(</span><span class="n">var</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Convenience function to transform `torch.Tensor` to numpy</span>
-<span class="sd">    array.</span>
-
-<span class="sd">    Should work both for CPU and GPU.&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">var</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-
-
-<span class="k">def</span> <span class="nf">corr</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Computes correlation only between terms of a and terms of b, not within</span>
-<span class="sd">    a and b.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a, b: 2darray, features x samples</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    Correlation between features in x and features in y</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Difference to numpy:</span>
-    <span class="c1"># Correlation only between terms of x and y</span>
-    <span class="c1"># not between x and x or y and y</span>
-    <span class="n">this_cov</span> <span class="o">=</span> <span class="n">cov</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_cov_to_corr</span><span class="p">(</span><span class="n">this_cov</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">cov</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Computes covariance only between terms of a and terms of b, not within</span>
-<span class="sd">    a and b.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    a, b: 2darray, features x samples</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    Covariance between features in x and features in y</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">demeaned_a</span> <span class="o">=</span> <span class="n">a</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">demeaned_b</span> <span class="o">=</span> <span class="n">b</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">this_cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">demeaned_a</span><span class="p">,</span> <span class="n">demeaned_b</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">this_cov</span>
-
-
-<span class="k">def</span> <span class="nf">_cov_to_corr</span><span class="p">(</span><span class="n">this_cov</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
-    <span class="c1"># computing &quot;unbiased&quot; corr</span>
-    <span class="c1"># ddof=1 for unbiased..</span>
-    <span class="n">var_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">var_b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_cov_and_var_to_corr</span><span class="p">(</span><span class="n">this_cov</span><span class="p">,</span> <span class="n">var_a</span><span class="p">,</span> <span class="n">var_b</span><span class="p">)</span>
-
-
-<span class="k">def</span> <span class="nf">_cov_and_var_to_corr</span><span class="p">(</span><span class="n">this_cov</span><span class="p">,</span> <span class="n">var_a</span><span class="p">,</span> <span class="n">var_b</span><span class="p">):</span>
-    <span class="n">divisor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">outer</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">var_a</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">var_b</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">this_cov</span> <span class="o">/</span> <span class="n">divisor</span>
-
-
-<span class="k">def</span> <span class="nf">wrap_reshape_apply_fn</span><span class="p">(</span><span class="n">stat_fn</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">axis_a</span><span class="p">,</span> <span class="n">axis_b</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Reshape two nd-arrays into 2d-arrays, apply function and reshape</span>
-<span class="sd">    result back.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    stat_fn: function</span>
-<span class="sd">        Function to apply to 2d-arrays</span>
-<span class="sd">    a: nd-array: nd-array</span>
-<span class="sd">    b: nd-array</span>
-<span class="sd">    axis_a: int or list of int</span>
-<span class="sd">        sample axis</span>
-<span class="sd">    axis_b: int or list of int</span>
-<span class="sd">        sample axis</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    result: nd-array</span>
-<span class="sd">        The result reshaped to remaining_dims_a + remaining_dims_b</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">axis_a</span><span class="p">,</span> <span class="s2">&quot;__len__&quot;</span><span class="p">):</span>
-        <span class="n">axis_a</span> <span class="o">=</span> <span class="p">[</span><span class="n">axis_a</span><span class="p">]</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">axis_b</span><span class="p">,</span> <span class="s2">&quot;__len__&quot;</span><span class="p">):</span>
-        <span class="n">axis_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">axis_b</span><span class="p">]</span>
-    <span class="n">other_axis_a</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">axis_a</span><span class="p">]</span>
-    <span class="n">other_axis_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="o">.</span><span class="n">ndim</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">axis_b</span><span class="p">]</span>
-    <span class="n">transposed_topo_a</span> <span class="o">=</span> <span class="n">a</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">other_axis_a</span><span class="p">)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">axis_a</span><span class="p">))</span>
-    <span class="n">n_stat_axis_a</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">axis_a</span><span class="p">]</span>
-    <span class="n">n_other_axis_a</span> <span class="o">=</span> <span class="p">[</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">other_axis_a</span><span class="p">]</span>
-    <span class="n">flat_topo_a</span> <span class="o">=</span> <span class="n">transposed_topo_a</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">n_other_axis_a</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">n_stat_axis_a</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="n">transposed_topo_b</span> <span class="o">=</span> <span class="n">b</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">other_axis_b</span><span class="p">)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">axis_b</span><span class="p">))</span>
-    <span class="n">n_stat_axis_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">axis_b</span><span class="p">]</span>
-    <span class="n">n_other_axis_b</span> <span class="o">=</span> <span class="p">[</span><span class="n">b</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">other_axis_b</span><span class="p">]</span>
-    <span class="n">flat_topo_b</span> <span class="o">=</span> <span class="n">transposed_topo_b</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">n_other_axis_b</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">n_stat_axis_b</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">array_equal</span><span class="p">(</span><span class="n">n_stat_axis_a</span><span class="p">,</span> <span class="n">n_stat_axis_b</span><span class="p">)</span>
-    <span class="n">stat_result</span> <span class="o">=</span> <span class="n">stat_fn</span><span class="p">(</span><span class="n">flat_topo_a</span><span class="p">,</span> <span class="n">flat_topo_b</span><span class="p">)</span>
-    <span class="n">topo_result</span> <span class="o">=</span> <span class="n">stat_result</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
-        <span class="nb">tuple</span><span class="p">(</span><span class="n">n_other_axis_a</span><span class="p">)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">n_other_axis_b</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">topo_result</span>
-
-
-<span class="k">def</span> <span class="nf">get_balanced_batches</span><span class="p">(</span>
-    <span class="n">n_trials</span><span class="p">,</span> <span class="n">rng</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">n_batches</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span>
-<span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create indices for batches balanced in size</span>
-<span class="sd">    (batches will have maximum size difference of 1).</span>
-<span class="sd">    Supply either batch size or number of batches. Resulting batches</span>
-<span class="sd">    will not have the given batch size but rather the next largest batch size</span>
-<span class="sd">    that allows to split the set into balanced batches (maximum size difference 1).</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_trials : int</span>
-<span class="sd">        Size of set.</span>
-<span class="sd">    rng : RandomState</span>
-<span class="sd">    shuffle : bool</span>
-<span class="sd">        Whether to shuffle indices before splitting set.</span>
-<span class="sd">    n_batches : int, optional</span>
-<span class="sd">    batch_size : int, optional</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    batches: list of list of int</span>
-<span class="sd">        Indices for each batch.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">n_batches</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="n">n_batches</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">n_batches</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">n_trials</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)))</span>
-
-    <span class="k">if</span> <span class="n">n_batches</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">min_batch_size</span> <span class="o">=</span> <span class="n">n_trials</span> <span class="o">//</span> <span class="n">n_batches</span>
-        <span class="n">n_batches_with_extra_trial</span> <span class="o">=</span> <span class="n">n_trials</span> <span class="o">%</span> <span class="n">n_batches</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">n_batches</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">min_batch_size</span> <span class="o">=</span> <span class="n">n_trials</span>
-        <span class="n">n_batches_with_extra_trial</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">assert</span> <span class="n">n_batches_with_extra_trial</span> <span class="o">&lt;</span> <span class="n">n_batches</span>
-    <span class="n">all_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_trials</span><span class="p">))</span>
-    <span class="k">if</span> <span class="n">shuffle</span><span class="p">:</span>
-        <span class="n">rng</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">all_inds</span><span class="p">)</span>
-    <span class="n">i_start_trial</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">i_stop_trial</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">batches</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i_batch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_batches</span><span class="p">):</span>
-        <span class="n">i_stop_trial</span> <span class="o">+=</span> <span class="n">min_batch_size</span>
-        <span class="k">if</span> <span class="n">i_batch</span> <span class="o">&lt;</span> <span class="n">n_batches_with_extra_trial</span><span class="p">:</span>
-            <span class="n">i_stop_trial</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">batch_inds</span> <span class="o">=</span> <span class="n">all_inds</span><span class="p">[</span><span class="nb">range</span><span class="p">(</span><span class="n">i_start_trial</span><span class="p">,</span> <span class="n">i_stop_trial</span><span class="p">)]</span>
-        <span class="n">batches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch_inds</span><span class="p">)</span>
-        <span class="n">i_start_trial</span> <span class="o">=</span> <span class="n">i_stop_trial</span>
-    <span class="k">assert</span> <span class="n">i_start_trial</span> <span class="o">==</span> <span class="n">n_trials</span>
-    <span class="k">return</span> <span class="n">batches</span>
-
-
-<span class="k">def</span> <span class="nf">create_mne_dummy_raw</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">n_times</span><span class="p">,</span> <span class="n">sfreq</span><span class="p">,</span> <span class="n">include_anns</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                         <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savedir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_format</span><span class="o">=</span><span class="s1">&#39;fif&#39;</span><span class="p">,</span>
-                         <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create an mne.io.RawArray with fake data, and optionally save it.</span>
-
-<span class="sd">    This will overwrite already existing files.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    n_channels : int</span>
-<span class="sd">        Number of channels.</span>
-<span class="sd">    n_times : int</span>
-<span class="sd">        Number of samples.</span>
-<span class="sd">    sfreq : float</span>
-<span class="sd">        Sampling frequency.</span>
-<span class="sd">    include_anns : bool</span>
-<span class="sd">        If True, also create annotations.</span>
-<span class="sd">    description : list | None</span>
-<span class="sd">        List of descriptions used for creating annotations. It should contain</span>
-<span class="sd">        10 elements.</span>
-<span class="sd">    savedir : str | None</span>
-<span class="sd">        If provided as a string, the file will be saved under that directory.</span>
-<span class="sd">    save_format : str | list</span>
-<span class="sd">        If `savedir` is provided, this specifies the file format the data should</span>
-<span class="sd">        be saved to. Can be &#39;raw&#39; or &#39;hdf5&#39;, or a list containing both.</span>
-<span class="sd">    random_state : int | RandomState</span>
-<span class="sd">        Random state for the generation of random data.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    raw : mne.io.Raw</span>
-<span class="sd">        The created Raw object.</span>
-<span class="sd">    save_fname : dict | None</span>
-<span class="sd">        Dictionary containing the name the raw data was saved to.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">random_state</span> <span class="o">=</span> <span class="n">check_random_state</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">random_state</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">n_times</span><span class="p">)</span>
-    <span class="n">ch_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;ch</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_channels</span><span class="p">)]</span>
-    <span class="n">ch_types</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;eeg&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_channels</span>
-    <span class="n">info</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">create_info</span><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="n">ch_names</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="n">sfreq</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="n">ch_types</span><span class="p">)</span>
-
-    <span class="n">raw</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">RawArray</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">info</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">include_anns</span><span class="p">:</span>
-        <span class="n">n_anns</span> <span class="o">=</span> <span class="mi">10</span>
-        <span class="n">inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span>
-            <span class="nb">int</span><span class="p">(</span><span class="n">sfreq</span> <span class="o">*</span> <span class="mi">2</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_times</span> <span class="o">-</span> <span class="n">sfreq</span> <span class="o">*</span> <span class="mi">2</span><span class="p">),</span> <span class="n">num</span><span class="o">=</span><span class="n">n_anns</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="n">onset</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">times</span><span class="p">[</span><span class="n">inds</span><span class="p">]</span>
-        <span class="n">duration</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_anns</span>
-        <span class="k">if</span> <span class="n">description</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">description</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_anns</span>
-        <span class="n">anns</span> <span class="o">=</span> <span class="n">mne</span><span class="o">.</span><span class="n">Annotations</span><span class="p">(</span><span class="n">onset</span><span class="p">,</span> <span class="n">duration</span><span class="p">,</span> <span class="n">description</span><span class="p">)</span>
-        <span class="n">raw</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">set_annotations</span><span class="p">(</span><span class="n">anns</span><span class="p">)</span>
-
-    <span class="n">save_fname</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">savedir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">save_format</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="n">save_format</span> <span class="o">=</span> <span class="p">[</span><span class="n">save_format</span><span class="p">]</span>
-        <span class="n">fname</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">savedir</span><span class="p">,</span> <span class="s1">&#39;fake_eeg_raw&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="s1">&#39;fif&#39;</span> <span class="ow">in</span> <span class="n">save_format</span><span class="p">:</span>
-            <span class="n">fif_fname</span> <span class="o">=</span> <span class="n">fname</span> <span class="o">+</span> <span class="s1">&#39;.fif&#39;</span>
-            <span class="n">raw</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">fif_fname</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="n">overwrite</span><span class="p">)</span>
-            <span class="n">save_fname</span><span class="p">[</span><span class="s1">&#39;fif&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fif_fname</span>
-        <span class="k">if</span> <span class="s1">&#39;hdf5&#39;</span> <span class="ow">in</span> <span class="n">save_format</span><span class="p">:</span>
-            <span class="n">h5_fname</span> <span class="o">=</span> <span class="n">fname</span> <span class="o">+</span> <span class="s1">&#39;.h5&#39;</span>
-            <span class="k">with</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="n">h5_fname</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="n">f</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span>
-                    <span class="s1">&#39;fake_raw&#39;</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;f8&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">raw</span><span class="o">.</span><span class="n">get_data</span><span class="p">())</span>
-            <span class="n">save_fname</span><span class="p">[</span><span class="s1">&#39;hdf5&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">h5_fname</span>
-
-    <span class="k">return</span> <span class="n">raw</span><span class="p">,</span> <span class="n">save_fname</span>
-
-
-<span class="k">class</span> <span class="nc">ThrowAwayIndexLoader</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">loader</span><span class="p">,</span> <span class="n">is_regression</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">net</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">loader</span> <span class="o">=</span> <span class="n">loader</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">last_i</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_regression</span> <span class="o">=</span> <span class="n">is_regression</span>
-
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="p">):</span>
-        <span class="n">normal_iter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loader</span><span class="o">.</span><span class="fm">__iter__</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">normal_iter</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">i</span> <span class="o">=</span> <span class="n">batch</span>
-                <span class="c1"># Store for scoring callbacks</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">_last_window_inds_</span> <span class="o">=</span> <span class="n">i</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">batch</span>
-
-            <span class="c1"># TODO: should be on dataset side</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="s1">&#39;type&#39;</span><span class="p">):</span>
-                <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_regression</span><span class="p">:</span>
-                    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
-            <span class="k">yield</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
-
-
-<span class="k">def</span> <span class="nf">update_estimator_docstring</span><span class="p">(</span><span class="n">base_class</span><span class="p">,</span> <span class="n">docstring</span><span class="p">):</span>
-    <span class="n">base_doc</span> <span class="o">=</span> <span class="n">base_class</span><span class="o">.</span><span class="vm">__doc__</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39; : &#39;</span><span class="p">,</span> <span class="s1">&#39;: &#39;</span><span class="p">)</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="n">base_doc</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;callbacks:&#39;</span><span class="p">)</span>
-    <span class="n">idx_end</span> <span class="o">=</span> <span class="n">idx</span> <span class="o">+</span> <span class="n">base_doc</span><span class="p">[</span><span class="n">idx</span><span class="p">:]</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="c1"># remove callback descripiton already included in braindecode docstring</span>
-    <span class="n">filtered_doc</span> <span class="o">=</span> <span class="n">base_doc</span><span class="p">[:</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="n">base_doc</span><span class="p">[</span><span class="n">idx_end</span> <span class="o">+</span> <span class="mi">6</span><span class="p">:]</span>
-    <span class="n">splitted</span> <span class="o">=</span> <span class="n">docstring</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;Parameters</span><span class="se">\n</span><span class="s1">    ----------</span><span class="se">\n</span><span class="s1">    &#39;</span><span class="p">)</span>
-    <span class="n">out_docstring</span> <span class="o">=</span> <span class="p">(</span>
-        <span class="n">splitted</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span>
-        <span class="n">filtered_doc</span><span class="p">[</span><span class="n">filtered_doc</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Parameters&#39;</span><span class="p">):</span><span class="n">filtered_doc</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Attributes&#39;</span><span class="p">)]</span> <span class="o">+</span>
-        <span class="n">splitted</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span>
-        <span class="n">filtered_doc</span><span class="p">[</span><span class="n">filtered_doc</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">&#39;Attributes&#39;</span><span class="p">):])</span>
-    <span class="k">return</span> <span class="n">out_docstring</span>
-
-
-<span class="k">def</span> <span class="nf">_update_moabb_docstring</span><span class="p">(</span><span class="n">base_class</span><span class="p">,</span> <span class="n">docstring</span><span class="p">):</span>
-    <span class="n">base_doc</span> <span class="o">=</span> <span class="n">base_class</span><span class="o">.</span><span class="vm">__doc__</span>
-    <span class="n">out_docstring</span> <span class="o">=</span> <span class="n">base_doc</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="si">{</span><span class="n">docstring</span><span class="si">}</span><span class="s1">&#39;</span>
-    <span class="k">return</span> <span class="n">out_docstring</span>
-
-
-<span class="k">def</span> <span class="nf">read_all_file_names</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">extension</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Read all files with specified extension from given path and sorts them</span>
-<span class="sd">    based on a given sorting key.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    directory: str</span>
-<span class="sd">        Parent directory to be searched for files of the specified type.</span>
-<span class="sd">    extension: str</span>
-<span class="sd">        File extension, i.e. &quot;.edf&quot; or &quot;.txt&quot;.</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    file_paths: list(str)</span>
-<span class="sd">        List of all files found in (sub)directories of path.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">extension</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
-    <span class="n">file_paths</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">directory</span> <span class="o">+</span> <span class="s1">&#39;**/*&#39;</span> <span class="o">+</span> <span class="n">extension</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">file_paths</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> <span class="p">(</span>
-        <span class="sa">f</span><span class="s1">&#39;something went wrong. Found no </span><span class="si">{</span><span class="n">extension</span><span class="si">}</span><span class="s1"> files in </span><span class="si">{</span><span class="n">directory</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">file_paths</span>
-</pre></div>
-
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
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-            </footer>
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-          
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-          
-            <div class="bd-sidebar-secondary bd-toc">
-              
-<div class="toc-item">
-  
-<div id="searchbox"></div>
-</div>
-
-<div class="toc-item">
-  
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-<div class="toc-item">
-  
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-          
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-        <footer class="bd-footer-content">
-          <div class="bd-footer-content__inner">
-            
-          </div>
-        </footer>
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-      </main>
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-  <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script src="../../_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536"></script>
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-  
-  <div class="footer-item">
-    
-<p class="copyright">
-
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-
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-  </footer>
-  </body>
-</html>
\ No newline at end of file
diff --git a/stable/_modules/braindecode/visualization/confusion_matrices.html b/stable/_modules/braindecode/visualization/confusion_matrices.html
deleted file mode 100644
index 364307ae..00000000
--- a/stable/_modules/braindecode/visualization/confusion_matrices.html
+++ /dev/null
@@ -1,673 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>braindecode.visualization.confusion_matrices &#8212; Braindecode 0.7 documentation</title>
-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
-    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
-  </script>
-  
-  <!-- Loaded before other Sphinx assets -->
-  <link href="../../../_static/styles/theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
-<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
-
-  
-  <link rel="stylesheet"
-    href="../../../_static/vendor/fontawesome/6.1.2/css/all.min.css">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="../../../_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2">
-
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/sg_gallery.css" />
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-    <link rel="stylesheet" type="text/css" href="../../../_static/style.css" />
-  
-  <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="../../../_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536">
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-    <script src="../../../_static/sphinx_highlight.js"></script>
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-        DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
-        </script>
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-  <meta name="viewport" content="width=device-width, initial-scale=1" />
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-  </head>
-  
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-  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="180" data-default-mode="light">
-
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-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
-  <label class="overlay overlay-primary" for="__primary"></label>
-
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-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
-
-  
-  <div class="search-button__wrapper">
-    <div class="search-button__overlay"></div>
-    <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="../../../search.html" method="get">
-  <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
-  <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
-</form>
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-  
-  <nav class="bd-header navbar navbar-expand-lg bd-navbar" id="navbar-main"><div class="bd-header__inner bd-page-width">
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-    
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-    <img src="../../../_static/braindecode_small.svg" class="logo__image only-light" alt="Logo image">
-    <img src="../../../_static/braindecode_small.svg" class="logo__image only-dark" alt="Logo image">
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-    <div id="navbar-center" class="mr-auto">
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-  <h1>Source code for braindecode.visualization.confusion_matrices</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
-
-
-<div class="viewcode-block" id="plot_confusion_matrix"><a class="viewcode-back" href="../../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix">[docs]</a><span class="k">def</span> <span class="nf">plot_confusion_matrix</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">,</span>
-                          <span class="n">class_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">bwr</span><span class="p">,</span>
-                          <span class="n">textcolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                          <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">,</span>
-                          <span class="n">rotate_row_labels</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
-                          <span class="n">rotate_col_labels</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                          <span class="n">with_f1_score</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                          <span class="n">norm_axes</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
-                          <span class="n">rotate_precision</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                          <span class="n">class_names_fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">    Generates a confusion matrix with additional precision and sensitivity metrics as in [1]_.</span>
-
-<span class="sd">    Parameters</span>
-<span class="sd">    ----------</span>
-<span class="sd">    confusion_mat: 2d numpy array</span>
-<span class="sd">        A confusion matrix, e.g. sklearn confusion matrix:</span>
-<span class="sd">        https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html</span>
-<span class="sd">    class_names: array, optional</span>
-<span class="sd">        List of classes/targets.</span>
-<span class="sd">    figsize: tuple, optional</span>
-<span class="sd">        Size of the generated confusion matrix figure.</span>
-<span class="sd">    colormap: matplotlib cm colormap, optional</span>
-<span class="sd">    textcolor: str, optional</span>
-<span class="sd">        Color  of the text in the figure.</span>
-<span class="sd">    vmin, vmax: float, optional</span>
-<span class="sd">        The data range that the colormap covers.</span>
-<span class="sd">    fontweight: str, optional</span>
-<span class="sd">        Weight of the font in the figure:</span>
-<span class="sd">        [ &#39;normal&#39; | &#39;bold&#39; | &#39;heavy&#39; | &#39;light&#39; | &#39;ultrabold&#39; | &#39;ultralight&#39;]</span>
-<span class="sd">    rotate_row_labels: int, optional</span>
-<span class="sd">        The rotation angle of the row labels</span>
-<span class="sd">    rotate_col_labels: int, optional</span>
-<span class="sd">        The rotation angle of the column labels</span>
-<span class="sd">    with_f1_score: bool, optional</span>
-<span class="sd">    norm_axes: tuple, optional</span>
-<span class="sd">    rotate_precision: bool, optional</span>
-<span class="sd">    class_names_fontsize: int, optional</span>
-
-<span class="sd">    Returns</span>
-<span class="sd">    -------</span>
-<span class="sd">    fig: matplotlib figure</span>
-
-<span class="sd">    References</span>
-<span class="sd">    ----------</span>
-
-<span class="sd">    .. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,</span>
-<span class="sd">       Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T. (2017).</span>
-<span class="sd">       Deep learning with convolutional neural networks for EEG decoding and</span>
-<span class="sd">       visualization.</span>
-<span class="sd">       Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># transpose to get confusion matrix same way as matlab</span>
-    <span class="n">confusion_mat</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">n_classes</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">class_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i_class</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i_class</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span>
-
-    <span class="c1"># norm by all targets</span>
-    <span class="n">normed_conf_mat</span> <span class="o">=</span> <span class="n">confusion_mat</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">,</span>
-                                                        <span class="n">axis</span><span class="o">=</span><span class="n">norm_axes</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
-
-    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
-    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">vmin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">normed_conf_mat</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">vmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">normed_conf_mat</span><span class="p">)</span>
-
-    <span class="c1"># see http://stackoverflow.com/a/31397438/1469195</span>
-    <span class="c1"># brighten so that black text remains readable</span>
-    <span class="c1"># used alpha=0.6 before</span>
-    <span class="k">def</span> <span class="nf">_brighten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">):</span>
-        <span class="n">brightened_x</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">*</span> <span class="mf">0.4</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">brightened_x</span>
-
-    <span class="n">brightened_cmap</span> <span class="o">=</span> <span class="n">_cmap_map</span><span class="p">(</span><span class="n">_brighten</span><span class="p">,</span> <span class="n">colormap</span><span class="p">)</span>  <span class="c1"># colormap #</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">normed_conf_mat</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">brightened_cmap</span><span class="p">,</span>
-              <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;nearest&#39;</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
-
-    <span class="c1"># make space for precision and sensitivity</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">normed_conf_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">normed_conf_mat</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="p">)</span>
-    <span class="n">width</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)</span>
-    <span class="n">height</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">width</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">height</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span><span class="p">:</span>
-                <span class="n">this_font_weight</span> <span class="o">=</span> <span class="s1">&#39;bold&#39;</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">this_font_weight</span> <span class="o">=</span> <span class="n">fontweight</span>
-            <span class="n">annotate_str</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{:d}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">y</span><span class="p">])</span>
-            <span class="n">annotate_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">annotate_str</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">y</span><span class="p">]),</span>
-                        <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                        <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-                        <span class="n">color</span><span class="o">=</span><span class="n">textcolor</span><span class="p">,</span>
-                        <span class="n">fontweight</span><span class="o">=</span><span class="n">this_font_weight</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">x</span> <span class="o">!=</span> <span class="n">y</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">with_f1_score</span><span class="p">):</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span>
-                    <span class="s2">&quot;</span><span class="se">\n\n</span><span class="si">{:4.1f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                        <span class="n">normed_conf_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">y</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span>
-                    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">textcolor</span><span class="p">,</span>
-                    <span class="n">fontweight</span><span class="o">=</span><span class="n">this_font_weight</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">assert</span> <span class="n">x</span> <span class="o">==</span> <span class="n">y</span>
-                <span class="n">precision</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
-                    <span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="p">:]))</span>
-                <span class="n">sensitivity</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
-                    <span class="n">confusion_mat</span><span class="p">[:,</span> <span class="n">y</span><span class="p">]))</span>
-                <span class="n">f1_score</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">precision</span> <span class="o">*</span> <span class="n">sensitivity</span> <span class="o">/</span> <span class="p">(</span><span class="n">precision</span> <span class="o">+</span> <span class="n">sensitivity</span><span class="p">)</span>
-
-                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{:4.1f}</span><span class="s2">%</span><span class="se">\n</span><span class="si">{:4.1f}</span><span class="s2">% (F)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                    <span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">y</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span>
-                    <span class="n">f1_score</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span>
-                    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="mf">0.1</span><span class="p">),</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-                    <span class="n">color</span><span class="o">=</span><span class="n">textcolor</span><span class="p">,</span>
-                    <span class="n">fontweight</span><span class="o">=</span><span class="n">this_font_weight</span><span class="p">)</span>
-
-    <span class="c1"># Add values for target correctness etc.</span>
-    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">width</span><span class="p">):</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="p">:]))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">annotate_str</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">correctness</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[</span><span class="n">x</span><span class="p">,</span> <span class="p">:]))</span>
-            <span class="n">annotate_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-            <span class="n">annotate_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="si">{:5.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">correctness</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">annotate_str</span><span class="p">,</span>
-                    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">height</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[:,</span> <span class="n">y</span><span class="p">]))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">annotate_str</span> <span class="o">=</span> <span class="s2">&quot;-&quot;</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">correctness</span> <span class="o">=</span> <span class="n">confusion_mat</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="n">y</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">[:,</span> <span class="n">y</span><span class="p">]))</span>
-            <span class="n">annotate_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-            <span class="n">annotate_str</span> <span class="o">+=</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="si">{:5.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">correctness</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">annotate_str</span><span class="p">,</span>
-                    <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">x</span><span class="p">),</span>
-                    <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                    <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
-
-    <span class="n">overall_correctness</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{:5.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">overall_correctness</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span>
-                <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">confusion_mat</span><span class="p">)),</span>
-                <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-                <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-                <span class="n">fontweight</span><span class="o">=</span><span class="s1">&#39;bold&#39;</span><span class="p">)</span>
-
-    <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">width</span><span class="p">),</span> <span class="n">class_names</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">class_names_fontsize</span><span class="p">,</span>
-               <span class="n">rotation</span><span class="o">=</span><span class="n">rotate_col_labels</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="p">),</span> <span class="n">class_names</span><span class="p">,</span>
-               <span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-               <span class="n">fontsize</span><span class="o">=</span><span class="n">class_names_fontsize</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="n">rotate_row_labels</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Predictions&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Targets&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-
-    <span class="c1"># n classes is also shape of matrix/size</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="o">-</span><span class="mf">1.2</span><span class="p">,</span> <span class="n">n_classes</span> <span class="o">+</span> <span class="mf">0.2</span><span class="p">,</span> <span class="s2">&quot;Recall&quot;</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span>
-            <span class="n">fontsize</span><span class="o">=</span><span class="mi">13</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">rotate_precision</span><span class="p">:</span>
-        <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span>
-        <span class="n">x_pos</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.1</span>
-        <span class="n">va</span> <span class="o">=</span> <span class="s1">&#39;center&#39;</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">rotation</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">x_pos</span> <span class="o">=</span> <span class="o">-</span><span class="mf">0.8</span>
-        <span class="n">va</span> <span class="o">=</span> <span class="s1">&#39;top&#39;</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">x_pos</span><span class="p">,</span> <span class="s2">&quot;Precision&quot;</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="n">va</span><span class="p">,</span>
-            <span class="n">rotation</span><span class="o">=</span><span class="n">rotation</span><span class="p">,</span>  <span class="c1"># 270,</span>
-            <span class="n">fontsize</span><span class="o">=</span><span class="mi">13</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">fig</span></div>
-
-
-<span class="c1"># see http://stackoverflow.com/a/31397438/1469195</span>
-<span class="k">def</span> <span class="nf">_cmap_map</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;colormap_mod&#39;</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Modify a colormap using `function` which must operate on 3-element</span>
-<span class="sd">    arrays of [r, g, b] values.</span>
-
-<span class="sd">    You may specify the number of colors, `N`, and the opacity, `gamma`,</span>
-<span class="sd">    value of the returned colormap. These values default to the ones in</span>
-<span class="sd">    the input `cmap`.</span>
-
-<span class="sd">    You may also specify a `name` for the colormap, so that it can be</span>
-<span class="sd">    loaded using plt.get_cmap(name).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">LinearSegmentedColormap</span> <span class="k">as</span> <span class="n">lsc</span>
-    <span class="k">if</span> <span class="n">N</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">N</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">.</span><span class="n">N</span>
-    <span class="k">if</span> <span class="n">gamma</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">gamma</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">.</span><span class="n">_gamma</span>
-    <span class="n">cdict</span> <span class="o">=</span> <span class="n">cmap</span><span class="o">.</span><span class="n">_segmentdata</span>
-    <span class="c1"># Cast the steps into lists:</span>
-    <span class="n">step_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cdict</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">cdict</span><span class="p">}</span>
-    <span class="c1"># Now get the unique steps (first column of the arrays):</span>
-    <span class="n">step_dicts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">step_dict</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-    <span class="n">step_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">step_dicts</span><span class="p">)</span>
-    <span class="c1"># &#39;y0&#39;, &#39;y1&#39; are as defined in LinearSegmentedColormap docstring:</span>
-    <span class="n">y0</span> <span class="o">=</span> <span class="n">cmap</span><span class="p">(</span><span class="n">step_list</span><span class="p">)[:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-    <span class="n">y1</span> <span class="o">=</span> <span class="n">y0</span><span class="o">.</span><span class="n">copy</span><span class="p">()[:,</span> <span class="p">:</span><span class="mi">3</span><span class="p">]</span>
-    <span class="c1"># Go back to catch the discontinuities, and place them into y0, y1</span>
-    <span class="k">for</span> <span class="n">iclr</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;blue&#39;</span><span class="p">]):</span>
-        <span class="k">for</span> <span class="n">istp</span><span class="p">,</span> <span class="n">step</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">step_list</span><span class="p">):</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">ind</span> <span class="o">=</span> <span class="n">step_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">step</span><span class="p">)</span>
-            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-                <span class="c1"># This step is not in this color</span>
-                <span class="k">continue</span>
-            <span class="n">y0</span><span class="p">[</span><span class="n">istp</span><span class="p">,</span> <span class="n">iclr</span><span class="p">]</span> <span class="o">=</span> <span class="n">cdict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">ind</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">y1</span><span class="p">[</span><span class="n">istp</span><span class="p">,</span> <span class="n">iclr</span><span class="p">]</span> <span class="o">=</span> <span class="n">cdict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">ind</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
-    <span class="c1"># Map the colors to their new values:</span>
-    <span class="n">y0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">y0</span><span class="p">)))</span>
-    <span class="n">y1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">y1</span><span class="p">)))</span>
-    <span class="c1"># Build the new colormap (overwriting step_dict):</span>
-    <span class="k">for</span> <span class="n">iclr</span><span class="p">,</span> <span class="n">clr</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">,</span> <span class="s1">&#39;blue&#39;</span><span class="p">]):</span>
-        <span class="n">step_dict</span><span class="p">[</span><span class="n">clr</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">step_list</span><span class="p">,</span> <span class="n">y0</span><span class="p">[:,</span> <span class="n">iclr</span><span class="p">],</span> <span class="n">y1</span><span class="p">[:,</span> <span class="n">iclr</span><span class="p">]))</span><span class="o">.</span><span class="n">T</span>
-    <span class="c1"># Remove alpha, otherwise crashes...</span>
-    <span class="n">step_dict</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">lsc</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">step_dict</span><span class="p">,</span> <span class="n">N</span><span class="o">=</span><span class="n">N</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
-</pre></div>
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-  <h1>Source code for braindecode.visualization.gradients</h1><div class="highlight"><pre>
-<span></span><span class="c1"># Authors: Robin Schirrmeister &lt;robintibor@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">skorch.utils</span> <span class="kn">import</span> <span class="n">to_numpy</span><span class="p">,</span> <span class="n">to_tensor</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-
-
-<div class="viewcode-block" id="compute_amplitude_gradients"><a class="viewcode-back" href="../../../generated/braindecode.visualization.compute_amplitude_gradients.html#braindecode.visualization.compute_amplitude_gradients">[docs]</a><span class="k">def</span> <span class="nf">compute_amplitude_gradients</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
-    <span class="n">loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
-                                         <span class="n">drop_last</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">all_amp_grads</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">batch_X</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">loader</span><span class="p">:</span>
-        <span class="n">this_amp_grads</span> <span class="o">=</span> <span class="n">compute_amplitude_gradients_for_X</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">batch_X</span><span class="p">,</span> <span class="p">)</span>
-        <span class="n">all_amp_grads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_amp_grads</span><span class="p">)</span>
-    <span class="n">all_amp_grads</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">all_amp_grads</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">all_amp_grads</span></div>
-
-
-<span class="k">def</span> <span class="nf">compute_amplitude_gradients_for_X</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-    <span class="n">device</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
-    <span class="n">ffted</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">amps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">ffted</span><span class="p">)</span>
-    <span class="n">phases</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">ffted</span><span class="p">)</span>
-    <span class="n">amps_th</span> <span class="o">=</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">amps</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">phases_th</span> <span class="o">=</span> <span class="n">to_tensor</span><span class="p">(</span><span class="n">phases</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">requires_grad_</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="n">fft_coefs</span> <span class="o">=</span> <span class="n">amps_th</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">phases_th</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">phases_th</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">fft_coefs</span> <span class="o">=</span> <span class="n">fft_coefs</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
-
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">complex_fft_coefs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">view_as_complex</span><span class="p">(</span><span class="n">fft_coefs</span><span class="p">)</span>
-        <span class="n">iffted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span>
-            <span class="n">complex_fft_coefs</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="n">iffted</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span>  <span class="c1"># Deprecated since 1.7</span>
-            <span class="n">fft_coefs</span><span class="p">,</span> <span class="n">signal_ndim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">signal_sizes</span><span class="o">=</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],))</span>
-
-    <span class="n">outs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">iffted</span><span class="p">)</span>
-
-    <span class="n">n_filters</span> <span class="o">=</span> <span class="n">outs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">amp_grads_per_filter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="n">n_filters</span><span class="p">,)</span> <span class="o">+</span> <span class="n">ffted</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
-                                   <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i_filter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_filters</span><span class="p">):</span>
-        <span class="n">mean_out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">outs</span><span class="p">[:,</span> <span class="n">i_filter</span><span class="p">])</span>
-        <span class="n">mean_out</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">retain_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">amp_grads</span> <span class="o">=</span> <span class="n">to_numpy</span><span class="p">(</span><span class="n">amps_th</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">clone</span><span class="p">())</span>
-        <span class="n">amp_grads_per_filter</span><span class="p">[</span><span class="n">i_filter</span><span class="p">]</span> <span class="o">=</span> <span class="n">amp_grads</span>
-        <span class="n">amps_th</span><span class="o">.</span><span class="n">grad</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">amp_grads_per_filter</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">amp_grads_per_filter</span>
-</pre></div>
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-  <h1>All modules for which code is available</h1>
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diff --git a/stable/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/stable/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
index 00000000..eb19f698
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+++ b/stable/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
@@ -0,0 +1 @@
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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem}
diff --git a/stable/_sphinx_design_static/design-tabs.js b/stable/_sphinx_design_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/stable/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/stable/_static/_sphinx_javascript_frameworks_compat.js b/stable/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8549469d..00000000
--- a/stable/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,134 +0,0 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-    if (!x) {
-        return x
-    }
-    return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-    if (typeof s === 'undefined')
-        s = document.location.search;
-    var parts = s.substr(s.indexOf('?') + 1).split('&');
-    var result = {};
-    for (var i = 0; i < parts.length; i++) {
-        var tmp = parts[i].split('=', 2);
-        var key = jQuery.urldecode(tmp[0]);
-        var value = jQuery.urldecode(tmp[1]);
-        if (key in result)
-            result[key].push(value);
-        else
-            result[key] = [value];
-    }
-    return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-    function highlight(node, addItems) {
-        if (node.nodeType === 3) {
-            var val = node.nodeValue;
-            var pos = val.toLowerCase().indexOf(text);
-            if (pos >= 0 &&
-                !jQuery(node.parentNode).hasClass(className) &&
-                !jQuery(node.parentNode).hasClass("nohighlight")) {
-                var span;
-                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-                if (isInSVG) {
-                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-                } else {
-                    span = document.createElement("span");
-                    span.className = className;
-                }
-                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-                    document.createTextNode(val.substr(pos + text.length)),
-                    node.nextSibling));
-                node.nodeValue = val.substr(0, pos);
-                if (isInSVG) {
-                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-                    var bbox = node.parentElement.getBBox();
-                    rect.x.baseVal.value = bbox.x;
-                    rect.y.baseVal.value = bbox.y;
-                    rect.width.baseVal.value = bbox.width;
-                    rect.height.baseVal.value = bbox.height;
-                    rect.setAttribute('class', className);
-                    addItems.push({
-                        "parent": node.parentNode,
-                        "target": rect});
-                }
-            }
-        }
-        else if (!jQuery(node).is("button, select, textarea")) {
-            jQuery.each(node.childNodes, function() {
-                highlight(this, addItems);
-            });
-        }
-    }
-    var addItems = [];
-    var result = this.each(function() {
-        highlight(this, addItems);
-    });
-    for (var i = 0; i < addItems.length; ++i) {
-        jQuery(addItems[i].parent).before(addItems[i].target);
-    }
-    return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-    jQuery.uaMatch = function(ua) {
-        ua = ua.toLowerCase();
-
-        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-            /(msie) ([\w.]+)/.exec(ua) ||
-            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-            [];
-
-        return {
-            browser: match[ 1 ] || "",
-            version: match[ 2 ] || "0"
-        };
-    };
-    jQuery.browser = {};
-    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
diff --git a/stable/_static/basic.css b/stable/_static/basic.css
index c5dde73d..e760386b 100644
--- a/stable/_static/basic.css
+++ b/stable/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -236,17 +236,11 @@ div.body p, div.body dd, div.body li, div.body blockquote {
 a.headerlink {
     visibility: hidden;
 }
-a.brackets:before,
-span.brackets > a:before{
-    content: "[";
-}
 
-a.brackets:after,
-span.brackets > a:after {
-    content: "]";
+a:visited {
+    color: #551A8B;
 }
 
-
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -334,11 +328,17 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
+
+nav.contents,
+aside.topic,
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -377,6 +377,8 @@ div.body p.centered {
 
 div.sidebar > :last-child,
 aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -384,6 +386,8 @@ div.admonition > :last-child {
 
 div.sidebar::after,
 aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -608,19 +612,27 @@ ol.simple p,
 ul.simple p {
     margin-bottom: 0;
 }
-dl.footnote > dt,
-dl.citation > dt {
+
+aside.footnote > span,
+div.citation > span {
     float: left;
-    margin-right: 0.5em;
 }
-
-dl.footnote > dd,
-dl.citation > dd {
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
     margin-bottom: 0em;
 }
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
     content: "";
     clear: both;
 }
@@ -636,10 +648,6 @@ dl.field-list > dt {
     padding-left: 0.5em;
     padding-right: 5px;
 }
-dl.field-list > dt:after {
-    content: ":";
-}
-
 
 dl.field-list > dd {
     padding-left: 0.5em;
@@ -666,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -734,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/stable/_static/braindecode_install.png b/stable/_static/braindecode_install.png
new file mode 100644
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diff --git a/stable/_static/braindecode_symbol.png b/stable/_static/braindecode_symbol.png
new file mode 100644
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diff --git a/stable/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/stable/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
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+++ b/stable/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
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diff --git a/stable/_static/design-tabs.js b/stable/_static/design-tabs.js
new file mode 100644
index 00000000..36b38cf0
--- /dev/null
+++ b/stable/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
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+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
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diff --git a/stable/_static/doctools.js b/stable/_static/doctools.js
index 527b876c..d06a71d7 100644
--- a/stable/_static/doctools.js
+++ b/stable/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
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diff --git a/stable/_static/documentation_options.js b/stable/_static/documentation_options.js
index 0c1bdaca..d40ea2b7 100644
--- a/stable/_static/documentation_options.js
+++ b/stable/_static/documentation_options.js
@@ -1,6 +1,5 @@
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-    VERSION: '0.7',
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diff --git a/stable/_static/jquery-3.6.0.js b/stable/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b..00000000
--- a/stable/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
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diff --git a/stable/_static/jquery.js b/stable/_static/jquery.js
deleted file mode 100644
index c4c6022f..00000000
--- a/stable/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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new file mode 100644
index 00000000..5de36d7f
--- /dev/null
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diff --git a/stable/_static/language_data.js b/stable/_static/language_data.js
index 2e22b06a..250f5665 100644
--- a/stable/_static/language_data.js
+++ b/stable/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/stable/_static/pygments.css b/stable/_static/pygments.css
index 6663ba2d..997797f2 100644
--- a/stable/_static/pygments.css
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 html[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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-html[data-theme="dark"] .highlight { background: #202020; color: #d0d0d0 }
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-html[data-theme="dark"] .highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */
-html[data-theme="dark"] .highlight .esc { color: #d0d0d0 } /* Escape */
-html[data-theme="dark"] .highlight .g { color: #d0d0d0 } /* Generic */
-html[data-theme="dark"] .highlight .k { color: #6ebf26; font-weight: bold } /* Keyword */
-html[data-theme="dark"] .highlight .l { color: #d0d0d0 } /* Literal */
-html[data-theme="dark"] .highlight .n { color: #d0d0d0 } /* Name */
-html[data-theme="dark"] .highlight .o { color: #d0d0d0 } /* Operator */
-html[data-theme="dark"] .highlight .x { color: #d0d0d0 } /* Other */
-html[data-theme="dark"] .highlight .p { color: #d0d0d0 } /* Punctuation */
-html[data-theme="dark"] .highlight .ch { color: #ababab; font-style: italic } /* Comment.Hashbang */
-html[data-theme="dark"] .highlight .cm { color: #ababab; font-style: italic } /* Comment.Multiline */
-html[data-theme="dark"] .highlight .cp { color: #cd2828; font-weight: bold } /* Comment.Preproc */
-html[data-theme="dark"] .highlight .cpf { color: #ababab; font-style: italic } /* Comment.PreprocFile */
-html[data-theme="dark"] .highlight .c1 { color: #ababab; font-style: italic } /* Comment.Single */
-html[data-theme="dark"] .highlight .cs { color: #e50808; font-weight: bold; background-color: #520000 } /* Comment.Special */
-html[data-theme="dark"] .highlight .gd { color: #d22323 } /* Generic.Deleted */
-html[data-theme="dark"] .highlight .ge { color: #d0d0d0; font-style: italic } /* Generic.Emph */
-html[data-theme="dark"] .highlight .gr { color: #d22323 } /* Generic.Error */
-html[data-theme="dark"] .highlight .gh { color: #ffffff; font-weight: bold } /* Generic.Heading */
-html[data-theme="dark"] .highlight .gi { color: #589819 } /* Generic.Inserted */
-html[data-theme="dark"] .highlight .go { color: #cccccc } /* Generic.Output */
-html[data-theme="dark"] .highlight .gp { color: #aaaaaa } /* Generic.Prompt */
-html[data-theme="dark"] .highlight .gs { color: #d0d0d0; font-weight: bold } /* Generic.Strong */
-html[data-theme="dark"] .highlight .gu { color: #ffffff; text-decoration: underline } /* Generic.Subheading */
-html[data-theme="dark"] .highlight .gt { color: #d22323 } /* Generic.Traceback */
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-html[data-theme="dark"] .highlight .kn { color: #6ebf26; font-weight: bold } /* Keyword.Namespace */
-html[data-theme="dark"] .highlight .kp { color: #6ebf26 } /* Keyword.Pseudo */
-html[data-theme="dark"] .highlight .kr { color: #6ebf26; font-weight: bold } /* Keyword.Reserved */
-html[data-theme="dark"] .highlight .kt { color: #6ebf26; font-weight: bold } /* Keyword.Type */
-html[data-theme="dark"] .highlight .ld { color: #d0d0d0 } /* Literal.Date */
-html[data-theme="dark"] .highlight .m { color: #51b2fd } /* Literal.Number */
-html[data-theme="dark"] .highlight .s { color: #ed9d13 } /* Literal.String */
-html[data-theme="dark"] .highlight .na { color: #bbbbbb } /* Name.Attribute */
-html[data-theme="dark"] .highlight .nb { color: #2fbccd } /* Name.Builtin */
-html[data-theme="dark"] .highlight .nc { color: #71adff; text-decoration: underline } /* Name.Class */
-html[data-theme="dark"] .highlight .no { color: #40ffff } /* Name.Constant */
-html[data-theme="dark"] .highlight .nd { color: #ffa500 } /* Name.Decorator */
-html[data-theme="dark"] .highlight .ni { color: #d0d0d0 } /* Name.Entity */
-html[data-theme="dark"] .highlight .ne { color: #bbbbbb } /* Name.Exception */
-html[data-theme="dark"] .highlight .nf { color: #71adff } /* Name.Function */
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-html[data-theme="dark"] .highlight .nx { color: #d0d0d0 } /* Name.Other */
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-html[data-theme="dark"] .highlight .nt { color: #6ebf26; font-weight: bold } /* Name.Tag */
-html[data-theme="dark"] .highlight .nv { color: #40ffff } /* Name.Variable */
-html[data-theme="dark"] .highlight .ow { color: #6ebf26; font-weight: bold } /* Operator.Word */
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-html[data-theme="dark"] .highlight .mb { color: #51b2fd } /* Literal.Number.Bin */
-html[data-theme="dark"] .highlight .mf { color: #51b2fd } /* Literal.Number.Float */
-html[data-theme="dark"] .highlight .mh { color: #51b2fd } /* Literal.Number.Hex */
-html[data-theme="dark"] .highlight .mi { color: #51b2fd } /* Literal.Number.Integer */
-html[data-theme="dark"] .highlight .mo { color: #51b2fd } /* Literal.Number.Oct */
-html[data-theme="dark"] .highlight .sa { color: #ed9d13 } /* Literal.String.Affix */
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-html[data-theme="dark"] .highlight .si { color: #ed9d13 } /* Literal.String.Interpol */
-html[data-theme="dark"] .highlight .sx { color: #ffa500 } /* Literal.String.Other */
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-html[data-theme="dark"] .highlight .s1 { color: #ed9d13 } /* Literal.String.Single */
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\ No newline at end of file
+html[data-theme="dark"] .highlight .hll { background-color: #ffd9002e }
+html[data-theme="dark"] .highlight { background: #2b2b2b; color: #f8f8f2 }
+html[data-theme="dark"] .highlight .c { color: #ffd900 } /* Comment */
+html[data-theme="dark"] .highlight .err { color: #ffa07a } /* Error */
+html[data-theme="dark"] .highlight .k { color: #dcc6e0 } /* Keyword */
+html[data-theme="dark"] .highlight .l { color: #ffd900 } /* Literal */
+html[data-theme="dark"] .highlight .n { color: #f8f8f2 } /* Name */
+html[data-theme="dark"] .highlight .o { color: #abe338 } /* Operator */
+html[data-theme="dark"] .highlight .p { color: #f8f8f2 } /* Punctuation */
+html[data-theme="dark"] .highlight .ch { color: #ffd900 } /* Comment.Hashbang */
+html[data-theme="dark"] .highlight .cm { color: #ffd900 } /* Comment.Multiline */
+html[data-theme="dark"] .highlight .cp { color: #ffd900 } /* Comment.Preproc */
+html[data-theme="dark"] .highlight .cpf { color: #ffd900 } /* Comment.PreprocFile */
+html[data-theme="dark"] .highlight .c1 { color: #ffd900 } /* Comment.Single */
+html[data-theme="dark"] .highlight .cs { color: #ffd900 } /* Comment.Special */
+html[data-theme="dark"] .highlight .gd { color: #00e0e0 } /* Generic.Deleted */
+html[data-theme="dark"] .highlight .ge { font-style: italic } /* Generic.Emph */
+html[data-theme="dark"] .highlight .gh { color: #00e0e0 } /* Generic.Heading */
+html[data-theme="dark"] .highlight .gs { font-weight: bold } /* Generic.Strong */
+html[data-theme="dark"] .highlight .gu { color: #00e0e0 } /* Generic.Subheading */
+html[data-theme="dark"] .highlight .kc { color: #dcc6e0 } /* Keyword.Constant */
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+html[data-theme="dark"] .highlight .kr { color: #dcc6e0 } /* Keyword.Reserved */
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+html[data-theme="dark"] .highlight .ld { color: #ffd900 } /* Literal.Date */
+html[data-theme="dark"] .highlight .m { color: #ffd900 } /* Literal.Number */
+html[data-theme="dark"] .highlight .s { color: #abe338 } /* Literal.String */
+html[data-theme="dark"] .highlight .na { color: #ffd900 } /* Name.Attribute */
+html[data-theme="dark"] .highlight .nb { color: #ffd900 } /* Name.Builtin */
+html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
+html[data-theme="dark"] .highlight .no { color: #00e0e0 } /* Name.Constant */
+html[data-theme="dark"] .highlight .nd { color: #ffd900 } /* Name.Decorator */
+html[data-theme="dark"] .highlight .ni { color: #abe338 } /* Name.Entity */
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+html[data-theme="dark"] .highlight .py { color: #00e0e0 } /* Name.Property */
+html[data-theme="dark"] .highlight .nt { color: #00e0e0 } /* Name.Tag */
+html[data-theme="dark"] .highlight .nv { color: #ffa07a } /* Name.Variable */
+html[data-theme="dark"] .highlight .ow { color: #dcc6e0 } /* Operator.Word */
+html[data-theme="dark"] .highlight .pm { color: #f8f8f2 } /* Punctuation.Marker */
+html[data-theme="dark"] .highlight .w { color: #f8f8f2 } /* Text.Whitespace */
+html[data-theme="dark"] .highlight .mb { color: #ffd900 } /* Literal.Number.Bin */
+html[data-theme="dark"] .highlight .mf { color: #ffd900 } /* Literal.Number.Float */
+html[data-theme="dark"] .highlight .mh { color: #ffd900 } /* Literal.Number.Hex */
+html[data-theme="dark"] .highlight .mi { color: #ffd900 } /* Literal.Number.Integer */
+html[data-theme="dark"] .highlight .mo { color: #ffd900 } /* Literal.Number.Oct */
+html[data-theme="dark"] .highlight .sa { color: #abe338 } /* Literal.String.Affix */
+html[data-theme="dark"] .highlight .sb { color: #abe338 } /* Literal.String.Backtick */
+html[data-theme="dark"] .highlight .sc { color: #abe338 } /* Literal.String.Char */
+html[data-theme="dark"] .highlight .dl { color: #abe338 } /* Literal.String.Delimiter */
+html[data-theme="dark"] .highlight .sd { color: #abe338 } /* Literal.String.Doc */
+html[data-theme="dark"] .highlight .s2 { color: #abe338 } /* Literal.String.Double */
+html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
+html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
+html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
+html[data-theme="dark"] .highlight .il { color: #ffd900 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/stable/_static/scripts/bootstrap.js b/stable/_static/scripts/bootstrap.js
new file mode 100644
index 00000000..bda8a602
--- /dev/null
+++ b/stable/_static/scripts/bootstrap.js
@@ -0,0 +1,3 @@
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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi,ui),this._element.style[e]="",ue.trigger(this._element,li)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(ue.trigger(this._element,ci).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,Rt(this._element),this._element.classList.add(pi),this._element.classList.remove(fi,ui);for(const t of this._triggerArray){const e=ye.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(pi),this._element.classList.add(fi),ue.trigger(this._element,hi)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(ui)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(gi);for(const e of t){const t=ye.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ye.find(mi,this._config.parent);return ye.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=vi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}ue.on(document,di,gi,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of ye.getMultipleElementsFromSelector(this))vi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Kt(vi);const yi="dropdown",wi=".bs.dropdown",Ei=".data-api",Ai="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ei}`,Si=`keydown${wi}${Ei}`,Di=`keyup${wi}${Ei}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=Yt()?"top-end":"top-start",ji=Yt()?"top-start":"top-end",Fi=Yt()?"bottom-end":"bottom-start",Hi=Yt()?"bottom-start":"bottom-end",Bi=Yt()?"left-start":"right-start",Wi=Yt()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends be{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ye.next(this._element,Pi)[0]||ye.prev(this._element,Pi)[0]||ye.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Bt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!ue.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),ue.trigger(this._element,ki,t)}}hide(){if(Bt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!ue.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),ge.removeDataAttribute(this._menu,"popper"),ue.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!jt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:jt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=St(t,this._menu,i)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Bi;if(t.classList.contains("dropstart"))return Wi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ge.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...Qt(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=ye.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Ht(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ye.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ai,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:ye.prev(this,Ii)[0]||ye.next(this,Ii)[0]||ye.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}ue.on(document,Si,Ii,qi.dataApiKeydownHandler),ue.on(document,Si,Pi,qi.dataApiKeydownHandler),ue.on(document,Li,qi.clearMenus),ue.on(document,Di,qi.clearMenus),ue.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),Kt(qi);const Vi="backdrop",Yi="show",Ki=`mousedown.bs.${Vi}`,Qi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Xi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Qi}static get DefaultType(){return Xi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void Qt(t);this._append();const e=this._getElement();this._config.isAnimated&&Rt(e),e.classList.add(Yi),this._emulateAnimation((()=>{Qt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Yi),this._emulateAnimation((()=>{this.dispose(),Qt(t)}))):Qt(t)}dispose(){this._isAppended&&(ue.off(this._element,Ki),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),ue.on(t,Ki,(()=>{Qt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Xt(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends _e{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),ue.off(document,Gi),ue.on(document,Ji,(t=>this._handleFocusin(t))),ue.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,ue.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ye.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ge.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=ge.getDataAttribute(t,e);null!==i?(ge.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(jt(t))e(t);else for(const i of ye.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",En="show",An="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends be{constructor(t,e){super(t,e),this._dialog=ye.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||ue.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(ue.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){ue.off(window,hn),ue.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ye.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Rt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,ue.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){ue.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),ue.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),ue.on(this._element,bn,(t=>{ue.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),ue.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(ue.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(An)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(An),this._queueCallback((()=>{this._element.classList.remove(An),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Yt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Yt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}ue.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=ye.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),ue.one(e,pn,(t=>{t.defaultPrevented||ue.one(e,fn,(()=>{Ht(this)&&this.focus()}))}));const i=ye.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),we(On),Kt(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Bn=`click${xn}${kn}`,Wn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends be{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||ue.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),ue.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(ue.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),ue.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():ue.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){ue.on(this._element,Wn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():ue.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}ue.on(document,Bn,'[data-bs-toggle="offcanvas"]',(function(t){const e=ye.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this))return;ue.one(e,Fn,(()=>{Ht(this)&&this.focus()}));const i=ye.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),ue.on(window,Ln,(()=>{for(const t of ye.find(In))qn.getOrCreateInstance(t).show()})),ue.on(window,Hn,(()=>{for(const t of ye.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),we(qn),Kt(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Yn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Kn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Qn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Yn.has(i)||Boolean(Kn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Xn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends _e{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Xn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=ye.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?jt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Qn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return Qt(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:Yt()?"left":"right",BOTTOM:"bottom",LEFT:Yt()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends be{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),ue.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=ue.trigger(this._element,this.constructor.eventName("show")),e=(Wt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),ue.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._queueCallback((()=>{ue.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!ue.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),ue.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=Qt(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return St(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return Qt(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...Qt(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)ue.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");ue.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),ue.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},ue.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ge.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Es={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class As extends be{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return Es}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=Ft(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(ue.off(this._config.target,ms),ue.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ye.find(bs,this._config.target);for(const e of t){if(!e.hash||Bt(e))continue;const t=ye.findOne(decodeURI(e.hash),this._element);Ht(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),ue.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ye.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of ye.parents(t,".nav, .list-group"))for(const t of ye.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=ye.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=As.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(window,gs,(()=>{for(const t of ye.find('[data-bs-spy="scroll"]'))As.getOrCreateInstance(t)})),Kt(As);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Bs="show",Ws=".dropdown-toggle",zs=`:not(${Ws})`,Rs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${zs}, .list-group-item${zs}, [role="tab"]${zs}, ${Rs}`,Vs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Ys extends be{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),ue.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?ue.trigger(e,Cs,{relatedTarget:t}):null;ue.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),ue.trigger(t,ks,{relatedTarget:e})):t.classList.add(Bs)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(ye.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),ue.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Bs)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!Bt(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=Ut(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ys.getOrCreateInstance(i).show())}_getChildren(){return ye.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=ye.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ye.findOne(t,i);s&&s.classList.toggle(n,e)};n(Ws,Fs),n(".dropdown-menu",Bs),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(qs)?t:ye.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Ys.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(document,Ls,Rs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Bt(this)||Ys.getOrCreateInstance(this).show()})),ue.on(window,Ds,(()=>{for(const t of ye.find(Vs))Ys.getOrCreateInstance(t)})),Kt(Ys);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Us=`focusin${Ks}`,Gs=`focusout${Ks}`,Js=`hide${Ks}`,Zs=`hidden${Ks}`,to=`show${Ks}`,eo=`shown${Ks}`,io="hide",no="show",so="showing",oo={animation:"boolean",autohide:"boolean",delay:"number"},ro={animation:!0,autohide:!0,delay:5e3};class ao extends be{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ro}static get DefaultType(){return oo}static get NAME(){return"toast"}show(){ue.trigger(this._element,to).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(io),Rt(this._element),this._element.classList.add(no,so),this._queueCallback((()=>{this._element.classList.remove(so),ue.trigger(this._element,eo),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(ue.trigger(this._element,Js).defaultPrevented||(this._element.classList.add(so),this._queueCallback((()=>{this._element.classList.add(io),this._element.classList.remove(so,no),ue.trigger(this._element,Zs)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(no),super.dispose()}isShown(){return this._element.classList.contains(no)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){ue.on(this._element,Qs,(t=>this._onInteraction(t,!0))),ue.on(this._element,Xs,(t=>this._onInteraction(t,!1))),ue.on(this._element,Us,(t=>this._onInteraction(t,!0))),ue.on(this._element,Gs,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ao.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}function lo(t){"loading"!=document.readyState?t():document.addEventListener("DOMContentLoaded",t)}we(ao),Kt(ao),lo((function(){[].slice.call(document.querySelectorAll('[data-bs-toggle="tooltip"]')).map((function(t){return new cs(t,{delay:{show:500,hide:100}})}))})),lo((function(){document.getElementById("pst-back-to-top").addEventListener("click",(function(){document.body.scrollTop=0,document.documentElement.scrollTop=0}))})),lo((function(){var t=document.getElementById("pst-back-to-top"),e=document.getElementsByClassName("bd-header")[0].getBoundingClientRect();window.addEventListener("scroll",(function(){this.oldScroll>this.scrollY&&this.scrollY>e.bottom?t.style.display="block":t.style.display="none",this.oldScroll=this.scrollY}))}))})();
+//# sourceMappingURL=bootstrap.js.map
\ No newline at end of file
diff --git a/stable/_static/scripts/bootstrap.js.LICENSE.txt b/stable/_static/scripts/bootstrap.js.LICENSE.txt
new file mode 100644
index 00000000..10f979d0
--- /dev/null
+++ b/stable/_static/scripts/bootstrap.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+  * Bootstrap v5.3.2 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
diff --git a/stable/_static/scripts/bootstrap.js.map b/stable/_static/scripts/bootstrap.js.map
new file mode 100644
index 00000000..e5bc1575
--- /dev/null
+++ b/stable/_static/scripts/bootstrap.js.map
@@ -0,0 +1 @@
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(element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n  * Bootstrap v5.3.2 (https://getbootstrap.com/)\n  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n  */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map();\nconst Data = {\n  set(element, key, instance) {\n    if (!elementMap.has(element)) {\n      elementMap.set(element, new Map());\n    }\n    const instanceMap = elementMap.get(element);\n\n    // make it clear we only want one instance per element\n    // can be removed later when multiple key/instances are fine to be used\n    if (!instanceMap.has(key) && instanceMap.size !== 0) {\n      // eslint-disable-next-line no-console\n      console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n      return;\n    }\n    instanceMap.set(key, instance);\n  },\n  get(element, key) {\n    if (elementMap.has(element)) {\n      return elementMap.get(element).get(key) || null;\n    }\n    return null;\n  },\n  remove(element, key) {\n    if (!elementMap.has(element)) {\n      return;\n    }\n    const instanceMap = elementMap.get(element);\n    instanceMap.delete(key);\n\n    // free up element references if there are no instances left for an element\n    if (instanceMap.size === 0) {\n      elementMap.delete(element);\n    }\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend';\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n  if (selector && window.CSS && window.CSS.escape) {\n    // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n    selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`);\n  }\n  return selector;\n};\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n  if (object === null || object === undefined) {\n    return `${object}`;\n  }\n  return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n  do {\n    prefix += Math.floor(Math.random() * MAX_UID);\n  } while (document.getElementById(prefix));\n  return prefix;\n};\nconst getTransitionDurationFromElement = element => {\n  if (!element) {\n    return 0;\n  }\n\n  // Get transition-duration of the element\n  let {\n    transitionDuration,\n    transitionDelay\n  } = window.getComputedStyle(element);\n  const floatTransitionDuration = Number.parseFloat(transitionDuration);\n  const floatTransitionDelay = Number.parseFloat(transitionDelay);\n\n  // Return 0 if element or transition duration is not found\n  if (!floatTransitionDuration && !floatTransitionDelay) {\n    return 0;\n  }\n\n  // If multiple durations are defined, take the first\n  transitionDuration = transitionDuration.split(',')[0];\n  transitionDelay = transitionDelay.split(',')[0];\n  return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\nconst triggerTransitionEnd = element => {\n  element.dispatchEvent(new Event(TRANSITION_END));\n};\nconst isElement = object => {\n  if (!object || typeof object !== 'object') {\n    return false;\n  }\n  if (typeof object.jquery !== 'undefined') {\n    object = object[0];\n  }\n  return typeof object.nodeType !== 'undefined';\n};\nconst getElement = object => {\n  // it's a jQuery object or a node element\n  if (isElement(object)) {\n    return object.jquery ? object[0] : object;\n  }\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object));\n  }\n  return null;\n};\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false;\n  }\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible';\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])');\n  if (!closedDetails) {\n    return elementIsVisible;\n  }\n  if (closedDetails !== element) {\n    const summary = element.closest('summary');\n    if (summary && summary.parentNode !== closedDetails) {\n      return false;\n    }\n    if (summary === null) {\n      return false;\n    }\n  }\n  return elementIsVisible;\n};\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true;\n  }\n  if (element.classList.contains('disabled')) {\n    return true;\n  }\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled;\n  }\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null;\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode();\n    return root instanceof ShadowRoot ? root : null;\n  }\n  if (element instanceof ShadowRoot) {\n    return element;\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null;\n  }\n  return findShadowRoot(element.parentNode);\n};\nconst noop = () => {};\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery;\n  }\n  return null;\n};\nconst DOMContentLoadedCallbacks = [];\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback();\n        }\n      });\n    }\n    DOMContentLoadedCallbacks.push(callback);\n  } else {\n    callback();\n  }\n};\nconst isRTL = () => document.documentElement.dir === 'rtl';\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery();\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME;\n      const JQUERY_NO_CONFLICT = $.fn[name];\n      $.fn[name] = plugin.jQueryInterface;\n      $.fn[name].Constructor = plugin;\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT;\n        return plugin.jQueryInterface;\n      };\n    }\n  });\n};\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue;\n};\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback);\n    return;\n  }\n  const durationPadding = 5;\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n  let called = false;\n  const handler = ({\n    target\n  }) => {\n    if (target !== transitionElement) {\n      return;\n    }\n    called = true;\n    transitionElement.removeEventListener(TRANSITION_END, handler);\n    execute(callback);\n  };\n  transitionElement.addEventListener(TRANSITION_END, handler);\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement);\n    }\n  }, emulatedDuration);\n};\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length;\n  let index = list.indexOf(activeElement);\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n  }\n  index += shouldGetNext ? 1 : -1;\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength;\n  }\n  return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\nlet uidEvent = 1;\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element);\n  element.uidEvent = uid;\n  eventRegistry[uid] = eventRegistry[uid] || {};\n  return eventRegistry[uid];\n}\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, {\n      delegateTarget: element\n    });\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn);\n    }\n    return fn.apply(element, [event]);\n  };\n}\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector);\n    for (let {\n      target\n    } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue;\n        }\n        hydrateObj(event, {\n          delegateTarget: target\n        });\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn);\n        }\n        return fn.apply(target, [event]);\n      }\n    }\n  };\n}\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string';\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n  let typeEvent = getTypeEvent(originalTypeEvent);\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent;\n  }\n  return [isDelegated, callable, typeEvent];\n}\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return;\n  }\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n          return fn.call(this, event);\n        }\n      };\n    };\n    callable = wrapFunction(callable);\n  }\n  const events = getElementEvents(element);\n  const handlers = events[typeEvent] || (events[typeEvent] = {});\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff;\n    return;\n  }\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n  const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n  fn.delegationSelector = isDelegated ? handler : null;\n  fn.callable = callable;\n  fn.oneOff = oneOff;\n  fn.uidEvent = uid;\n  handlers[uid] = fn;\n  element.addEventListener(typeEvent, fn, isDelegated);\n}\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector);\n  if (!fn) {\n    return;\n  }\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n  delete events[typeEvent][fn.uidEvent];\n}\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {};\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n    }\n  }\n}\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '');\n  return customEvents[event] || event;\n}\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false);\n  },\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true);\n  },\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return;\n    }\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n    const inNamespace = typeEvent !== originalTypeEvent;\n    const events = getElementEvents(element);\n    const storeElementEvent = events[typeEvent] || {};\n    const isNamespace = originalTypeEvent.startsWith('.');\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return;\n      }\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n      return;\n    }\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n      }\n    }\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '');\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n      }\n    }\n  },\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null;\n    }\n    const $ = getjQuery();\n    const typeEvent = getTypeEvent(event);\n    const inNamespace = event !== typeEvent;\n    let jQueryEvent = null;\n    let bubbles = true;\n    let nativeDispatch = true;\n    let defaultPrevented = false;\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args);\n      $(element).trigger(jQueryEvent);\n      bubbles = !jQueryEvent.isPropagationStopped();\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n      defaultPrevented = jQueryEvent.isDefaultPrevented();\n    }\n    const evt = hydrateObj(new Event(event, {\n      bubbles,\n      cancelable: true\n    }), args);\n    if (defaultPrevented) {\n      evt.preventDefault();\n    }\n    if (nativeDispatch) {\n      element.dispatchEvent(evt);\n    }\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault();\n    }\n    return evt;\n  }\n};\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value;\n    } catch (_unused) {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value;\n        }\n      });\n    }\n  }\n  return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true;\n  }\n  if (value === 'false') {\n    return false;\n  }\n  if (value === Number(value).toString()) {\n    return Number(value);\n  }\n  if (value === '' || value === 'null') {\n    return null;\n  }\n  if (typeof value !== 'string') {\n    return value;\n  }\n  try {\n    return JSON.parse(decodeURIComponent(value));\n  } catch (_unused) {\n    return value;\n  }\n}\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n  },\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n  },\n  getDataAttributes(element) {\n    if (!element) {\n      return {};\n    }\n    const attributes = {};\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '');\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n      attributes[pureKey] = normalizeData(element.dataset[key]);\n    }\n    return attributes;\n  },\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {};\n  }\n  static get DefaultType() {\n    return {};\n  }\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!');\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    return config;\n  }\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    };\n  }\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property];\n      const valueType = isElement(value) ? 'element' : toType(value);\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n      }\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.2';\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super();\n    element = getElement(element);\n    if (!element) {\n      return;\n    }\n    this._element = element;\n    this._config = this._getConfig(config);\n    Data.set(this._element, this.constructor.DATA_KEY, this);\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY);\n    EventHandler.off(this._element, this.constructor.EVENT_KEY);\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null;\n    }\n  }\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated);\n  }\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY);\n  }\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n  }\n  static get VERSION() {\n    return VERSION;\n  }\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`;\n  }\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`;\n  }\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target');\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href');\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n      return null;\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n    }\n    selector = hrefAttribute && hrefAttribute !== '#' ? parseSelector(hrefAttribute.trim()) : null;\n  }\n  return selector;\n};\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n  },\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector);\n  },\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector));\n  },\n  parents(element, selector) {\n    const parents = [];\n    let ancestor = element.parentNode.closest(selector);\n    while (ancestor) {\n      parents.push(ancestor);\n      ancestor = ancestor.parentNode.closest(selector);\n    }\n    return parents;\n  },\n  prev(element, selector) {\n    let previous = element.previousElementSibling;\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous];\n      }\n      previous = previous.previousElementSibling;\n    }\n    return [];\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling;\n    while (next) {\n      if (next.matches(selector)) {\n        return [next];\n      }\n      next = next.nextElementSibling;\n    }\n    return [];\n  },\n  focusableChildren(element) {\n    const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n  },\n  getSelectorFromElement(element) {\n    const selector = getSelector(element);\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null;\n    }\n    return null;\n  },\n  getElementFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.findOne(selector) : null;\n  },\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element);\n    return selector ? SelectorEngine.find(selector) : [];\n  }\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n  const name = component.NAME;\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault();\n    }\n    if (isDisabled(this)) {\n      return;\n    }\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`);\n    const instance = component.getOrCreateInstance(target);\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]();\n  });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$f;\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n    if (closeEvent.defaultPrevented) {\n      return;\n    }\n    this._element.classList.remove(CLASS_NAME_SHOW$8);\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove();\n    EventHandler.trigger(this._element, EVENT_CLOSED);\n    this.dispose();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close');\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME$e;\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this);\n      if (config === 'toggle') {\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n  event.preventDefault();\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n  const data = Button.getOrCreateInstance(button);\n  data.toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n};\nconst DefaultType$c = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n};\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super();\n    this._element = element;\n    if (!element || !Swipe.isSupported()) {\n      return;\n    }\n    this._config = this._getConfig(config);\n    this._deltaX = 0;\n    this._supportPointerEvents = Boolean(window.PointerEvent);\n    this._initEvents();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$c;\n  }\n  static get DefaultType() {\n    return DefaultType$c;\n  }\n  static get NAME() {\n    return NAME$d;\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY$9);\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX;\n      return;\n    }\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX;\n    }\n  }\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX;\n    }\n    this._handleSwipe();\n    execute(this._config.endCallback);\n  }\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n  }\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX);\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return;\n    }\n    const direction = absDeltaX / this._deltaX;\n    this._deltaX = 0;\n    if (!direction) {\n      return;\n    }\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n  }\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n    }\n  }\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n};\nconst DefaultType$b = {\n  interval: '(number|boolean)',\n  // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._interval = null;\n    this._activeElement = null;\n    this._isSliding = false;\n    this.touchTimeout = null;\n    this._swipeHelper = null;\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n    this._addEventListeners();\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$b;\n  }\n  static get DefaultType() {\n    return DefaultType$b;\n  }\n  static get NAME() {\n    return NAME$c;\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT);\n  }\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next();\n    }\n  }\n  prev() {\n    this._slide(ORDER_PREV);\n  }\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element);\n    }\n    this._clearInterval();\n  }\n  cycle() {\n    this._clearInterval();\n    this._updateInterval();\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n  }\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n      return;\n    }\n    this.cycle();\n  }\n  to(index) {\n    const items = this._getItems();\n    if (index > items.length - 1 || index < 0) {\n      return;\n    }\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n      return;\n    }\n    const activeIndex = this._getItemIndex(this._getActive());\n    if (activeIndex === index) {\n      return;\n    }\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n    this._slide(order, items[index]);\n  }\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose();\n    }\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval;\n    return config;\n  }\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n    }\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n      EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n    }\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners();\n    }\n  }\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n    }\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return;\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause();\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout);\n      }\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n    };\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    };\n    this._swipeHelper = new Swipe(this._element, swipeConfig);\n  }\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return;\n    }\n    const direction = KEY_TO_DIRECTION[event.key];\n    if (direction) {\n      event.preventDefault();\n      this._slide(this._directionToOrder(direction));\n    }\n  }\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element);\n  }\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return;\n    }\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n    activeIndicator.removeAttribute('aria-current');\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n      newActiveIndicator.setAttribute('aria-current', 'true');\n    }\n  }\n  _updateInterval() {\n    const element = this._activeElement || this._getActive();\n    if (!element) {\n      return;\n    }\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n    this._config.interval = elementInterval || this._config.defaultInterval;\n  }\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return;\n    }\n    const activeElement = this._getActive();\n    const isNext = order === ORDER_NEXT;\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n    if (nextElement === activeElement) {\n      return;\n    }\n    const nextElementIndex = this._getItemIndex(nextElement);\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      });\n    };\n    const slideEvent = triggerEvent(EVENT_SLIDE);\n    if (slideEvent.defaultPrevented) {\n      return;\n    }\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return;\n    }\n    const isCycling = Boolean(this._interval);\n    this.pause();\n    this._isSliding = true;\n    this._setActiveIndicatorElement(nextElementIndex);\n    this._activeElement = nextElement;\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n    nextElement.classList.add(orderClassName);\n    reflow(nextElement);\n    activeElement.classList.add(directionalClassName);\n    nextElement.classList.add(directionalClassName);\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName);\n      nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n      activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n      this._isSliding = false;\n      triggerEvent(EVENT_SLID);\n    };\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n    if (isCycling) {\n      this.cycle();\n    }\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE);\n  }\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n  }\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element);\n  }\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval);\n      this._interval = null;\n    }\n  }\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n    }\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n  }\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n    }\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config);\n      if (typeof config === 'number') {\n        data.to(config);\n        return;\n      }\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return;\n  }\n  event.preventDefault();\n  const carousel = Carousel.getOrCreateInstance(target);\n  const slideIndex = this.getAttribute('data-bs-slide-to');\n  if (slideIndex) {\n    carousel.to(slideIndex);\n    carousel._maybeEnableCycle();\n    return;\n  }\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next();\n    carousel._maybeEnableCycle();\n    return;\n  }\n  carousel.prev();\n  carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n  parent: null,\n  toggle: true\n};\nconst DefaultType$a = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isTransitioning = false;\n    this._triggerArray = [];\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem);\n      const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem);\n      }\n    }\n    this._initializeChildren();\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n    }\n    if (this._config.toggle) {\n      this.toggle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$a;\n  }\n  static get DefaultType() {\n    return DefaultType$a;\n  }\n  static get NAME() {\n    return NAME$b;\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide();\n    } else {\n      this.show();\n    }\n  }\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return;\n    }\n    let activeChildren = [];\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n        toggle: false\n      }));\n    }\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide();\n    }\n    const dimension = this._getDimension();\n    this._element.classList.remove(CLASS_NAME_COLLAPSE);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.style[dimension] = 0;\n    this._addAriaAndCollapsedClass(this._triggerArray, true);\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n      this._element.style[dimension] = '';\n      EventHandler.trigger(this._element, EVENT_SHOWN$6);\n    };\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n    const scrollSize = `scroll${capitalizedDimension}`;\n    this._queueCallback(complete, this._element, true);\n    this._element.style[dimension] = `${this._element[scrollSize]}px`;\n  }\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return;\n    }\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n    if (startEvent.defaultPrevented) {\n      return;\n    }\n    const dimension = this._getDimension();\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_COLLAPSING);\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger);\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false);\n      }\n    }\n    this._isTransitioning = true;\n    const complete = () => {\n      this._isTransitioning = false;\n      this._element.classList.remove(CLASS_NAME_COLLAPSING);\n      this._element.classList.add(CLASS_NAME_COLLAPSE);\n      EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n    };\n    this._element.style[dimension] = '';\n    this._queueCallback(complete, this._element, true);\n  }\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW$7);\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle); // Coerce string values\n    config.parent = getElement(config.parent);\n    return config;\n  }\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n  }\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return;\n    }\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element);\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected));\n      }\n    }\n  }\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent);\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n  }\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return;\n    }\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n      element.setAttribute('aria-expanded', isOpen);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {};\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false;\n    }\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config]();\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n    event.preventDefault();\n  }\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, {\n      toggle: false\n    }).toggle();\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n};\nconst DefaultType$9 = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n};\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._popper = null;\n    this._parent = this._element.parentNode; // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n    this._inNavbar = this._detectNavbar();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$9;\n  }\n  static get DefaultType() {\n    return DefaultType$9;\n  }\n  static get NAME() {\n    return NAME$a;\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show();\n  }\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._createPopper();\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    this._element.focus();\n    this._element.setAttribute('aria-expanded', true);\n    this._menu.classList.add(CLASS_NAME_SHOW$6);\n    this._element.classList.add(CLASS_NAME_SHOW$6);\n    EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n  }\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return;\n    }\n    const relatedTarget = {\n      relatedTarget: this._element\n    };\n    this._completeHide(relatedTarget);\n  }\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    super.dispose();\n  }\n  update() {\n    this._inNavbar = this._detectNavbar();\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    if (this._popper) {\n      this._popper.destroy();\n    }\n    this._menu.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.classList.remove(CLASS_NAME_SHOW$6);\n    this._element.setAttribute('aria-expanded', 'false');\n    Manipulator.removeDataAttribute(this._menu, 'popper');\n    EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n  }\n  _getConfig(config) {\n    config = super._getConfig(config);\n    if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n    }\n    return config;\n  }\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n    }\n    let referenceElement = this._element;\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent;\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference);\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference;\n    }\n    const popperConfig = this._getPopperConfig();\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n  }\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n  }\n  _getPlacement() {\n    const parentDropdown = this._parent;\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER;\n    }\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER;\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n    }\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n  }\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null;\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    };\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }];\n    }\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _selectMenuItem({\n    key,\n    target\n  }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n    if (!items.length) {\n      return;\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n      return;\n    }\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle);\n      if (!context || context._config.autoClose === false) {\n        continue;\n      }\n      const composedPath = event.composedPath();\n      const isMenuTarget = composedPath.includes(context._menu);\n      if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n        continue;\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue;\n      }\n      const relatedTarget = {\n        relatedTarget: context._element\n      };\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event;\n      }\n      context._completeHide(relatedTarget);\n    }\n  }\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName);\n    const isEscapeEvent = event.key === ESCAPE_KEY$2;\n    const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return;\n    }\n    if (isInput && !isEscapeEvent) {\n      return;\n    }\n    event.preventDefault();\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n    const instance = Dropdown.getOrCreateInstance(getToggleButton);\n    if (isUpOrDownEvent) {\n      event.stopPropagation();\n      instance.show();\n      instance._selectMenuItem(event);\n      return;\n    }\n    if (instance._isShown()) {\n      // else is escape and we check if it is shown\n      event.stopPropagation();\n      instance.hide();\n      getToggleButton.focus();\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n  event.preventDefault();\n  Dropdown.getOrCreateInstance(this).toggle();\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true,\n  // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n};\n\nconst DefaultType$8 = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n};\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isAppended = false;\n    this._element = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$8;\n  }\n  static get DefaultType() {\n    return DefaultType$8;\n  }\n  static get NAME() {\n    return NAME$9;\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._append();\n    const element = this._getElement();\n    if (this._config.isAnimated) {\n      reflow(element);\n    }\n    element.classList.add(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      execute(callback);\n    });\n  }\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback);\n      return;\n    }\n    this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n    this._emulateAnimation(() => {\n      this.dispose();\n      execute(callback);\n    });\n  }\n  dispose() {\n    if (!this._isAppended) {\n      return;\n    }\n    EventHandler.off(this._element, EVENT_MOUSEDOWN);\n    this._element.remove();\n    this._isAppended = false;\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div');\n      backdrop.className = this._config.className;\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE$4);\n      }\n      this._element = backdrop;\n    }\n    return this._element;\n  }\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement);\n    return config;\n  }\n  _append() {\n    if (this._isAppended) {\n      return;\n    }\n    const element = this._getElement();\n    this._config.rootElement.append(element);\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback);\n    });\n    this._isAppended = true;\n  }\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n};\n\nconst DefaultType$7 = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n};\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n    this._isActive = false;\n    this._lastTabNavDirection = null;\n  }\n\n  // Getters\n  static get Default() {\n    return Default$7;\n  }\n  static get DefaultType() {\n    return DefaultType$7;\n  }\n  static get NAME() {\n    return NAME$8;\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return;\n    }\n    if (this._config.autofocus) {\n      this._config.trapElement.focus();\n    }\n    EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n    this._isActive = true;\n  }\n  deactivate() {\n    if (!this._isActive) {\n      return;\n    }\n    this._isActive = false;\n    EventHandler.off(document, EVENT_KEY$5);\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const {\n      trapElement\n    } = this._config;\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return;\n    }\n    const elements = SelectorEngine.focusableChildren(trapElement);\n    if (elements.length === 0) {\n      trapElement.focus();\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus();\n    } else {\n      elements[0].focus();\n    }\n  }\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return;\n    }\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body;\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth;\n    return Math.abs(window.innerWidth - documentWidth);\n  }\n  hide() {\n    const width = this.getWidth();\n    this._disableOverFlow();\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n  }\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow');\n    this._resetElementAttributes(this._element, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n  }\n  isOverflowing() {\n    return this.getWidth() > 0;\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow');\n    this._element.style.overflow = 'hidden';\n  }\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth();\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return;\n      }\n      this._saveInitialAttribute(element, styleProperty);\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty);\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue);\n    }\n  }\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty);\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty);\n        return;\n      }\n      Manipulator.removeDataAttribute(element, styleProperty);\n      element.style.setProperty(styleProperty, value);\n    };\n    this._applyManipulationCallback(selector, manipulationCallBack);\n  }\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector);\n      return;\n    }\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel);\n    }\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n};\nconst DefaultType$6 = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._isShown = false;\n    this._isTransitioning = false;\n    this._scrollBar = new ScrollBarHelper();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$6;\n  }\n  static get DefaultType() {\n    return DefaultType$6;\n  }\n  static get NAME() {\n    return NAME$7;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._isTransitioning = true;\n    this._scrollBar.hide();\n    document.body.classList.add(CLASS_NAME_OPEN);\n    this._adjustDialog();\n    this._backdrop.show(() => this._showElement(relatedTarget));\n  }\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = false;\n    this._isTransitioning = true;\n    this._focustrap.deactivate();\n    this._element.classList.remove(CLASS_NAME_SHOW$4);\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n  }\n  dispose() {\n    EventHandler.off(window, EVENT_KEY$4);\n    EventHandler.off(this._dialog, EVENT_KEY$4);\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n  handleUpdate() {\n    this._adjustDialog();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop),\n      // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element);\n    }\n    this._element.style.display = 'block';\n    this._element.removeAttribute('aria-hidden');\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.scrollTop = 0;\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n    if (modalBody) {\n      modalBody.scrollTop = 0;\n    }\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW$4);\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate();\n      }\n      this._isTransitioning = false;\n      EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n      if (event.key !== ESCAPE_KEY$1) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      this._triggerBackdropTransition();\n    });\n    EventHandler.on(window, EVENT_RESIZE$1, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog();\n      }\n    });\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return;\n        }\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition();\n          return;\n        }\n        if (this._config.backdrop) {\n          this.hide();\n        }\n      });\n    });\n  }\n  _hideModal() {\n    this._element.style.display = 'none';\n    this._element.setAttribute('aria-hidden', true);\n    this._element.removeAttribute('aria-modal');\n    this._element.removeAttribute('role');\n    this._isTransitioning = false;\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN);\n      this._resetAdjustments();\n      this._scrollBar.reset();\n      EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n    });\n  }\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE$3);\n  }\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const initialOverflowY = this._element.style.overflowY;\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return;\n    }\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden';\n    }\n    this._element.classList.add(CLASS_NAME_STATIC);\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC);\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY;\n      }, this._dialog);\n    }, this._dialog);\n    this._element.focus();\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n    const scrollbarWidth = this._scrollBar.getWidth();\n    const isBodyOverflowing = scrollbarWidth > 0;\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n      this._element.style[property] = `${scrollbarWidth}px`;\n    }\n  }\n  _resetAdjustments() {\n    this._element.style.paddingLeft = '';\n    this._element.style.paddingRight = '';\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](relatedTarget);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return;\n    }\n    EventHandler.one(target, EVENT_HIDDEN$4, () => {\n      if (isVisible(this)) {\n        this.focus();\n      }\n    });\n  });\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide();\n  }\n  const data = Modal.getOrCreateInstance(target);\n  data.toggle(this);\n});\nenableDismissTrigger(Modal);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n};\nconst DefaultType$5 = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n};\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._isShown = false;\n    this._backdrop = this._initializeBackDrop();\n    this._focustrap = this._initializeFocusTrap();\n    this._addEventListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default$5;\n  }\n  static get DefaultType() {\n    return DefaultType$5;\n  }\n  static get NAME() {\n    return NAME$6;\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget);\n  }\n  show(relatedTarget) {\n    if (this._isShown) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n      relatedTarget\n    });\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._isShown = true;\n    this._backdrop.show();\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide();\n    }\n    this._element.setAttribute('aria-modal', true);\n    this._element.setAttribute('role', 'dialog');\n    this._element.classList.add(CLASS_NAME_SHOWING$1);\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate();\n      }\n      this._element.classList.add(CLASS_NAME_SHOW$3);\n      this._element.classList.remove(CLASS_NAME_SHOWING$1);\n      EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n        relatedTarget\n      });\n    };\n    this._queueCallback(completeCallBack, this._element, true);\n  }\n  hide() {\n    if (!this._isShown) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    this._focustrap.deactivate();\n    this._element.blur();\n    this._isShown = false;\n    this._element.classList.add(CLASS_NAME_HIDING);\n    this._backdrop.hide();\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n      this._element.removeAttribute('aria-modal');\n      this._element.removeAttribute('role');\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset();\n      }\n      EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n    };\n    this._queueCallback(completeCallback, this._element, true);\n  }\n  dispose() {\n    this._backdrop.dispose();\n    this._focustrap.deactivate();\n    super.dispose();\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n        return;\n      }\n      this.hide();\n    };\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop);\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    });\n  }\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    });\n  }\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return;\n      }\n      if (this._config.keyboard) {\n        this.hide();\n        return;\n      }\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n    });\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config](this);\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this);\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  EventHandler.one(target, EVENT_HIDDEN$3, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus();\n    }\n  });\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide();\n  }\n  const data = Offcanvas.getOrCreateInstance(target);\n  data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show();\n  }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide();\n    }\n  }\n});\nenableDismissTrigger(Offcanvas);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\nconst DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n};\n// js-docs-end allow-list\n\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i;\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase();\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue));\n    }\n    return true;\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml;\n  }\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml);\n  }\n  const domParser = new window.DOMParser();\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase();\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove();\n      continue;\n    }\n    const attributeList = [].concat(...element.attributes);\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName);\n      }\n    }\n  }\n  return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n  allowList: DefaultAllowlist,\n  content: {},\n  // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n};\nconst DefaultType$4 = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n};\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n};\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super();\n    this._config = this._getConfig(config);\n  }\n\n  // Getters\n  static get Default() {\n    return Default$4;\n  }\n  static get DefaultType() {\n    return DefaultType$4;\n  }\n  static get NAME() {\n    return NAME$5;\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n  }\n  hasContent() {\n    return this.getContent().length > 0;\n  }\n  changeContent(content) {\n    this._checkContent(content);\n    this._config.content = {\n      ...this._config.content,\n      ...content\n    };\n    return this;\n  }\n  toHtml() {\n    const templateWrapper = document.createElement('div');\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector);\n    }\n    const template = templateWrapper.children[0];\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '));\n    }\n    return template;\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config);\n    this._checkContent(config.content);\n  }\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({\n        selector,\n        entry: content\n      }, DefaultContentType);\n    }\n  }\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template);\n    if (!templateElement) {\n      return;\n    }\n    content = this._resolvePossibleFunction(content);\n    if (!content) {\n      templateElement.remove();\n      return;\n    }\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement);\n      return;\n    }\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content);\n      return;\n    }\n    templateElement.textContent = content;\n  }\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this]);\n  }\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = '';\n      templateElement.append(element);\n      return;\n    }\n    templateElement.textContent = element.textContent;\n  }\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' + '<div class=\"tooltip-arrow\"></div>' + '<div class=\"tooltip-inner\"></div>' + '</div>',\n  title: '',\n  trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n};\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n    }\n    super(element, config);\n\n    // Private\n    this._isEnabled = true;\n    this._timeout = 0;\n    this._isHovered = null;\n    this._activeTrigger = {};\n    this._popper = null;\n    this._templateFactory = null;\n    this._newContent = null;\n\n    // Protected\n    this.tip = null;\n    this._setListeners();\n    if (!this._config.selector) {\n      this._fixTitle();\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default$3;\n  }\n  static get DefaultType() {\n    return DefaultType$3;\n  }\n  static get NAME() {\n    return NAME$4;\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true;\n  }\n  disable() {\n    this._isEnabled = false;\n  }\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled;\n  }\n  toggle() {\n    if (!this._isEnabled) {\n      return;\n    }\n    this._activeTrigger.click = !this._activeTrigger.click;\n    if (this._isShown()) {\n      this._leave();\n      return;\n    }\n    this._enter();\n  }\n  dispose() {\n    clearTimeout(this._timeout);\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n    }\n    this._disposePopper();\n    super.dispose();\n  }\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements');\n    }\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return;\n    }\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n    const shadowRoot = findShadowRoot(this._element);\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return;\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper();\n    const tip = this._getTipElement();\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n    const {\n      container\n    } = this._config;\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip);\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n    }\n    this._popper = this._createPopper(tip);\n    tip.classList.add(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop);\n      }\n    }\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n      if (this._isHovered === false) {\n        this._leave();\n      }\n      this._isHovered = false;\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  hide() {\n    if (!this._isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const tip = this._getTipElement();\n    tip.classList.remove(CLASS_NAME_SHOW$2);\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop);\n      }\n    }\n    this._activeTrigger[TRIGGER_CLICK] = false;\n    this._activeTrigger[TRIGGER_FOCUS] = false;\n    this._activeTrigger[TRIGGER_HOVER] = false;\n    this._isHovered = null; // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return;\n      }\n      if (!this._isHovered) {\n        this._disposePopper();\n      }\n      this._element.removeAttribute('aria-describedby');\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n    };\n    this._queueCallback(complete, this.tip, this._isAnimated());\n  }\n  update() {\n    if (this._popper) {\n      this._popper.update();\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle());\n  }\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n    }\n    return this.tip;\n  }\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml();\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null;\n    }\n    tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2);\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n    const tipId = getUID(this.constructor.NAME).toString();\n    tip.setAttribute('id', tipId);\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE$2);\n    }\n    return tip;\n  }\n  setContent(content) {\n    this._newContent = content;\n    if (this._isShown()) {\n      this._disposePopper();\n      this.show();\n    }\n  }\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content);\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      });\n    }\n    return this._templateFactory;\n  }\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    };\n  }\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n  }\n  _isAnimated() {\n    return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n  }\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n  }\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element]);\n    const attachment = AttachmentMap[placement.toUpperCase()];\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n  }\n  _getOffset() {\n    const {\n      offset\n    } = this._config;\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10));\n    }\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element);\n    }\n    return offset;\n  }\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element]);\n  }\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [{\n        name: 'flip',\n        options: {\n          fallbackPlacements: this._config.fallbackPlacements\n        }\n      }, {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }, {\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      }, {\n        name: 'arrow',\n        options: {\n          element: `.${this.constructor.NAME}-arrow`\n        }\n      }, {\n        name: 'preSetPlacement',\n        enabled: true,\n        phase: 'beforeMain',\n        fn: data => {\n          // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n          // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n          this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n        }\n      }]\n    };\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    };\n  }\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ');\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context.toggle();\n        });\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n        const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n          context._enter();\n        });\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event);\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n          context._leave();\n        });\n      }\n    }\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide();\n      }\n    };\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n  }\n  _fixTitle() {\n    const title = this._element.getAttribute('title');\n    if (!title) {\n      return;\n    }\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title);\n    }\n    this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title');\n  }\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true;\n      return;\n    }\n    this._isHovered = true;\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show();\n      }\n    }, this._config.delay.show);\n  }\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return;\n    }\n    this._isHovered = false;\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide();\n      }\n    }, this._config.delay.hide);\n  }\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout);\n    this._timeout = setTimeout(handler, timeout);\n  }\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true);\n  }\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element);\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute];\n      }\n    }\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    };\n    config = this._mergeConfigObj(config);\n    config = this._configAfterMerge(config);\n    this._typeCheckConfig(config);\n    return config;\n  }\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container);\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      };\n    }\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString();\n    }\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString();\n    }\n    return config;\n  }\n  _getDelegateConfig() {\n    const config = {};\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value;\n      }\n    }\n    config.selector = false;\n    config.trigger = 'manual';\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config;\n  }\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy();\n      this._popper = null;\n    }\n    if (this.tip) {\n      this.tip.remove();\n      this.tip = null;\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' + '<div class=\"popover-arrow\"></div>' + '<h3 class=\"popover-header\"></h3>' + '<div class=\"popover-body\"></div>' + '</div>',\n  trigger: 'click'\n};\nconst DefaultType$2 = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n};\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default$2;\n  }\n  static get DefaultType() {\n    return DefaultType$2;\n  }\n  static get NAME() {\n    return NAME$3;\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent();\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    };\n  }\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content);\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n  offset: null,\n  // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n  offset: '(number|null)',\n  // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n};\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n    this._activeTarget = null;\n    this._observer = null;\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    };\n    this.refresh(); // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default$1;\n  }\n  static get DefaultType() {\n    return DefaultType$1;\n  }\n  static get NAME() {\n    return NAME$2;\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables();\n    this._maybeEnableSmoothScroll();\n    if (this._observer) {\n      this._observer.disconnect();\n    } else {\n      this._observer = this._getNewObserver();\n    }\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section);\n    }\n  }\n  dispose() {\n    this._observer.disconnect();\n    super.dispose();\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body;\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n    }\n    return config;\n  }\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return;\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK);\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash);\n      if (observableSection) {\n        event.preventDefault();\n        const root = this._rootElement || window;\n        const height = observableSection.offsetTop - this._element.offsetTop;\n        if (root.scrollTo) {\n          root.scrollTo({\n            top: height,\n            behavior: 'smooth'\n          });\n          return;\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height;\n      }\n    });\n  }\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    };\n    return new IntersectionObserver(entries => this._observerCallback(entries), options);\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n      this._process(targetElement(entry));\n    };\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n    this._previousScrollData.parentScrollTop = parentScrollTop;\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null;\n        this._clearActiveClass(targetElement(entry));\n        continue;\n      }\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop;\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry);\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return;\n        }\n        continue;\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry);\n      }\n    }\n  }\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map();\n    this._observableSections = new Map();\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue;\n      }\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element);\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor);\n        this._observableSections.set(anchor.hash, observableSection);\n      }\n    }\n  }\n  _process(target) {\n    if (this._activeTarget === target) {\n      return;\n    }\n    this._clearActiveClass(this._config.target);\n    this._activeTarget = target;\n    target.classList.add(CLASS_NAME_ACTIVE$1);\n    this._activateParents(target);\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n      relatedTarget: target\n    });\n  }\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n      return;\n    }\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE$1);\n      }\n    }\n  }\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE$1);\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE$1}`, parent);\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE$1);\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API$1, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy);\n  }\n});\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME$1 = 'tab';\nconst DATA_KEY$1 = 'bs.tab';\nconst EVENT_KEY$1 = `.${DATA_KEY$1}`;\nconst EVENT_HIDE$1 = `hide${EVENT_KEY$1}`;\nconst EVENT_HIDDEN$1 = `hidden${EVENT_KEY$1}`;\nconst EVENT_SHOW$1 = `show${EVENT_KEY$1}`;\nconst EVENT_SHOWN$1 = `shown${EVENT_KEY$1}`;\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY$1}`;\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY$1}`;\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY$1}`;\nconst ARROW_LEFT_KEY = 'ArrowLeft';\nconst ARROW_RIGHT_KEY = 'ArrowRight';\nconst ARROW_UP_KEY = 'ArrowUp';\nconst ARROW_DOWN_KEY = 'ArrowDown';\nconst HOME_KEY = 'Home';\nconst END_KEY = 'End';\nconst CLASS_NAME_ACTIVE = 'active';\nconst CLASS_NAME_FADE$1 = 'fade';\nconst CLASS_NAME_SHOW$1 = 'show';\nconst CLASS_DROPDOWN = 'dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle';\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu';\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = `:not(${SELECTOR_DROPDOWN_TOGGLE})`;\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]';\nconst SELECTOR_OUTER = '.nav-item, .list-group-item';\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]'; // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`;\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`;\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element);\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL);\n    if (!this._parent) {\n      return;\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren());\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event));\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME$1;\n  }\n\n  // Public\n  show() {\n    // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element;\n    if (this._elemIsActive(innerElem)) {\n      return;\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem();\n    const hideEvent = active ? EventHandler.trigger(active, EVENT_HIDE$1, {\n      relatedTarget: innerElem\n    }) : null;\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW$1, {\n      relatedTarget: active\n    });\n    if (showEvent.defaultPrevented || hideEvent && hideEvent.defaultPrevented) {\n      return;\n    }\n    this._deactivate(active, innerElem);\n    this._activate(innerElem, active);\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.add(CLASS_NAME_ACTIVE);\n    this._activate(SelectorEngine.getElementFromSelector(element)); // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.removeAttribute('tabindex');\n      element.setAttribute('aria-selected', true);\n      this._toggleDropDown(element, true);\n      EventHandler.trigger(element, EVENT_SHOWN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return;\n    }\n    element.classList.remove(CLASS_NAME_ACTIVE);\n    element.blur();\n    this._deactivate(SelectorEngine.getElementFromSelector(element)); // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW$1);\n        return;\n      }\n      element.setAttribute('aria-selected', false);\n      element.setAttribute('tabindex', '-1');\n      this._toggleDropDown(element, false);\n      EventHandler.trigger(element, EVENT_HIDDEN$1, {\n        relatedTarget: relatedElem\n      });\n    };\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE$1));\n  }\n  _keydown(event) {\n    if (![ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key)) {\n      return;\n    }\n    event.stopPropagation(); // stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault();\n    const children = this._getChildren().filter(element => !isDisabled(element));\n    let nextActiveElement;\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1];\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key);\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true);\n    }\n    if (nextActiveElement) {\n      nextActiveElement.focus({\n        preventScroll: true\n      });\n      Tab.getOrCreateInstance(nextActiveElement).show();\n    }\n  }\n  _getChildren() {\n    // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent);\n  }\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null;\n  }\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist');\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child);\n    }\n  }\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child);\n    const isActive = this._elemIsActive(child);\n    const outerElem = this._getOuterElement(child);\n    child.setAttribute('aria-selected', isActive);\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation');\n    }\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1');\n    }\n    this._setAttributeIfNotExists(child, 'role', 'tab');\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child);\n  }\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child);\n    if (!target) {\n      return;\n    }\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel');\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`);\n    }\n  }\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element);\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return;\n    }\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem);\n      if (element) {\n        element.classList.toggle(className, open);\n      }\n    };\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE);\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW$1);\n    outerElem.setAttribute('aria-expanded', open);\n  }\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value);\n    }\n  }\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE);\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem);\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this);\n      if (typeof config !== 'string') {\n        return;\n      }\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`);\n      }\n      data[config]();\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault();\n  }\n  if (isDisabled(this)) {\n    return;\n  }\n  Tab.getOrCreateInstance(this).show();\n});\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element);\n  }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n\n/**\n * Constants\n */\n\nconst NAME = 'toast';\nconst DATA_KEY = 'bs.toast';\nconst EVENT_KEY = `.${DATA_KEY}`;\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`;\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`;\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`;\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`;\nconst EVENT_HIDE = `hide${EVENT_KEY}`;\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`;\nconst EVENT_SHOW = `show${EVENT_KEY}`;\nconst EVENT_SHOWN = `shown${EVENT_KEY}`;\nconst CLASS_NAME_FADE = 'fade';\nconst CLASS_NAME_HIDE = 'hide'; // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show';\nconst CLASS_NAME_SHOWING = 'showing';\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n};\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n};\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config);\n    this._timeout = null;\n    this._hasMouseInteraction = false;\n    this._hasKeyboardInteraction = false;\n    this._setListeners();\n  }\n\n  // Getters\n  static get Default() {\n    return Default;\n  }\n  static get DefaultType() {\n    return DefaultType;\n  }\n  static get NAME() {\n    return NAME;\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW);\n    if (showEvent.defaultPrevented) {\n      return;\n    }\n    this._clearTimeout();\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE);\n    }\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING);\n      EventHandler.trigger(this._element, EVENT_SHOWN);\n      this._maybeScheduleHide();\n    };\n    this._element.classList.remove(CLASS_NAME_HIDE); // @deprecated\n    reflow(this._element);\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  hide() {\n    if (!this.isShown()) {\n      return;\n    }\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE);\n    if (hideEvent.defaultPrevented) {\n      return;\n    }\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE); // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW);\n      EventHandler.trigger(this._element, EVENT_HIDDEN);\n    };\n    this._element.classList.add(CLASS_NAME_SHOWING);\n    this._queueCallback(complete, this._element, this._config.animation);\n  }\n  dispose() {\n    this._clearTimeout();\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW);\n    }\n    super.dispose();\n  }\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW);\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return;\n    }\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return;\n    }\n    this._timeout = setTimeout(() => {\n      this.hide();\n    }, this._config.delay);\n  }\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout':\n        {\n          this._hasMouseInteraction = isInteracting;\n          break;\n        }\n      case 'focusin':\n      case 'focusout':\n        {\n          this._hasKeyboardInteraction = isInteracting;\n          break;\n        }\n    }\n    if (isInteracting) {\n      this._clearTimeout();\n      return;\n    }\n    const nextElement = event.relatedTarget;\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return;\n    }\n    this._maybeScheduleHide();\n  }\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false));\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true));\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false));\n  }\n  _clearTimeout() {\n    clearTimeout(this._timeout);\n    this._timeout = null;\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config);\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`);\n        }\n        data[config](this);\n      }\n    });\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast);\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast);\n\nexport { Alert, Button, Carousel, Collapse, Dropdown, Modal, Offcanvas, Popover, ScrollSpy, Tab, Toast, Tooltip };\n//# sourceMappingURL=bootstrap.esm.js.map\n","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Import and setup functions to control Bootstrap's behavior.\nimport \"@popperjs/core\";\nimport { Tooltip } from \"bootstrap\";\nimport { documentReady } from \"./mixin\";\n\nimport \"../styles/bootstrap.scss\";\n\n/*******************************************************************************\n * Trigger tooltips\n */\n\n/**\n * Add tooltip to each element with the \"tooltip\" data-bs-toogle class\n */\nfunction TriggerTooltip() {\n  var tooltipTriggerList = [].slice.call(\n    document.querySelectorAll('[data-bs-toggle=\"tooltip\"]')\n  );\n  tooltipTriggerList.map(function (tooltipTriggerEl) {\n    return new Tooltip(tooltipTriggerEl, { delay: { show: 500, hide: 100 } });\n  });\n}\n\n/*******************************************************************************\n * back to top button\n */\nfunction backToTop() {\n  var btn = document.getElementById(\"pst-back-to-top\");\n  btn.addEventListener(\"click\", function () {\n    document.body.scrollTop = 0;\n    document.documentElement.scrollTop = 0;\n  });\n}\n\nfunction showBackToTop() {\n  var btn = document.getElementById(\"pst-back-to-top\");\n  var header = document\n    .getElementsByClassName(\"bd-header\")[0]\n    .getBoundingClientRect();\n  window.addEventListener(\"scroll\", function () {\n    if (this.oldScroll > this.scrollY && this.scrollY > header.bottom) {\n      btn.style.display = \"block\";\n    } else {\n      btn.style.display = \"none\";\n    }\n    this.oldScroll = this.scrollY;\n  });\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n 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diff --git a/stable/_static/scripts/pydata-sphinx-theme.js b/stable/_static/scripts/pydata-sphinx-theme.js
index adb90bd9..85ae6bad 100644
--- a/stable/_static/scripts/pydata-sphinx-theme.js
+++ b/stable/_static/scripts/pydata-sphinx-theme.js
@@ -1,32 +1,2 @@
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i='[data-toggle="collapse"][data-parent="'+this._config.parent+'"]',r=[].slice.call(t.querySelectorAll(i));return o.default(r).each((function(t,i){n._addAriaAndCollapsedClass(e._getTargetFromElement(i),[i])})),t},t._addAriaAndCollapsedClass=function(e,t){var n=o.default(e).hasClass("show");t.length&&o.default(t).toggleClass("collapsed",!n).attr("aria-expanded",n)},e._getTargetFromElement=function(e){var t=c.getSelectorFromElement(e);return t?document.querySelector(t):null},e._jQueryInterface=function(t){return this.each((function(){var n=o.default(this),i=n.data("bs.collapse"),r=l({},S,n.data(),"object"==typeof t&&t?t:{});if(!i&&r.toggle&&"string"==typeof t&&/show|hide/.test(t)&&(r.toggle=!1),i||(i=new e(this,r),n.data("bs.collapse",i)),"string"==typeof t){if(void 0===i[t])throw new TypeError('No method named "'+t+'"');i[t]()}}))},s(e,null,[{key:"VERSION",get:function(){return"4.6.1"}},{key:"Default",get:function(){return 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e(e,t){this._element=e,this._popper=null,this._config=this._getConfig(t),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar(),this._addEventListeners()}var t=e.prototype;return t.toggle=function(){if(!this._element.disabled&&!o.default(this._element).hasClass("disabled")){var t=o.default(this._menu).hasClass("show");e._clearMenus(),t||this.show(!0)}},t.show=function(t){if(void 0===t&&(t=!1),!(this._element.disabled||o.default(this._element).hasClass("disabled")||o.default(this._menu).hasClass("show"))){var n={relatedTarget:this._element},i=o.default.Event("show.bs.dropdown",n),a=e._getParentFromElement(this._element);if(o.default(a).trigger(i),!i.isDefaultPrevented()){if(!this._inNavbar&&t){if(void 0===r.default)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");var s=this._element;"parent"===this._config.reference?s=a:c.isElement(this._config.reference)&&(s=this._config.reference,void 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t={relatedTarget:this._element},n=o.default.Event("hide.bs.dropdown",t),i=e._getParentFromElement(this._element);o.default(i).trigger(n),n.isDefaultPrevented()||(this._popper&&this._popper.destroy(),o.default(this._menu).toggleClass("show"),o.default(i).toggleClass("show").trigger(o.default.Event("hidden.bs.dropdown",t)))}},t.dispose=function(){o.default.removeData(this._element,"bs.dropdown"),o.default(this._element).off(".bs.dropdown"),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},t.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},t._addEventListeners=function(){var e=this;o.default(this._element).on("click.bs.dropdown",(function(t){t.preventDefault(),t.stopPropagation(),e.toggle()}))},t._getConfig=function(e){return 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e=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){o.default(document.body).removeClass("modal-open"),e._resetAdjustments(),e._resetScrollbar(),o.default(e._element).trigger("hidden.bs.modal")}))},t._removeBackdrop=function(){this._backdrop&&(o.default(this._backdrop).remove(),this._backdrop=null)},t._showBackdrop=function(e){var 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a=this.getTipElement(),s=c.getUID(this.constructor.NAME);a.setAttribute("id",s),this.element.setAttribute("aria-describedby",s),this.setContent(),this.config.animation&&o.default(a).addClass("fade");var l="function"==typeof this.config.placement?this.config.placement.call(this,a,this.element):this.config.placement,u=this._getAttachment(l);this.addAttachmentClass(u);var d=this._getContainer();o.default(a).data(this.constructor.DATA_KEY,this),o.default.contains(this.element.ownerDocument.documentElement,this.tip)||o.default(a).appendTo(d),o.default(this.element).trigger(this.constructor.Event.INSERTED),this._popper=new r.default(this.element,a,this._getPopperConfig(u)),o.default(a).addClass("show"),o.default(a).addClass(this.config.customClass),"ontouchstart"in document.documentElement&&o.default(document.body).children().on("mouseover",null,o.default.noop);var f=function(){e.config.animation&&e._fixTransition();var t=e._hoverState;e._hoverState=null,o.default(e.element).trigger(e.constructor.Event.SHOWN),"out"===t&&e._leave(null,e)};if(o.default(this.tip).hasClass("fade")){var h=c.getTransitionDurationFromElement(this.tip);o.default(this.tip).one(c.TRANSITION_END,f).emulateTransitionEnd(h)}else f()}},t.hide=function(e){var t=this,n=this.getTipElement(),i=o.default.Event(this.constructor.Event.HIDE),r=function(){"show"!==t._hoverState&&n.parentNode&&n.parentNode.removeChild(n),t._cleanTipClass(),t.element.removeAttribute("aria-describedby"),o.default(t.element).trigger(t.constructor.Event.HIDDEN),null!==t._popper&&t._popper.destroy(),e&&e()};if(o.default(this.element).trigger(i),!i.isDefaultPrevented()){if(o.default(n).removeClass("show"),"ontouchstart"in document.documentElement&&o.default(document.body).children().off("mouseover",null,o.default.noop),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,o.default(this.tip).hasClass("fade")){var 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Symbol(\"webpack error\") : \"__webpack_error__\";\nvar resolveQueue = (queue) => {\n\tif(queue && queue.d < 1) {\n\t\tqueue.d = 1;\n\t\tqueue.forEach((fn) => (fn.r--));\n\t\tqueue.forEach((fn) => (fn.r-- ? fn.r++ : fn()));\n\t}\n}\nvar wrapDeps = (deps) => (deps.map((dep) => {\n\tif(dep !== null && typeof dep === \"object\") {\n\t\tif(dep[webpackQueues]) return dep;\n\t\tif(dep.then) {\n\t\t\tvar queue = [];\n\t\t\tqueue.d = 0;\n\t\t\tdep.then((r) => {\n\t\t\t\tobj[webpackExports] = r;\n\t\t\t\tresolveQueue(queue);\n\t\t\t}, (e) => {\n\t\t\t\tobj[webpackError] = e;\n\t\t\t\tresolveQueue(queue);\n\t\t\t});\n\t\t\tvar obj = {};\n\t\t\tobj[webpackQueues] = (fn) => (fn(queue));\n\t\t\treturn obj;\n\t\t}\n\t}\n\tvar ret = {};\n\tret[webpackQueues] = x => {};\n\tret[webpackExports] = dep;\n\treturn ret;\n}));\n__webpack_require__.a = (module, body, hasAwait) => {\n\tvar queue;\n\thasAwait && ((queue = []).d = -1);\n\tvar depQueues = new Set();\n\tvar exports = module.exports;\n\tvar currentDeps;\n\tvar outerResolve;\n\tvar reject;\n\tvar promise = new Promise((resolve, rej) => {\n\t\treject = rej;\n\t\touterResolve = resolve;\n\t});\n\tpromise[webpackExports] = exports;\n\tpromise[webpackQueues] = (fn) => (queue && fn(queue), depQueues.forEach(fn), promise[\"catch\"](x => {}));\n\tmodule.exports = promise;\n\tbody((deps) => {\n\t\tcurrentDeps = wrapDeps(deps);\n\t\tvar fn;\n\t\tvar getResult = () => (currentDeps.map((d) => {\n\t\t\tif(d[webpackError]) throw d[webpackError];\n\t\t\treturn d[webpackExports];\n\t\t}))\n\t\tvar promise = new Promise((resolve) => {\n\t\t\tfn = () => (resolve(getResult));\n\t\t\tfn.r = 0;\n\t\t\tvar fnQueue = (q) => (q !== queue && !depQueues.has(q) && (depQueues.add(q), q && !q.d && (fn.r++, q.push(fn))));\n\t\t\tcurrentDeps.map((dep) => (dep[webpackQueues](fnQueue)));\n\t\t});\n\t\treturn fn.r ? promise : getResult();\n\t}, (err) => ((err ? reject(promise[webpackError] = err) : outerResolve(exports)), resolveQueue(queue)));\n\tqueue && queue.d < 0 && (queue.d = 0);\n};","/**\n * Compare [semver](https://semver.org/) version strings to find greater, equal or lesser.\n * This library supports the full semver specification, including comparing versions with different number of digits like `1.0.0`, `1.0`, `1`, and pre-release versions like `1.0.0-alpha`.\n * @param v1 - First version to compare\n * @param v2 - Second version to compare\n * @returns Numeric value compatible with the [Array.sort(fn) interface](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort#Parameters).\n */\nexport const compareVersions = (v1, v2) => {\n    // validate input and split into segments\n    const n1 = validateAndParse(v1);\n    const n2 = validateAndParse(v2);\n    // pop off the patch\n    const p1 = n1.pop();\n    const p2 = n2.pop();\n    // validate numbers\n    const r = compareSegments(n1, n2);\n    if (r !== 0)\n        return r;\n    // validate pre-release\n    if (p1 && p2) {\n        return compareSegments(p1.split('.'), p2.split('.'));\n    }\n    else if (p1 || p2) {\n        return p1 ? -1 : 1;\n    }\n    return 0;\n};\n/**\n * Validate [semver](https://semver.org/) version strings.\n *\n * @param version Version number to validate\n * @returns `true` if the version number is a valid semver version number, `false` otherwise.\n *\n * @example\n * ```\n * validate('1.0.0-rc.1'); // return true\n * validate('1.0-rc.1'); // return false\n * validate('foo'); // return false\n * ```\n */\nexport const validate = (version) => typeof version === 'string' && /^[v\\d]/.test(version) && semver.test(version);\n/**\n * Compare [semver](https://semver.org/) version strings using the specified operator.\n *\n * @param v1 First version to compare\n * @param v2 Second version to compare\n * @param operator Allowed arithmetic operator to use\n * @returns `true` if the comparison between the firstVersion and the secondVersion satisfies the operator, `false` otherwise.\n *\n * @example\n * ```\n * compare('10.1.8', '10.0.4', '>'); // return true\n * compare('10.0.1', '10.0.1', '='); // return true\n * compare('10.1.1', '10.2.2', '<'); // return true\n * compare('10.1.1', '10.2.2', '<='); // return true\n * compare('10.1.1', '10.2.2', '>='); // return false\n * ```\n */\nexport const compare = (v1, v2, operator) => {\n    // validate input operator\n    assertValidOperator(operator);\n    // since result of compareVersions can only be -1 or 0 or 1\n    // a simple map can be used to replace switch\n    const res = compareVersions(v1, v2);\n    return operatorResMap[operator].includes(res);\n};\n/**\n * Match [npm semver](https://docs.npmjs.com/cli/v6/using-npm/semver) version range.\n *\n * @param version Version number to match\n * @param range Range pattern for version\n * @returns `true` if the version number is within the range, `false` otherwise.\n *\n * @example\n * ```\n * satisfies('1.1.0', '^1.0.0'); // return true\n * satisfies('1.1.0', '~1.0.0'); // return false\n * ```\n */\nexport const satisfies = (version, range) => {\n    // if no range operator then \"=\"\n    const m = range.match(/^([<>=~^]+)/);\n    const op = m ? m[1] : '=';\n    // if gt/lt/eq then operator compare\n    if (op !== '^' && op !== '~')\n        return compare(version, range, op);\n    // else range of either \"~\" or \"^\" is assumed\n    const [v1, v2, v3, , vp] = validateAndParse(version);\n    const [r1, r2, r3, , rp] = validateAndParse(range);\n    const v = [v1, v2, v3];\n    const r = [r1, r2 !== null && r2 !== void 0 ? r2 : 'x', r3 !== null && r3 !== void 0 ? r3 : 'x'];\n    // validate pre-release\n    if (rp) {\n        if (!vp)\n            return false;\n        if (compareSegments(v, r) !== 0)\n            return false;\n        if (compareSegments(vp.split('.'), rp.split('.')) === -1)\n            return false;\n    }\n    // first non-zero number\n    const nonZero = r.findIndex((v) => v !== '0') + 1;\n    // pointer to where segments can be >=\n    const i = op === '~' ? 2 : nonZero > 1 ? nonZero : 1;\n    // before pointer must be equal\n    if (compareSegments(v.slice(0, i), r.slice(0, i)) !== 0)\n        return false;\n    // after pointer must be >=\n    if (compareSegments(v.slice(i), r.slice(i)) === -1)\n        return false;\n    return true;\n};\nconst semver = /^[v^~<>=]*?(\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+)(?:\\.([x*]|\\d+))?(?:-([\\da-z\\-]+(?:\\.[\\da-z\\-]+)*))?(?:\\+[\\da-z\\-]+(?:\\.[\\da-z\\-]+)*)?)?)?$/i;\nconst validateAndParse = (version) => {\n    if (typeof version !== 'string') {\n        throw new TypeError('Invalid argument expected string');\n    }\n    const match = version.match(semver);\n    if (!match) {\n        throw new Error(`Invalid argument not valid semver ('${version}' received)`);\n    }\n    match.shift();\n    return match;\n};\nconst isWildcard = (s) => s === '*' || s === 'x' || s === 'X';\nconst tryParse = (v) => {\n    const n = parseInt(v, 10);\n    return isNaN(n) ? v : n;\n};\nconst forceType = (a, b) => typeof a !== typeof b ? [String(a), String(b)] : [a, b];\nconst compareStrings = (a, b) => {\n    if (isWildcard(a) || isWildcard(b))\n        return 0;\n    const [ap, bp] = forceType(tryParse(a), tryParse(b));\n    if (ap > bp)\n        return 1;\n    if (ap < bp)\n        return -1;\n    return 0;\n};\nconst compareSegments = (a, b) => {\n    for (let i = 0; i < Math.max(a.length, b.length); i++) {\n        const r = compareStrings(a[i] || '0', b[i] || '0');\n        if (r !== 0)\n            return r;\n    }\n    return 0;\n};\nconst operatorResMap = {\n    '>': [1],\n    '>=': [0, 1],\n    '=': [0],\n    '<=': [-1, 0],\n    '<': [-1],\n};\nconst allowedOperators = Object.keys(operatorResMap);\nconst assertValidOperator = (op) => {\n    if (typeof op !== 'string') {\n        throw new TypeError(`Invalid operator type, expected string but got ${typeof op}`);\n    }\n    if (allowedOperators.indexOf(op) === -1) {\n        throw new Error(`Invalid operator, expected one of ${allowedOperators.join('|')}`);\n    }\n};\n//# sourceMappingURL=index.js.map","/* define several functions to replace jQuery methods\n * inspired by https://tobiasahlin.com/blog/move-from-jquery-to-vanilla-javascript/\n */\n\n/**\n * Execute a method if DOM has finished loading\n *\n * @param {function} callback the method to execute\n */\nexport function documentReady(callback) {\n  if (document.readyState != \"loading\") callback();\n  else document.addEventListener(\"DOMContentLoaded\", callback);\n}\n","// Define the custom behavior of the page\nimport { documentReady } from \"./mixin\";\nimport { compare, validate } from \"compare-versions\";\n\nimport \"../styles/pydata-sphinx-theme.scss\";\n\n/*******************************************************************************\n * Theme interaction\n */\n\nvar prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\");\n\n/**\n * set the the body theme to the one specified by the user browser\n *\n * @param {event} e\n */\nfunction autoTheme(e) {\n  document.documentElement.dataset.theme = prefersDark.matches\n    ? \"dark\"\n    : \"light\";\n}\n\n/**\n * Set the theme using the specified mode.\n * It can be one of [\"auto\", \"dark\", \"light\"]\n *\n * @param {str} mode\n */\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  // get the theme\n  var colorScheme = prefersDark.matches ? \"dark\" : \"light\";\n  document.documentElement.dataset.mode = mode;\n  var theme = mode == \"auto\" ? colorScheme : mode;\n  document.documentElement.dataset.theme = theme;\n  // TODO: remove this line after Bootstrap upgrade\n  // v5.3 has a colors mode: https://getbootstrap.com/docs/5.3/customize/color-modes/\n  document.querySelectorAll(\".dropdown-menu\").forEach((el) => {\n    if (theme === \"dark\") {\n      el.classList.add(\"dropdown-menu-dark\");\n    } else {\n      el.classList.remove(\"dropdown-menu-dark\");\n    }\n  });\n\n  // save mode and theme\n  localStorage.setItem(\"mode\", mode);\n  localStorage.setItem(\"theme\", theme);\n  console.log(`[PST]: Changed to ${mode} mode using the ${theme} theme.`);\n\n  // add a listener if set on auto\n  prefersDark.onchange = mode == \"auto\" ? autoTheme : \"\";\n}\n\n/**\n * Change the theme option order so that clicking on the btn is always a change\n * from \"auto\"\n */\nfunction cycleMode() {\n  const defaultMode = document.documentElement.dataset.defaultMode || \"auto\";\n  const currentMode = localStorage.getItem(\"mode\") || defaultMode;\n\n  var loopArray = (arr, current) => {\n    var nextPosition = arr.indexOf(current) + 1;\n    if (nextPosition === arr.length) {\n      nextPosition = 0;\n    }\n    return arr[nextPosition];\n  };\n\n  // make sure the next theme after auto is always a change\n  var modeList = prefersDark.matches\n    ? [\"auto\", \"light\", \"dark\"]\n    : [\"auto\", \"dark\", \"light\"];\n  var newMode = loopArray(modeList, currentMode);\n  setTheme(newMode);\n}\n\n/**\n * add the theme listener on the btns of the navbar\n */\nfunction addModeListener() {\n  // the theme was set a first time using the initial mini-script\n  // running setMode will ensure the use of the dark mode if auto is selected\n  setTheme(document.documentElement.dataset.mode);\n\n  // Attach event handlers for toggling themes colors\n  document.querySelectorAll(\".theme-switch-button\").forEach((el) => {\n    el.addEventListener(\"click\", cycleMode);\n  });\n}\n\n/*******************************************************************************\n * TOC interactivity\n */\n\n/**\n * TOC sidebar - add \"active\" class to parent list\n *\n * Bootstrap's scrollspy adds the active class to the <a> link,\n * but for the automatic collapsing we need this on the parent list item.\n *\n * The event is triggered on \"window\" (and not the nav item as documented),\n * see https://github.com/twbs/bootstrap/issues/20086\n */\nfunction addTOCInteractivity() {\n  window.addEventListener(\"activate.bs.scrollspy\", function () {\n    const navLinks = document.querySelectorAll(\".bd-toc-nav a\");\n\n    navLinks.forEach((navLink) => {\n      navLink.parentElement.classList.remove(\"active\");\n    });\n\n    const activeNavLinks = document.querySelectorAll(\".bd-toc-nav a.active\");\n    activeNavLinks.forEach((navLink) => {\n      navLink.parentElement.classList.add(\"active\");\n    });\n  });\n}\n\n/*******************************************************************************\n * Scroll\n */\n\n/**\n * Navigation sidebar scrolling to active page\n */\nfunction scrollToActive() {\n  // If the docs nav doesn't exist, do nothing (e.g., on search page)\n  if (!document.querySelector(\".bd-docs-nav\")) {\n    return;\n  }\n\n  var sidebar = document.querySelector(\"div.bd-sidebar\");\n\n  // Remember the sidebar scroll position between page loads\n  // Inspired on source of revealjs.com\n  let storedScrollTop = parseInt(\n    sessionStorage.getItem(\"sidebar-scroll-top\"),\n    10\n  );\n\n  if (!isNaN(storedScrollTop)) {\n    // If we've got a saved scroll position, just use that\n    sidebar.scrollTop = storedScrollTop;\n    console.log(\"[PST]: Scrolled sidebar using stored browser position...\");\n  } else {\n    // Otherwise, calculate a position to scroll to based on the lowest `active` link\n    var sidebarNav = document.querySelector(\".bd-docs-nav\");\n    var active_pages = sidebarNav.querySelectorAll(\".active\");\n    if (active_pages.length > 0) {\n      // Use the last active page as the offset since it's the page we're on\n      var latest_active = active_pages[active_pages.length - 1];\n      var offset =\n        latest_active.getBoundingClientRect().y -\n        sidebar.getBoundingClientRect().y;\n      // Only scroll the navbar if the active link is lower than 50% of the page\n      if (latest_active.getBoundingClientRect().y > window.innerHeight * 0.5) {\n        let buffer = 0.25; // Buffer so we have some space above the scrolled item\n        sidebar.scrollTop = offset - sidebar.clientHeight * buffer;\n        console.log(\"[PST]: Scrolled sidebar using last active link...\");\n      }\n    }\n  }\n\n  // Store the sidebar scroll position\n  window.addEventListener(\"beforeunload\", () => {\n    sessionStorage.setItem(\"sidebar-scroll-top\", sidebar.scrollTop);\n  });\n}\n\n/*******************************************************************************\n * Search\n */\n\n/**\n * Find any search forms on the page and return their input element\n */\nvar findSearchInput = () => {\n  let forms = document.querySelectorAll(\"form.bd-search\");\n  if (!forms.length) {\n    // no search form found\n    return;\n  } else {\n    var form;\n    if (forms.length == 1) {\n      // there is exactly one search form (persistent or hidden)\n      form = forms[0];\n    } else {\n      // must be at least one persistent form, use the first persistent one\n      form = document.querySelector(\n        \"div:not(.search-button__search-container) > form.bd-search\"\n      );\n    }\n    return form.querySelector(\"input\");\n  }\n};\n\n/**\n * Activate the search field on the page.\n * - If there is a search field already visible it will be activated.\n * - If not, then a search field will pop up.\n */\nvar toggleSearchField = () => {\n  // Find the search input to highlight\n  let input = findSearchInput();\n\n  // if the input field is the hidden one (the one associated with the\n  // search button) then toggle the button state (to show/hide the field)\n  let searchPopupWrapper = document.querySelector(\".search-button__wrapper\");\n  let hiddenInput = searchPopupWrapper.querySelector(\"input\");\n  if (input === hiddenInput) {\n    searchPopupWrapper.classList.toggle(\"show\");\n  }\n  // when toggling off the search field, remove its focus\n  if (document.activeElement === input) {\n    input.blur();\n  } else {\n    input.focus();\n    input.select();\n    input.scrollIntoView({ block: \"center\" });\n  }\n};\n\n/**\n * Add an event listener for toggleSearchField() for Ctrl/Cmd + K\n */\nvar addEventListenerForSearchKeyboard = () => {\n  window.addEventListener(\n    \"keydown\",\n    (event) => {\n      let input = findSearchInput();\n      // toggle on Ctrl+k or ⌘+k\n      if (\n        // Ignore if shift or alt are pressed\n        !event.shiftKey &&\n        !event.altKey &&\n        // On Mac use ⌘, all other OS use Ctrl\n        (isMac\n          ? event.metaKey && !event.ctrlKey\n          : !event.metaKey && event.ctrlKey) &&\n        // Case-insensitive so the shortcut still works with caps lock\n        /k/i.test(event.key)\n      ) {\n        event.preventDefault();\n        toggleSearchField();\n      }\n      // also allow Escape key to hide (but not show) the dynamic search field\n      else if (document.activeElement === input && /Escape/i.test(event.key)) {\n        toggleSearchField();\n      }\n    },\n    true\n  );\n};\n\n/**\n * Find out if we're on a Mac\n */\nvar isMac = (navigator) => {\n  var platform = \"\";\n  if (\n    typeof navigator.userAgentData !== \"undefined\" &&\n    navigator.userAgentData != null\n  ) {\n    platform = navigator.userAgentData.platform;\n  } else if (typeof navigator.platform !== \"undefined\") {\n    platform = navigator.platform;\n  }\n  return /mac.?os/.test(platform.toLowerCase());\n};\n\n/**\n * Change the search hint to `meta key` if we are a Mac\n */\n\nvar changeSearchShortcutKey = () => {\n  let shortcuts = document.querySelectorAll(\".search-button__kbd-shortcut\");\n  if (isMac(window.navigator)) {\n    shortcuts.forEach(\n      (f) => (f.querySelector(\"kbd.kbd-shortcut__modifier\").innerText = \"⌘\")\n    );\n  }\n};\n\n/**\n * Activate callbacks for search button popup\n */\nvar setupSearchButtons = () => {\n  changeSearchShortcutKey();\n  addEventListenerForSearchKeyboard();\n\n  // Add the search button trigger event callback\n  document.querySelectorAll(\".search-button__button\").forEach((btn) => {\n    btn.onclick = toggleSearchField;\n  });\n\n  // Add the search button overlay event callback\n  let overlay = document.querySelector(\".search-button__overlay\");\n  if (overlay) {\n    overlay.onclick = toggleSearchField;\n  }\n};\n\n/*******************************************************************************\n * Version Switcher\n * Note that this depends on two variables existing that are defined in\n * and `html-page-context` hook:\n *\n * - DOCUMENTATION_OPTIONS.pagename\n * - DOCUMENTATION_OPTIONS.theme_switcher_url\n */\n\n/**\n * Check if corresponding page path exists in other version of docs\n * and, if so, go there instead of the homepage of the other docs version\n *\n * @param {event} event the event that trigger the check\n */\nasync function checkPageExistsAndRedirect(event) {\n  // ensure we don't follow the initial link\n  event.preventDefault();\n  let currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n  let tryUrl = event.currentTarget.getAttribute(\"href\");\n  let otherDocsHomepage = tryUrl.replace(currentFilePath, \"\");\n  try {\n    let head = await fetch(tryUrl, { method: \"HEAD\" });\n    if (head.ok) {\n      location.href = tryUrl; // the page exists, go there\n    } else {\n      location.href = otherDocsHomepage;\n    }\n  } catch (err) {\n    // something went wrong, probably CORS restriction, fallback to other docs homepage\n    location.href = otherDocsHomepage;\n  }\n}\n\n/**\n * Load and parse the version switcher JSON file from an absolute or relative URL.\n *\n * @param {string} url The URL to load version switcher entries from.\n */\nasync function fetchVersionSwitcherJSON(url) {\n  // first check if it's a valid URL\n  try {\n    var result = new URL(url);\n  } catch (err) {\n    if (err instanceof TypeError) {\n      // assume we got a relative path, and fix accordingly. But first, we need to\n      // use `fetch()` to follow redirects so we get the correct final base URL\n      const origin = await fetch(window.location.origin, { method: \"HEAD\" });\n      result = new URL(url, origin.url);\n    } else {\n      // something unexpected happened\n      throw err;\n    }\n  }\n  // load and return the JSON\n  const response = await fetch(result);\n  const data = await response.json();\n  return data;\n}\n\n// Populate the version switcher from the JSON data\nfunction populateVersionSwitcher(data, versionSwitcherBtns) {\n  const currentFilePath = `${DOCUMENTATION_OPTIONS.pagename}.html`;\n  versionSwitcherBtns.forEach((btn) => {\n    // Set empty strings by default so that these attributes exist and can be used in CSS selectors\n    btn.dataset[\"activeVersionName\"] = \"\";\n    btn.dataset[\"activeVersion\"] = \"\";\n  });\n  // in case there are multiple entries with the same version string, this helps us\n  // decide which entry's `name` to put on the button itself. Without this, it would\n  // always be the *last* version-matching entry; now it will be either the\n  // version-matching entry that is also marked as `\"preferred\": true`, or if that\n  // doesn't exist: the *first* version-matching entry.\n  data = data.map((entry) => {\n    // does this entry match the version that we're currently building/viewing?\n    entry.match =\n      entry.version == DOCUMENTATION_OPTIONS.theme_switcher_version_match;\n    entry.preferred = entry.preferred || false;\n    // if no custom name specified (e.g., \"latest\"), use version string\n    if (!(\"name\" in entry)) {\n      entry.name = entry.version;\n    }\n    return entry;\n  });\n  const hasMatchingPreferredEntry = data\n    .map((entry) => entry.preferred && entry.match)\n    .some(Boolean);\n  var foundMatch = false;\n  // create links to the corresponding page in the other docs versions\n  data.forEach((entry) => {\n    // create the node\n    const anchor = document.createElement(\"a\");\n    anchor.setAttribute(\n      \"class\",\n      \"dropdown-item list-group-item list-group-item-action py-1\"\n    );\n    anchor.setAttribute(\"href\", `${entry.url}${currentFilePath}`);\n    anchor.setAttribute(\"role\", \"option\");\n    const span = document.createElement(\"span\");\n    span.textContent = `${entry.name}`;\n    anchor.appendChild(span);\n    // Add dataset values for the version and name in case people want\n    // to apply CSS styling based on this information.\n    anchor.dataset[\"versionName\"] = entry.name;\n    anchor.dataset[\"version\"] = entry.version;\n    // replace dropdown button text with the preferred display name of the\n    // currently-viewed version, rather than using sphinx's {{ version }} variable.\n    // also highlight the dropdown entry for the currently-viewed version's entry\n    let matchesAndIsPreferred = hasMatchingPreferredEntry && entry.preferred;\n    let matchesAndIsFirst =\n      !hasMatchingPreferredEntry && !foundMatch && entry.match;\n    if (matchesAndIsPreferred || matchesAndIsFirst) {\n      anchor.classList.add(\"active\");\n      versionSwitcherBtns.forEach((btn) => {\n        btn.innerText = entry.name;\n        btn.dataset[\"activeVersionName\"] = entry.name;\n        btn.dataset[\"activeVersion\"] = entry.version;\n      });\n      foundMatch = true;\n    }\n    // There may be multiple version-switcher elements, e.g. one\n    // in a slide-over panel displayed on smaller screens.\n    document.querySelectorAll(\".version-switcher__menu\").forEach((menu) => {\n      // we need to clone the node for each menu, but onclick attributes are not\n      // preserved by `.cloneNode()` so we add onclick here after cloning.\n      let node = anchor.cloneNode(true);\n      node.onclick = checkPageExistsAndRedirect;\n      // on click, AJAX calls will check if the linked page exists before\n      // trying to redirect, and if not, will redirect to the homepage\n      // for that version of the docs.\n      menu.append(node);\n    });\n  });\n}\n\n/*******************************************************************************\n * Warning banner when viewing non-stable version of the docs.\n */\n\n/**\n * Show a warning banner when viewing a non-stable version of the docs.\n *\n * adapted 2023-06 from https://mne.tools/versionwarning.js, which was\n * originally adapted 2020-05 from https://scikit-learn.org/versionwarning.js\n *\n * @param {Array} data The version data used to populate the switcher menu.\n */\nfunction showVersionWarningBanner(data) {\n  var version = DOCUMENTATION_OPTIONS.VERSION;\n  // figure out what latest stable version is\n  var preferredEntries = data.filter((entry) => entry.preferred);\n  if (preferredEntries.length !== 1) {\n    const howMany = preferredEntries.length == 0 ? \"No\" : \"Multiple\";\n    console.log(\n      `[PST] ${howMany} versions marked \"preferred\" found in versions JSON, ignoring.`\n    );\n    return;\n  }\n  const preferredVersion = preferredEntries[0].version;\n  const preferredURL = preferredEntries[0].url;\n  // if already on preferred version, nothing to do\n  const versionsAreComparable = validate(version) && validate(preferredVersion);\n  if (versionsAreComparable && compare(version, preferredVersion, \"=\")) {\n    return;\n  }\n  // now construct the warning banner\n  var outer = document.createElement(\"div\");\n  const middle = document.createElement(\"div\");\n  const inner = document.createElement(\"div\");\n  const bold = document.createElement(\"strong\");\n  const button = document.createElement(\"a\");\n  // these classes exist since pydata-sphinx-theme v0.10.0\n  outer.classList = \"bd-header-version-warning container-fluid\";\n  middle.classList = \"bd-header-announcement__content\";\n  inner.classList = \"sidebar-message\";\n  button.classList =\n    \"sd-btn sd-btn-danger sd-shadow-sm sd-text-wrap font-weight-bold ms-3 my-1 align-baseline\";\n  button.href = `${preferredURL}${DOCUMENTATION_OPTIONS.pagename}.html`;\n  button.innerText = \"Switch to stable version\";\n  button.onclick = checkPageExistsAndRedirect;\n  // add the version-dependent text\n  inner.innerText = \"This is documentation for \";\n  const isDev =\n    version.includes(\"dev\") ||\n    version.includes(\"rc\") ||\n    version.includes(\"pre\");\n  const newerThanPreferred =\n    versionsAreComparable && compare(version, preferredVersion, \">\");\n  if (isDev || newerThanPreferred) {\n    bold.innerText = \"an unstable development version\";\n  } else if (versionsAreComparable && compare(version, preferredVersion, \"<\")) {\n    bold.innerText = `an old version (${version})`;\n  } else if (!version) {\n    bold.innerText = \"an unknown version\"; // e.g., an empty string\n  } else {\n    bold.innerText = `version ${version}`;\n  }\n  outer.appendChild(middle);\n  middle.appendChild(inner);\n  inner.appendChild(bold);\n  inner.appendChild(document.createTextNode(\".\"));\n  inner.appendChild(button);\n  document.body.prepend(outer);\n}\n\n/*******************************************************************************\n * MutationObserver to move the ReadTheDocs button\n */\n\n/**\n * intercept the RTD flyout and place it in the rtd-footer-container if existing\n * if not it stays where on top of the page\n */\nfunction initRTDObserver() {\n  const mutatedCallback = (mutationList, observer) => {\n    mutationList.forEach((mutation) => {\n      // Check whether the mutation is for RTD, which will have a specific structure\n      if (mutation.addedNodes.length === 0) {\n        return;\n      }\n      if (mutation.addedNodes[0].data === undefined) {\n        return;\n      }\n      if (mutation.addedNodes[0].data.search(\"Inserted RTD Footer\") != -1) {\n        mutation.addedNodes.forEach((node) => {\n          document.getElementById(\"rtd-footer-container\").append(node);\n        });\n      }\n    });\n  };\n\n  const observer = new MutationObserver(mutatedCallback);\n  const config = { childList: true };\n  observer.observe(document.body, config);\n}\n\n// fetch the JSON version data (only once), then use it to populate the version\n// switcher and maybe show the version warning bar\nvar versionSwitcherBtns = document.querySelectorAll(\n  \".version-switcher__button\"\n);\nconst hasSwitcherMenu = versionSwitcherBtns.length > 0;\nconst hasVersionsJSON = DOCUMENTATION_OPTIONS.hasOwnProperty(\n  \"theme_switcher_json_url\"\n);\nconst wantsWarningBanner = DOCUMENTATION_OPTIONS.show_version_warning_banner;\n\nif (hasVersionsJSON && (hasSwitcherMenu || wantsWarningBanner)) {\n  const data = await fetchVersionSwitcherJSON(\n    DOCUMENTATION_OPTIONS.theme_switcher_json_url\n  );\n  populateVersionSwitcher(data, versionSwitcherBtns);\n  if (wantsWarningBanner) {\n    showVersionWarningBanner(data);\n  }\n}\n\n/*******************************************************************************\n * Call functions after document loading.\n */\n\ndocumentReady(addModeListener);\ndocumentReady(scrollToActive);\ndocumentReady(addTOCInteractivity);\ndocumentReady(setupSearchButtons);\ndocumentReady(initRTDObserver);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","// startup\n// Load entry module and return exports\n// This entry module used 'module' so it can't be inlined\nvar __webpack_exports__ = 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\ No newline at end of file
diff --git a/stable/_static/searchtools.js b/stable/_static/searchtools.js
index e89e34d4..7918c3fa 100644
--- a/stable/_static/searchtools.js
+++ b/stable/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -57,12 +57,12 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
   const [docName, title, anchor, descr, score, _filename] = item;
 
@@ -75,20 +75,24 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
@@ -97,6 +101,9 @@ const _displayItem = (item, searchTerms) => {
           listItem.appendChild(
             Search.makeSearchSummary(data, searchTerms)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -115,14 +122,15 @@ const _finishSearch = (resultCount) => {
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
@@ -360,7 +368,7 @@ const Search = {
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
diff --git a/stable/_static/sg_gallery-binder.css b/stable/_static/sg_gallery-binder.css
index a33aa420..420005d2 100644
--- a/stable/_static/sg_gallery-binder.css
+++ b/stable/_static/sg_gallery-binder.css
@@ -4,3 +4,8 @@ div.binder-badge {
   margin: 1em auto;
   vertical-align: middle;
 }
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/stable/_static/sg_gallery-dataframe.css b/stable/_static/sg_gallery-dataframe.css
index 25be7309..fac74c43 100644
--- a/stable/_static/sg_gallery-dataframe.css
+++ b/stable/_static/sg_gallery-dataframe.css
@@ -19,6 +19,7 @@ table.dataframe {
   color: var(--sg-text-color);
   font-size: 12px;
   table-layout: fixed;
+  width: auto;
 }
 table.dataframe thead {
   border-bottom: 1px solid var(--sg-text-color);
diff --git a/stable/_static/sphinx_highlight.js b/stable/_static/sphinx_highlight.js
index aae669d7..8a96c69a 100644
--- a/stable/_static/sphinx_highlight.js
+++ b/stable/_static/sphinx_highlight.js
@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/stable/_static/style.css b/stable/_static/style.css
index 8351ae57..83cdd28d 100644
--- a/stable/_static/style.css
+++ b/stable/_static/style.css
@@ -115,7 +115,7 @@ aside.sidebar {
 
 /* **************************************************************** homepage */
 img.logo {
-    max-width: 360px;
+    max-width: 500px;
     width: 100%;
 }
 
@@ -155,46 +155,6 @@ h5.card-header::before {
     font-weight: 600;
 }
 
-/* ***************************************************** front page carousel */
-div.frontpage-gallery {
-    overflow: hidden;
-    height: 180px;
-    justify-content: center;
-}
-div.frontpage-gallery a {
-    text-decoration: none;
-    color: rgb(var(--pst-color-text-base));
-}
-div.frontpage-gallery img.card-img {
-    transform: scale(1.8);
-    transform-origin: 40% 20%;
-    opacity: 0.2;
-    transition: 400ms ease-out;
-}
-div.frontpage-gallery:hover img.card-img {
-    transform: scale(1.2);
-    opacity: 1.0;
-    transition: 400ms ease-out;
-}
-div.frontpage-gallery .fadeout {
-    opacity: 1.0;
-    transition: 200ms linear;
-}
-div.frontpage-gallery:hover .fadeout {
-    opacity: 0.0;
-    transition: 200ms linear;
-}
-div.card img.institution {
-    background-color: #fffe;
-}
-div.card {
-    border: 1px solid var(--pst-color-border-rgba);
-    background-color: rgb(var(--pst-color-background));
-}
-.card-header {
-    border-bottom-color: var(--pst-color-border-rgba);
-    background-color: var(--pst-color-background-rgba);
-}
 /* ********************************************* sphinx-design compatibility */
 .sd-dropdown {
     border: 1px solid var(--pst-color-border-rgba) !important;
diff --git a/stable/_static/styles/bootstrap.css b/stable/_static/styles/bootstrap.css
new file mode 100644
index 00000000..82b03c0c
--- /dev/null
+++ b/stable/_static/styles/bootstrap.css
@@ -0,0 +1,6 @@
+/*!
+ * Bootstrap  v5.3.2 (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */:root,[data-bs-theme=light]{--bs-blue:#0d6efd;--bs-indigo:#6610f2;--bs-purple:#6f42c1;--bs-pink:#d63384;--bs-red:#dc3545;--bs-orange:#fd7e14;--bs-yellow:#ffc107;--bs-green:#198754;--bs-teal:#20c997;--bs-cyan:#0dcaf0;--bs-black:#000;--bs-white:#fff;--bs-gray:#6c757d;--bs-gray-dark:#343a40;--bs-gray-100:#f8f9fa;--bs-gray-200:#e9ecef;--bs-gray-300:#dee2e6;--bs-gray-400:#ced4da;--bs-gray-500:#adb5bd;--bs-gray-600:#6c757d;--bs-gray-700:#495057;--bs-gray-800:#343a40;--bs-gray-900:#212529;--bs-primary:#0d6efd;--bs-secondary:#6c757d;--bs-success:#198754;--bs-info:#0dcaf0;--bs-warning:#ffc107;--bs-danger:#dc3545;--bs-light:#f8f9fa;--bs-dark:#212529;--bs-primary-rgb:13,110,253;--bs-secondary-rgb:108,117,125;--bs-success-rgb:25,135,84;--bs-info-rgb:13,202,240;--bs-warning-rgb:255,193,7;--bs-danger-rgb:220,53,69;--bs-light-rgb:248,249,250;--bs-dark-rgb:33,37,41;--bs-primary-text-emphasis:#052c65;--bs-secondary-text-emphasis:#2b2f32;--bs-success-text-emphasis:#0a3622;--bs-info-text-emphasis:#055160;--bs-warning-text-emphasis:#664d03;--bs-danger-text-emphasis:#58151c;--bs-light-text-emphasis:#495057;--bs-dark-text-emphasis:#495057;--bs-primary-bg-subtle:#cfe2ff;--bs-secondary-bg-subtle:#e2e3e5;--bs-success-bg-subtle:#d1e7dd;--bs-info-bg-subtle:#cff4fc;--bs-warning-bg-subtle:#fff3cd;--bs-danger-bg-subtle:#f8d7da;--bs-light-bg-subtle:#fcfcfd;--bs-dark-bg-subtle:#ced4da;--bs-primary-border-subtle:#9ec5fe;--bs-secondary-border-subtle:#c4c8cb;--bs-success-border-subtle:#a3cfbb;--bs-info-border-subtle:#9eeaf9;--bs-warning-border-subtle:#ffe69c;--bs-danger-border-subtle:#f1aeb5;--bs-light-border-subtle:#e9ecef;--bs-dark-border-subtle:#adb5bd;--bs-white-rgb:255,255,255;--bs-black-rgb:0,0,0;--bs-font-sans-serif:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--bs-font-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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.accordion-collapse{border-bottom-left-radius:var(--bs-accordion-border-radius);border-bottom-right-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-left:0;border-radius:0;border-right:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button:after{--bs-accordion-btn-icon:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3E%3Cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 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var(--bs-breadcrumb-padding-x)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item:before{color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider,"/");float:left;padding-right:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;list-style:none;padding-left:0}.page-link{background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);color:var(--bs-pagination-color);display:block;font-size:var(--bs-pagination-font-size);padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);position:relative;text-decoration:none;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s 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.page-link{border-bottom-right-radius:var(--bs-pagination-border-radius);border-top-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);border-radius:var(--bs-badge-border-radius);color:var(--bs-badge-color);display:inline-block;font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius);color:var(--bs-alert-color);margin-bottom:var(--bs-alert-margin-bottom);padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);position:relative}.alert-heading{color:inherit}.alert-link{color:var(--bs-alert-link-color);font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{padding:1.25rem 1rem;position:absolute;right:0;top:0;z-index:2}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius);display:flex;font-size:var(--bs-progress-font-size);height:var(--bs-progress-height);overflow:hidden}.progress-bar{background-color:var(--bs-progress-bar-bg);color:var(--bs-progress-bar-color);display:flex;flex-direction:column;justify-content:center;overflow:hidden;text-align:center;transition:var(--bs-progress-bar-transition);white-space:nowrap}@media(prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,hsla(0,0%,100%,.15) 25%,transparent 0,transparent 50%,hsla(0,0%,100%,.15) 0,hsla(0,0%,100%,.15) 75%,transparent 0,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:progress-bar-stripes 1s linear infinite}@media(prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;border-radius:var(--bs-list-group-border-radius);display:flex;flex-direction:column;margin-bottom:0;padding-left:0}.list-group-numbered{counter-reset:section;list-style-type:none}.list-group-numbered>.list-group-item:before{content:counters(section,".") ". ";counter-increment:section}.list-group-item-action{color:var(--bs-list-group-action-color);text-align:inherit;width:100%}.list-group-item-action:focus,.list-group-item-action:hover{background-color:var(--bs-list-group-action-hover-bg);color:var(--bs-list-group-action-hover-color);text-decoration:none;z-index:1}.list-group-item-action:active{background-color:var(--bs-list-group-action-active-bg);color:var(--bs-list-group-action-active-color)}.list-group-item{background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color);color:var(--bs-list-group-color);display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);position:relative;text-decoration:none}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-left-radius:inherit;border-bottom-right-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{background-color:var(--bs-list-group-disabled-bg);color:var(--bs-list-group-disabled-color);pointer-events:none}.list-group-item.active{background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color);color:var(--bs-list-group-active-color);z-index:2}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{border-top-width:var(--bs-list-group-border-width);margin-top:calc(var(--bs-list-group-border-width)*-1)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}@media(min-width:540px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:720px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-md>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}@media(min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-bottom-left-radius:0;border-top-right-radius:var(--bs-list-group-border-radius)}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-left-width:0;border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{border-left-width:var(--bs-list-group-border-width);margin-left:calc(var(--bs-list-group-border-width)*-1)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'%3E%3Cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3E%3C/svg%3E");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(13,110,253,.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;box-sizing:content-box;height:1em;opacity:var(--bs-btn-close-opacity);padding:.25em;width:1em}.btn-close,.btn-close:hover{color:var(--bs-btn-close-color)}.btn-close:hover{opacity:var(--bs-btn-close-hover-opacity);text-decoration:none}.btn-close:focus{box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity);outline:0}.btn-close.disabled,.btn-close:disabled{opacity:var(--bs-btn-close-disabled-opacity);pointer-events:none;user-select:none}.btn-close-white,[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb),0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);background-clip:padding-box;background-color:var(--bs-toast-bg);border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);border-radius:var(--bs-toast-border-radius);box-shadow:var(--bs-toast-box-shadow);color:var(--bs-toast-color);font-size:var(--bs-toast-font-size);max-width:100%;pointer-events:auto;width:var(--bs-toast-max-width)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex:1090;max-width:100%;pointer-events:none;position:absolute;width:max-content;z-index:var(--bs-toast-zindex)}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{align-items:center;background-clip:padding-box;background-color:var(--bs-toast-header-bg);border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));color:var(--bs-toast-header-color);display:flex;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x)}.toast-header .btn-close{margin-left:var(--bs-toast-padding-x);margin-right:calc(var(--bs-toast-padding-x)*-.5)}.toast-body{word-wrap:break-word;padding:var(--bs-toast-padding-x)}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);display:none;height:100%;left:0;outline:0;overflow-x:hidden;overflow-y:auto;position:fixed;top:0;width:100%;z-index:var(--bs-modal-zindex)}.modal-dialog{margin:var(--bs-modal-margin);pointer-events:none;position:relative;width:auto}.modal.fade .modal-dialog{transform:translateY(-50px);transition:transform .3s ease-out}@media(prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{align-items:center;display:flex;min-height:calc(100% - 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0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:rgba(0,0,0,0)}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: var(--pst-color-surface);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: var(--pst-color-surface);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;flex-wrap:wrap;justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width)*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;align-items:flex-start;justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width)*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: 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1.25rem;--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25rem;--bs-navbar-toggler-padding-x: 0.75rem;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%2833, 37, 41, 0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\");--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.15);--bs-navbar-toggler-border-radius: var(--bs-border-radius);--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;padding:var(--bs-navbar-padding-y) 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.offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgba(255, 255, 255, 0.55);--bs-navbar-hover-color: rgba(255, 255, 255, 0.75);--bs-navbar-disabled-color: rgba(255, 255, 255, 0.25);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #fff;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(255, 255, 255, 0.1);--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255, 255, 255, 0.55%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e\")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 540px){.card-group{display:flex;flex-flow:row wrap}.card-group>.card{flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: var(--bs-body-color);--bs-accordion-bg: var(--bs-body-bg);--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: var(--bs-border-color);--bs-accordion-border-width: var(--bs-border-width);--bs-accordion-border-radius: var(--bs-border-radius);--bs-accordion-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: var(--bs-body-color);--bs-accordion-btn-bg: var(--bs-accordion-bg);--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:\"\";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-active-icon: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, \"/\") /* rtl: var(--bs-breadcrumb-divider, \"/\") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width)*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 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960px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static 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!important}.pt-lg-0{padding-top:0 !important}.pt-lg-1{padding-top:.25rem !important}.pt-lg-2{padding-top:.5rem !important}.pt-lg-3{padding-top:1rem !important}.pt-lg-4{padding-top:1.5rem !important}.pt-lg-5{padding-top:3rem !important}.pe-lg-0{padding-right:0 !important}.pe-lg-1{padding-right:.25rem !important}.pe-lg-2{padding-right:.5rem !important}.pe-lg-3{padding-right:1rem !important}.pe-lg-4{padding-right:1.5rem !important}.pe-lg-5{padding-right:3rem !important}.pb-lg-0{padding-bottom:0 !important}.pb-lg-1{padding-bottom:.25rem !important}.pb-lg-2{padding-bottom:.5rem !important}.pb-lg-3{padding-bottom:1rem !important}.pb-lg-4{padding-bottom:1.5rem !important}.pb-lg-5{padding-bottom:3rem !important}.ps-lg-0{padding-left:0 !important}.ps-lg-1{padding-left:.25rem !important}.ps-lg-2{padding-left:.5rem !important}.ps-lg-3{padding-left:1rem !important}.ps-lg-4{padding-left:1.5rem !important}.ps-lg-5{padding-left:3rem !important}.gap-lg-0{gap:0 !important}.gap-lg-1{gap:.25rem !important}.gap-lg-2{gap:.5rem !important}.gap-lg-3{gap:1rem !important}.gap-lg-4{gap:1.5rem !important}.gap-lg-5{gap:3rem !important}.row-gap-lg-0{row-gap:0 !important}.row-gap-lg-1{row-gap:.25rem !important}.row-gap-lg-2{row-gap:.5rem !important}.row-gap-lg-3{row-gap:1rem !important}.row-gap-lg-4{row-gap:1.5rem !important}.row-gap-lg-5{row-gap:3rem !important}.column-gap-lg-0{column-gap:0 !important}.column-gap-lg-1{column-gap:.25rem !important}.column-gap-lg-2{column-gap:.5rem !important}.column-gap-lg-3{column-gap:1rem !important}.column-gap-lg-4{column-gap:1.5rem !important}.column-gap-lg-5{column-gap:3rem !important}.text-lg-start{text-align:left !important}.text-lg-end{text-align:right !important}.text-lg-center{text-align:center !important}}@media(min-width: 1200px){.float-xl-start{float:left !important}.float-xl-end{float:right !important}.float-xl-none{float:none !important}.object-fit-xl-contain{object-fit:contain !important}.object-fit-xl-cover{object-fit:cover !important}.object-fit-xl-fill{object-fit:fill !important}.object-fit-xl-scale{object-fit:scale-down !important}.object-fit-xl-none{object-fit:none !important}.d-xl-inline{display:inline !important}.d-xl-inline-block{display:inline-block !important}.d-xl-block{display:block !important}.d-xl-grid{display:grid !important}.d-xl-inline-grid{display:inline-grid !important}.d-xl-table{display:table !important}.d-xl-table-row{display:table-row !important}.d-xl-table-cell{display:table-cell !important}.d-xl-flex{display:flex !important}.d-xl-inline-flex{display:inline-flex !important}.d-xl-none{display:none !important}.flex-xl-fill{flex:1 1 auto !important}.flex-xl-row{flex-direction:row !important}.flex-xl-column{flex-direction:column !important}.flex-xl-row-reverse{flex-direction:row-reverse !important}.flex-xl-column-reverse{flex-direction:column-reverse !important}.flex-xl-grow-0{flex-grow:0 !important}.flex-xl-grow-1{flex-grow:1 !important}.flex-xl-shrink-0{flex-shrink:0 !important}.flex-xl-shrink-1{flex-shrink:1 !important}.flex-xl-wrap{flex-wrap:wrap !important}.flex-xl-nowrap{flex-wrap:nowrap !important}.flex-xl-wrap-reverse{flex-wrap:wrap-reverse !important}.justify-content-xl-start{justify-content:flex-start !important}.justify-content-xl-end{justify-content:flex-end !important}.justify-content-xl-center{justify-content:center !important}.justify-content-xl-between{justify-content:space-between !important}.justify-content-xl-around{justify-content:space-around !important}.justify-content-xl-evenly{justify-content:space-evenly !important}.align-items-xl-start{align-items:flex-start !important}.align-items-xl-end{align-items:flex-end !important}.align-items-xl-center{align-items:center !important}.align-items-xl-baseline{align-items:baseline !important}.align-items-xl-stretch{align-items:stretch !important}.align-content-xl-start{align-content:flex-start !important}.align-content-xl-end{align-content:flex-end !important}.align-content-xl-center{align-content:center !important}.align-content-xl-between{align-content:space-between !important}.align-content-xl-around{align-content:space-around !important}.align-content-xl-stretch{align-content:stretch !important}.align-self-xl-auto{align-self:auto !important}.align-self-xl-start{align-self:flex-start !important}.align-self-xl-end{align-self:flex-end !important}.align-self-xl-center{align-self:center !important}.align-self-xl-baseline{align-self:baseline !important}.align-self-xl-stretch{align-self:stretch !important}.order-xl-first{order:-1 !important}.order-xl-0{order:0 !important}.order-xl-1{order:1 !important}.order-xl-2{order:2 !important}.order-xl-3{order:3 !important}.order-xl-4{order:4 !important}.order-xl-5{order:5 !important}.order-xl-last{order:6 !important}.m-xl-0{margin:0 !important}.m-xl-1{margin:.25rem !important}.m-xl-2{margin:.5rem !important}.m-xl-3{margin:1rem !important}.m-xl-4{margin:1.5rem !important}.m-xl-5{margin:3rem !important}.m-xl-auto{margin:auto !important}.mx-xl-0{margin-right:0 !important;margin-left:0 !important}.mx-xl-1{margin-right:.25rem !important;margin-left:.25rem !important}.mx-xl-2{margin-right:.5rem !important;margin-left:.5rem !important}.mx-xl-3{margin-right:1rem !important;margin-left:1rem !important}.mx-xl-4{margin-right:1.5rem !important;margin-left:1.5rem !important}.mx-xl-5{margin-right:3rem !important;margin-left:3rem !important}.mx-xl-auto{margin-right:auto !important;margin-left:auto !important}.my-xl-0{margin-top:0 !important;margin-bottom:0 !important}.my-xl-1{margin-top:.25rem !important;margin-bottom:.25rem !important}.my-xl-2{margin-top:.5rem !important;margin-bottom:.5rem !important}.my-xl-3{margin-top:1rem !important;margin-bottom:1rem !important}.my-xl-4{margin-top:1.5rem !important;margin-bottom:1.5rem !important}.my-xl-5{margin-top:3rem !important;margin-bottom:3rem !important}.my-xl-auto{margin-top:auto !important;margin-bottom:auto !important}.mt-xl-0{margin-top:0 !important}.mt-xl-1{margin-top:.25rem !important}.mt-xl-2{margin-top:.5rem !important}.mt-xl-3{margin-top:1rem !important}.mt-xl-4{margin-top:1.5rem !important}.mt-xl-5{margin-top:3rem !important}.mt-xl-auto{margin-top:auto !important}.me-xl-0{margin-right:0 !important}.me-xl-1{margin-right:.25rem !important}.me-xl-2{margin-right:.5rem !important}.me-xl-3{margin-right:1rem !important}.me-xl-4{margin-right:1.5rem !important}.me-xl-5{margin-right:3rem !important}.me-xl-auto{margin-right:auto !important}.mb-xl-0{margin-bottom:0 !important}.mb-xl-1{margin-bottom:.25rem !important}.mb-xl-2{margin-bottom:.5rem !important}.mb-xl-3{margin-bottom:1rem !important}.mb-xl-4{margin-bottom:1.5rem !important}.mb-xl-5{margin-bottom:3rem !important}.mb-xl-auto{margin-bottom:auto !important}.ms-xl-0{margin-left:0 !important}.ms-xl-1{margin-left:.25rem !important}.ms-xl-2{margin-left:.5rem !important}.ms-xl-3{margin-left:1rem !important}.ms-xl-4{margin-left:1.5rem !important}.ms-xl-5{margin-left:3rem !important}.ms-xl-auto{margin-left:auto !important}.p-xl-0{padding:0 !important}.p-xl-1{padding:.25rem !important}.p-xl-2{padding:.5rem !important}.p-xl-3{padding:1rem !important}.p-xl-4{padding:1.5rem !important}.p-xl-5{padding:3rem !important}.px-xl-0{padding-right:0 !important;padding-left:0 !important}.px-xl-1{padding-right:.25rem !important;padding-left:.25rem !important}.px-xl-2{padding-right:.5rem !important;padding-left:.5rem !important}.px-xl-3{padding-right:1rem !important;padding-left:1rem !important}.px-xl-4{padding-right:1.5rem !important;padding-left:1.5rem !important}.px-xl-5{padding-right:3rem !important;padding-left:3rem !important}.py-xl-0{padding-top:0 !important;padding-bottom:0 !important}.py-xl-1{padding-top:.25rem !important;padding-bottom:.25rem !important}.py-xl-2{padding-top:.5rem !important;padding-bottom:.5rem !important}.py-xl-3{padding-top:1rem !important;padding-bottom:1rem !important}.py-xl-4{padding-top:1.5rem !important;padding-bottom:1.5rem !important}.py-xl-5{padding-top:3rem !important;padding-bottom:3rem !important}.pt-xl-0{padding-top:0 !important}.pt-xl-1{padding-top:.25rem !important}.pt-xl-2{padding-top:.5rem !important}.pt-xl-3{padding-top:1rem !important}.pt-xl-4{padding-top:1.5rem !important}.pt-xl-5{padding-top:3rem !important}.pe-xl-0{padding-right:0 !important}.pe-xl-1{padding-right:.25rem !important}.pe-xl-2{padding-right:.5rem !important}.pe-xl-3{padding-right:1rem !important}.pe-xl-4{padding-right:1.5rem !important}.pe-xl-5{padding-right:3rem !important}.pb-xl-0{padding-bottom:0 !important}.pb-xl-1{padding-bottom:.25rem !important}.pb-xl-2{padding-bottom:.5rem !important}.pb-xl-3{padding-bottom:1rem !important}.pb-xl-4{padding-bottom:1.5rem !important}.pb-xl-5{padding-bottom:3rem !important}.ps-xl-0{padding-left:0 !important}.ps-xl-1{padding-left:.25rem !important}.ps-xl-2{padding-left:.5rem !important}.ps-xl-3{padding-left:1rem !important}.ps-xl-4{padding-left:1.5rem !important}.ps-xl-5{padding-left:3rem !important}.gap-xl-0{gap:0 !important}.gap-xl-1{gap:.25rem !important}.gap-xl-2{gap:.5rem !important}.gap-xl-3{gap:1rem !important}.gap-xl-4{gap:1.5rem !important}.gap-xl-5{gap:3rem !important}.row-gap-xl-0{row-gap:0 !important}.row-gap-xl-1{row-gap:.25rem !important}.row-gap-xl-2{row-gap:.5rem !important}.row-gap-xl-3{row-gap:1rem !important}.row-gap-xl-4{row-gap:1.5rem !important}.row-gap-xl-5{row-gap:3rem !important}.column-gap-xl-0{column-gap:0 !important}.column-gap-xl-1{column-gap:.25rem !important}.column-gap-xl-2{column-gap:.5rem !important}.column-gap-xl-3{column-gap:1rem !important}.column-gap-xl-4{column-gap:1.5rem !important}.column-gap-xl-5{column-gap:3rem !important}.text-xl-start{text-align:left !important}.text-xl-end{text-align:right !important}.text-xl-center{text-align:center !important}}@media(min-width: 1200px){.fs-1{font-size:2.5rem !important}.fs-2{font-size:2rem !important}.fs-3{font-size:1.75rem !important}.fs-4{font-size:1.5rem !important}}@media print{.d-print-inline{display:inline !important}.d-print-inline-block{display:inline-block !important}.d-print-block{display:block !important}.d-print-grid{display:grid !important}.d-print-inline-grid{display:inline-grid !important}.d-print-table{display:table !important}.d-print-table-row{display:table-row !important}.d-print-table-cell{display:table-cell !important}.d-print-flex{display:flex !important}.d-print-inline-flex{display:inline-flex !important}.d-print-none{display:none !important}}","@mixin bsBanner($file) {\n  /*!\n   * Bootstrap #{$file} v5.3.2 (https://getbootstrap.com/)\n   * Copyright 2011-2023 The Bootstrap Authors\n   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n   */\n}\n",":root,\n[data-bs-theme=\"light\"] {\n  // Note: Custom variable values only support SassScript inside `#{}`.\n\n  // Colors\n  //\n  // Generate palettes for full colors, grays, and theme colors.\n\n  @each $color, $value in $colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $grays {\n    --#{$prefix}gray-#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors {\n    --#{$prefix}#{$color}: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-rgb {\n    --#{$prefix}#{$color}-rgb: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-text {\n    --#{$prefix}#{$color}-text-emphasis: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-bg-subtle {\n    --#{$prefix}#{$color}-bg-subtle: #{$value};\n  }\n\n  @each $color, $value in $theme-colors-border-subtle {\n    --#{$prefix}#{$color}-border-subtle: #{$value};\n  }\n\n  --#{$prefix}white-rgb: #{to-rgb($white)};\n  --#{$prefix}black-rgb: #{to-rgb($black)};\n\n  // Fonts\n\n  // Note: Use `inspect` for lists so that quoted items keep the quotes.\n  // See https://github.com/sass/sass/issues/2383#issuecomment-336349172\n  --#{$prefix}font-sans-serif: #{inspect($font-family-sans-serif)};\n  --#{$prefix}font-monospace: #{inspect($font-family-monospace)};\n  --#{$prefix}gradient: #{$gradient};\n\n  // Root and body\n  // scss-docs-start root-body-variables\n  @if $font-size-root != null {\n    --#{$prefix}root-font-size: #{$font-size-root};\n  }\n  --#{$prefix}body-font-family: #{inspect($font-family-base)};\n  @include rfs($font-size-base, --#{$prefix}body-font-size);\n  --#{$prefix}body-font-weight: #{$font-weight-base};\n  --#{$prefix}body-line-height: #{$line-height-base};\n  @if $body-text-align != null {\n    --#{$prefix}body-text-align: #{$body-text-align};\n  }\n\n  --#{$prefix}body-color: #{$body-color};\n  --#{$prefix}body-color-rgb: #{to-rgb($body-color)};\n  --#{$prefix}body-bg: #{$body-bg};\n  --#{$prefix}body-bg-rgb: #{to-rgb($body-bg)};\n\n  --#{$prefix}emphasis-color: #{$body-emphasis-color};\n  --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color)};\n\n  --#{$prefix}secondary-color: #{$body-secondary-color};\n  --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color)};\n  --#{$prefix}secondary-bg: #{$body-secondary-bg};\n  --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg)};\n\n  --#{$prefix}tertiary-color: #{$body-tertiary-color};\n  --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color)};\n  --#{$prefix}tertiary-bg: #{$body-tertiary-bg};\n  --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg)};\n  // scss-docs-end root-body-variables\n\n  --#{$prefix}heading-color: #{$headings-color};\n\n  --#{$prefix}link-color: #{$link-color};\n  --#{$prefix}link-color-rgb: #{to-rgb($link-color)};\n  --#{$prefix}link-decoration: #{$link-decoration};\n\n  --#{$prefix}link-hover-color: #{$link-hover-color};\n  --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color)};\n\n  @if $link-hover-decoration != null {\n    --#{$prefix}link-hover-decoration: #{$link-hover-decoration};\n  }\n\n  --#{$prefix}code-color: #{$code-color};\n  --#{$prefix}highlight-color: #{$mark-color};\n  --#{$prefix}highlight-bg: #{$mark-bg};\n\n  // scss-docs-start root-border-var\n  --#{$prefix}border-width: #{$border-width};\n  --#{$prefix}border-style: #{$border-style};\n  --#{$prefix}border-color: #{$border-color};\n  --#{$prefix}border-color-translucent: #{$border-color-translucent};\n\n  --#{$prefix}border-radius: #{$border-radius};\n  --#{$prefix}border-radius-sm: #{$border-radius-sm};\n  --#{$prefix}border-radius-lg: #{$border-radius-lg};\n  --#{$prefix}border-radius-xl: #{$border-radius-xl};\n  --#{$prefix}border-radius-xxl: #{$border-radius-xxl};\n  --#{$prefix}border-radius-2xl: var(--#{$prefix}border-radius-xxl); // Deprecated in v5.3.0 for consistency\n  --#{$prefix}border-radius-pill: #{$border-radius-pill};\n  // scss-docs-end root-border-var\n\n  --#{$prefix}box-shadow: #{$box-shadow};\n  --#{$prefix}box-shadow-sm: #{$box-shadow-sm};\n  --#{$prefix}box-shadow-lg: #{$box-shadow-lg};\n  --#{$prefix}box-shadow-inset: #{$box-shadow-inset};\n\n  // Focus styles\n  // scss-docs-start root-focus-variables\n  --#{$prefix}focus-ring-width: #{$focus-ring-width};\n  --#{$prefix}focus-ring-opacity: #{$focus-ring-opacity};\n  --#{$prefix}focus-ring-color: #{$focus-ring-color};\n  // scss-docs-end root-focus-variables\n\n  // scss-docs-start root-form-validation-variables\n  --#{$prefix}form-valid-color: #{$form-valid-color};\n  --#{$prefix}form-valid-border-color: #{$form-valid-border-color};\n  --#{$prefix}form-invalid-color: #{$form-invalid-color};\n  --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color};\n  // scss-docs-end root-form-validation-variables\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark, true) {\n    color-scheme: dark;\n\n    // scss-docs-start root-dark-mode-vars\n    --#{$prefix}body-color: #{$body-color-dark};\n    --#{$prefix}body-color-rgb: #{to-rgb($body-color-dark)};\n    --#{$prefix}body-bg: #{$body-bg-dark};\n    --#{$prefix}body-bg-rgb: #{to-rgb($body-bg-dark)};\n\n    --#{$prefix}emphasis-color: #{$body-emphasis-color-dark};\n    --#{$prefix}emphasis-color-rgb: #{to-rgb($body-emphasis-color-dark)};\n\n    --#{$prefix}secondary-color: #{$body-secondary-color-dark};\n    --#{$prefix}secondary-color-rgb: #{to-rgb($body-secondary-color-dark)};\n    --#{$prefix}secondary-bg: #{$body-secondary-bg-dark};\n    --#{$prefix}secondary-bg-rgb: #{to-rgb($body-secondary-bg-dark)};\n\n    --#{$prefix}tertiary-color: #{$body-tertiary-color-dark};\n    --#{$prefix}tertiary-color-rgb: #{to-rgb($body-tertiary-color-dark)};\n    --#{$prefix}tertiary-bg: #{$body-tertiary-bg-dark};\n    --#{$prefix}tertiary-bg-rgb: #{to-rgb($body-tertiary-bg-dark)};\n\n    @each $color, $value in $theme-colors-text-dark {\n      --#{$prefix}#{$color}-text-emphasis: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-bg-subtle-dark {\n      --#{$prefix}#{$color}-bg-subtle: #{$value};\n    }\n\n    @each $color, $value in $theme-colors-border-subtle-dark {\n      --#{$prefix}#{$color}-border-subtle: #{$value};\n    }\n\n    --#{$prefix}heading-color: #{$headings-color-dark};\n\n    --#{$prefix}link-color: #{$link-color-dark};\n    --#{$prefix}link-hover-color: #{$link-hover-color-dark};\n    --#{$prefix}link-color-rgb: #{to-rgb($link-color-dark)};\n    --#{$prefix}link-hover-color-rgb: #{to-rgb($link-hover-color-dark)};\n\n    --#{$prefix}code-color: #{$code-color-dark};\n    --#{$prefix}highlight-color: #{$mark-color-dark};\n    --#{$prefix}highlight-bg: #{$mark-bg-dark};\n\n    --#{$prefix}border-color: #{$border-color-dark};\n    --#{$prefix}border-color-translucent: #{$border-color-translucent-dark};\n\n    --#{$prefix}form-valid-color: #{$form-valid-color-dark};\n    --#{$prefix}form-valid-border-color: #{$form-valid-border-color-dark};\n    --#{$prefix}form-invalid-color: #{$form-invalid-color-dark};\n    --#{$prefix}form-invalid-border-color: #{$form-invalid-border-color-dark};\n    // scss-docs-end root-dark-mode-vars\n  }\n}\n","// stylelint-disable scss/dimension-no-non-numeric-values\n\n// SCSS RFS mixin\n//\n// Automated responsive values for font sizes, paddings, margins and much more\n//\n// Licensed under MIT (https://github.com/twbs/rfs/blob/main/LICENSE)\n\n// Configuration\n\n// Base value\n$rfs-base-value: 1.25rem !default;\n$rfs-unit: rem !default;\n\n@if $rfs-unit != rem and $rfs-unit != px {\n  @error \"`#{$rfs-unit}` is not a valid unit for $rfs-unit. Use `px` or `rem`.\";\n}\n\n// Breakpoint at where values start decreasing if screen width is smaller\n$rfs-breakpoint: 1200px !default;\n$rfs-breakpoint-unit: px !default;\n\n@if $rfs-breakpoint-unit != px and $rfs-breakpoint-unit != em and $rfs-breakpoint-unit != rem {\n  @error \"`#{$rfs-breakpoint-unit}` is not a valid unit for $rfs-breakpoint-unit. Use `px`, `em` or `rem`.\";\n}\n\n// Resize values based on screen height and width\n$rfs-two-dimensional: false !default;\n\n// Factor of decrease\n$rfs-factor: 10 !default;\n\n@if type-of($rfs-factor) != number or $rfs-factor <= 1 {\n  @error \"`#{$rfs-factor}` is not a valid  $rfs-factor, it must be greater than 1.\";\n}\n\n// Mode. Possibilities: \"min-media-query\", \"max-media-query\"\n$rfs-mode: min-media-query !default;\n\n// Generate enable or disable classes. Possibilities: false, \"enable\" or \"disable\"\n$rfs-class: false !default;\n\n// 1 rem = $rfs-rem-value px\n$rfs-rem-value: 16 !default;\n\n// Safari iframe resize bug: https://github.com/twbs/rfs/issues/14\n$rfs-safari-iframe-resize-bug-fix: false !default;\n\n// Disable RFS by setting $enable-rfs to false\n$enable-rfs: true !default;\n\n// Cache $rfs-base-value unit\n$rfs-base-value-unit: unit($rfs-base-value);\n\n@function divide($dividend, $divisor, $precision: 10) {\n  $sign: if($dividend > 0 and $divisor > 0 or $dividend < 0 and $divisor < 0, 1, -1);\n  $dividend: abs($dividend);\n  $divisor: abs($divisor);\n  @if $dividend == 0 {\n    @return 0;\n  }\n  @if $divisor == 0 {\n    @error \"Cannot divide by 0\";\n  }\n  $remainder: $dividend;\n  $result: 0;\n  $factor: 10;\n  @while ($remainder > 0 and $precision >= 0) {\n    $quotient: 0;\n    @while ($remainder >= $divisor) {\n      $remainder: $remainder - $divisor;\n      $quotient: $quotient + 1;\n    }\n    $result: $result * 10 + $quotient;\n    $factor: $factor * .1;\n    $remainder: $remainder * 10;\n    $precision: $precision - 1;\n    @if ($precision < 0 and $remainder >= $divisor * 5) {\n      $result: $result + 1;\n    }\n  }\n  $result: $result * $factor * $sign;\n  $dividend-unit: unit($dividend);\n  $divisor-unit: unit($divisor);\n  $unit-map: (\n    \"px\": 1px,\n    \"rem\": 1rem,\n    \"em\": 1em,\n    \"%\": 1%\n  );\n  @if ($dividend-unit != $divisor-unit and map-has-key($unit-map, $dividend-unit)) {\n    $result: $result * map-get($unit-map, $dividend-unit);\n  }\n  @return $result;\n}\n\n// Remove px-unit from $rfs-base-value for calculations\n@if $rfs-base-value-unit == px {\n  $rfs-base-value: divide($rfs-base-value, $rfs-base-value * 0 + 1);\n}\n@else if $rfs-base-value-unit == rem {\n  $rfs-base-value: divide($rfs-base-value, divide($rfs-base-value * 0 + 1, $rfs-rem-value));\n}\n\n// Cache $rfs-breakpoint unit to prevent multiple calls\n$rfs-breakpoint-unit-cache: unit($rfs-breakpoint);\n\n// Remove unit from $rfs-breakpoint for calculations\n@if $rfs-breakpoint-unit-cache == px {\n  $rfs-breakpoint: divide($rfs-breakpoint, $rfs-breakpoint * 0 + 1);\n}\n@else if $rfs-breakpoint-unit-cache == rem or $rfs-breakpoint-unit-cache == \"em\" {\n  $rfs-breakpoint: divide($rfs-breakpoint, divide($rfs-breakpoint * 0 + 1, $rfs-rem-value));\n}\n\n// Calculate the media query value\n$rfs-mq-value: if($rfs-breakpoint-unit == px, #{$rfs-breakpoint}px, #{divide($rfs-breakpoint, $rfs-rem-value)}#{$rfs-breakpoint-unit});\n$rfs-mq-property-width: if($rfs-mode == max-media-query, max-width, min-width);\n$rfs-mq-property-height: if($rfs-mode == max-media-query, max-height, min-height);\n\n// Internal mixin used to determine which media query needs to be used\n@mixin _rfs-media-query {\n  @if $rfs-two-dimensional {\n    @if $rfs-mode == max-media-query {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}), (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n    @else {\n      @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) and (#{$rfs-mq-property-height}: #{$rfs-mq-value}) {\n        @content;\n      }\n    }\n  }\n  @else {\n    @media (#{$rfs-mq-property-width}: #{$rfs-mq-value}) {\n      @content;\n    }\n  }\n}\n\n// Internal mixin that adds disable classes to the selector if needed.\n@mixin _rfs-rule {\n  @if $rfs-class == disable and $rfs-mode == max-media-query {\n    // Adding an extra class increases specificity, which prevents the media query to override the property\n    &,\n    .disable-rfs &,\n    &.disable-rfs {\n      @content;\n    }\n  }\n  @else if $rfs-class == enable and $rfs-mode == min-media-query {\n    .enable-rfs &,\n    &.enable-rfs {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Internal mixin that adds enable classes to the selector if needed.\n@mixin _rfs-media-query-rule {\n\n  @if $rfs-class == enable {\n    @if $rfs-mode == min-media-query {\n      @content;\n    }\n\n    @include _rfs-media-query () {\n      .enable-rfs &,\n      &.enable-rfs {\n        @content;\n      }\n    }\n  }\n  @else {\n    @if $rfs-class == disable and $rfs-mode == min-media-query {\n      .disable-rfs &,\n      &.disable-rfs {\n        @content;\n      }\n    }\n    @include _rfs-media-query () {\n      @content;\n    }\n  }\n}\n\n// Helper function to get the formatted non-responsive value\n@function rfs-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    }\n    @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      @if $unit == px {\n        // Convert to rem if needed\n        $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $value * 0 + $rfs-rem-value)}rem, $value);\n      }\n      @else if $unit == rem {\n        // Convert to px if needed\n        $val: $val + \" \" + if($rfs-unit == px, #{divide($value, $value * 0 + 1) * $rfs-rem-value}px, $value);\n      } @else {\n        // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n        $val: $val + \" \" + $value;\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// Helper function to get the responsive value calculated by RFS\n@function rfs-fluid-value($values) {\n  // Convert to list\n  $values: if(type-of($values) != list, ($values,), $values);\n\n  $val: \"\";\n\n  // Loop over each value and calculate value\n  @each $value in $values {\n    @if $value == 0 {\n      $val: $val + \" 0\";\n    } @else {\n      // Cache $value unit\n      $unit: if(type-of($value) == \"number\", unit($value), false);\n\n      // If $value isn't a number (like inherit) or $value has a unit (not px or rem, like 1.5em) or $ is 0, just print the value\n      @if not $unit or $unit != px and $unit != rem {\n        $val: $val + \" \" + $value;\n      } @else {\n        // Remove unit from $value for calculations\n        $value: divide($value, $value * 0 + if($unit == px, 1, divide(1, $rfs-rem-value)));\n\n        // Only add the media query if the value is greater than the minimum value\n        @if abs($value) <= $rfs-base-value or not $enable-rfs {\n          $val: $val + \" \" + if($rfs-unit == rem, #{divide($value, $rfs-rem-value)}rem, #{$value}px);\n        }\n        @else {\n          // Calculate the minimum value\n          $value-min: $rfs-base-value + divide(abs($value) - $rfs-base-value, $rfs-factor);\n\n          // Calculate difference between $value and the minimum value\n          $value-diff: abs($value) - $value-min;\n\n          // Base value formatting\n          $min-width: if($rfs-unit == rem, #{divide($value-min, $rfs-rem-value)}rem, #{$value-min}px);\n\n          // Use negative value if needed\n          $min-width: if($value < 0, -$min-width, $min-width);\n\n          // Use `vmin` if two-dimensional is enabled\n          $variable-unit: if($rfs-two-dimensional, vmin, vw);\n\n          // Calculate the variable width between 0 and $rfs-breakpoint\n          $variable-width: #{divide($value-diff * 100, $rfs-breakpoint)}#{$variable-unit};\n\n          // Return the calculated value\n          $val: $val + \" calc(\" + $min-width + if($value < 0, \" - \", \" + \") + $variable-width + \")\";\n        }\n      }\n    }\n  }\n\n  // Remove first space\n  @return unquote(str-slice($val, 2));\n}\n\n// RFS mixin\n@mixin rfs($values, $property: font-size) {\n  @if $values != null {\n    $val: rfs-value($values);\n    $fluid-val: rfs-fluid-value($values);\n\n    // Do not print the media query if responsive & non-responsive values are the same\n    @if $val == $fluid-val {\n      #{$property}: $val;\n    }\n    @else {\n      @include _rfs-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $val, $fluid-val);\n\n        // Include safari iframe resize fix if needed\n        min-width: if($rfs-safari-iframe-resize-bug-fix, (0 * 1vw), null);\n      }\n\n      @include _rfs-media-query-rule () {\n        #{$property}: if($rfs-mode == max-media-query, $fluid-val, $val);\n      }\n    }\n  }\n}\n\n// Shorthand helper mixins\n@mixin font-size($value) {\n  @include rfs($value);\n}\n\n@mixin padding($value) {\n  @include rfs($value, padding);\n}\n\n@mixin padding-top($value) {\n  @include rfs($value, padding-top);\n}\n\n@mixin padding-right($value) {\n  @include rfs($value, padding-right);\n}\n\n@mixin padding-bottom($value) {\n  @include rfs($value, padding-bottom);\n}\n\n@mixin padding-left($value) {\n  @include rfs($value, padding-left);\n}\n\n@mixin margin($value) {\n  @include rfs($value, margin);\n}\n\n@mixin margin-top($value) {\n  @include rfs($value, margin-top);\n}\n\n@mixin margin-right($value) {\n  @include rfs($value, margin-right);\n}\n\n@mixin margin-bottom($value) {\n  @include rfs($value, margin-bottom);\n}\n\n@mixin margin-left($value) {\n  @include rfs($value, margin-left);\n}\n","// scss-docs-start color-mode-mixin\n@mixin color-mode($mode: light, $root: false) {\n  @if $color-mode-type == \"media-query\" {\n    @if $root == true {\n      @media (prefers-color-scheme: $mode) {\n        :root {\n          @content;\n        }\n      }\n    } @else {\n      @media (prefers-color-scheme: $mode) {\n        @content;\n      }\n    }\n  } @else {\n    [data-bs-theme=\"#{$mode}\"] {\n      @content;\n    }\n  }\n}\n// scss-docs-end color-mode-mixin\n","// stylelint-disable declaration-no-important, selector-no-qualifying-type, property-no-vendor-prefix\n\n\n// Reboot\n//\n// Normalization of HTML elements, manually forked from Normalize.css to remove\n// styles targeting irrelevant browsers while applying new styles.\n//\n// Normalize is licensed MIT. https://github.com/necolas/normalize.css\n\n\n// Document\n//\n// Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n\n*,\n*::before,\n*::after {\n  box-sizing: border-box;\n}\n\n\n// Root\n//\n// Ability to the value of the root font sizes, affecting the value of `rem`.\n// null by default, thus nothing is generated.\n\n:root {\n  @if $font-size-root != null {\n    @include font-size(var(--#{$prefix}root-font-size));\n  }\n\n  @if $enable-smooth-scroll {\n    @media (prefers-reduced-motion: no-preference) {\n      scroll-behavior: smooth;\n    }\n  }\n}\n\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Prevent adjustments of font size after orientation changes in iOS.\n// 4. Change the default tap highlight to be completely transparent in iOS.\n\n// scss-docs-start reboot-body-rules\nbody {\n  margin: 0; // 1\n  font-family: var(--#{$prefix}body-font-family);\n  @include font-size(var(--#{$prefix}body-font-size));\n  font-weight: var(--#{$prefix}body-font-weight);\n  line-height: var(--#{$prefix}body-line-height);\n  color: var(--#{$prefix}body-color);\n  text-align: var(--#{$prefix}body-text-align);\n  background-color: var(--#{$prefix}body-bg); // 2\n  -webkit-text-size-adjust: 100%; // 3\n  -webkit-tap-highlight-color: rgba($black, 0); // 4\n}\n// scss-docs-end reboot-body-rules\n\n\n// Content grouping\n//\n// 1. Reset Firefox's gray color\n\nhr {\n  margin: $hr-margin-y 0;\n  color: $hr-color; // 1\n  border: 0;\n  border-top: $hr-border-width solid $hr-border-color;\n  opacity: $hr-opacity;\n}\n\n\n// Typography\n//\n// 1. Remove top margins from headings\n//    By default, `<h1>`-`<h6>` all receive top and bottom margins. We nuke the top\n//    margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n  margin-top: 0; // 1\n  margin-bottom: $headings-margin-bottom;\n  font-family: $headings-font-family;\n  font-style: $headings-font-style;\n  font-weight: $headings-font-weight;\n  line-height: $headings-line-height;\n  color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n  @extend %heading;\n  @include font-size($h1-font-size);\n}\n\nh2 {\n  @extend %heading;\n  @include font-size($h2-font-size);\n}\n\nh3 {\n  @extend %heading;\n  @include font-size($h3-font-size);\n}\n\nh4 {\n  @extend %heading;\n  @include font-size($h4-font-size);\n}\n\nh5 {\n  @extend %heading;\n  @include font-size($h5-font-size);\n}\n\nh6 {\n  @extend %heading;\n  @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `<p>`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n  margin-top: 0;\n  margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n  text-decoration: underline dotted; // 1\n  cursor: help; // 2\n  text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n  margin-bottom: 1rem;\n  font-style: normal;\n  line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n  padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n  margin-top: 0;\n  margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n  margin-bottom: 0;\n}\n\ndt {\n  font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n  margin-bottom: .5rem;\n  margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n  margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n  font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n  @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n  padding: $mark-padding;\n  color: var(--#{$prefix}highlight-color);\n  background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n  position: relative;\n  @include font-size($sub-sup-font-size);\n  line-height: 0;\n  vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n  color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n  text-decoration: $link-decoration;\n\n  &:hover {\n    --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n    text-decoration: $link-hover-decoration;\n  }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n  &,\n  &:hover {\n    color: inherit;\n    text-decoration: none;\n  }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: $font-family-code;\n  @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n  display: block;\n  margin-top: 0; // 1\n  margin-bottom: 1rem; // 2\n  overflow: auto; // 3\n  @include font-size($code-font-size);\n  color: $pre-color;\n\n  // Account for some code outputs that place code tags in pre tags\n  code {\n    @include font-size(inherit);\n    color: inherit;\n    word-break: normal;\n  }\n}\n\ncode {\n  @include font-size($code-font-size);\n  color: var(--#{$prefix}code-color);\n  word-wrap: break-word;\n\n  // Streamline the style when inside anchors to avoid broken underline and more\n  a > & {\n    color: inherit;\n  }\n}\n\nkbd {\n  padding: $kbd-padding-y $kbd-padding-x;\n  @include font-size($kbd-font-size);\n  color: $kbd-color;\n  background-color: $kbd-bg;\n  @include border-radius($border-radius-sm);\n\n  kbd {\n    padding: 0;\n    @include font-size(1em);\n    font-weight: $nested-kbd-font-weight;\n  }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n  margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n  vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n  caption-side: bottom;\n  border-collapse: collapse;\n}\n\ncaption {\n  padding-top: $table-cell-padding-y;\n  padding-bottom: $table-cell-padding-y;\n  color: $table-caption-color;\n  text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `<td>` alignment by inheriting `text-align`.\n// 3. Fix alignment for Safari\n\nth {\n  font-weight: $table-th-font-weight; // 1\n  text-align: inherit; // 2\n  text-align: -webkit-match-parent; // 3\n}\n\nthead,\ntbody,\ntfoot,\ntr,\ntd,\nth {\n  border-color: inherit;\n  border-style: solid;\n  border-width: 0;\n}\n\n\n// Forms\n//\n// 1. Allow labels to use `margin` for spacing.\n\nlabel {\n  display: inline-block; // 1\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n// See https://github.com/twbs/bootstrap/issues/24093\n\nbutton {\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 0;\n}\n\n// Explicitly remove focus outline in Chromium when it shouldn't be\n// visible (e.g. as result of mouse click or touch tap). It already\n// should be doing this automatically, but seems to currently be\n// confused and applies its very visible two-tone outline anyway.\n\nbutton:focus:not(:focus-visible) {\n  outline: 0;\n}\n\n// 1. Remove the margin in Firefox and Safari\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n  margin: 0; // 1\n  font-family: inherit;\n  @include font-size(inherit);\n  line-height: inherit;\n}\n\n// Remove the inheritance of text transform in Firefox\nbutton,\nselect {\n  text-transform: none;\n}\n// Set the cursor for non-`<button>` buttons\n//\n// Details at https://github.com/twbs/bootstrap/pull/30562\n[role=\"button\"] {\n  cursor: pointer;\n}\n\nselect {\n  // Remove the inheritance of word-wrap in Safari.\n  // See https://github.com/twbs/bootstrap/issues/24990\n  word-wrap: normal;\n\n  // Undo the opacity change from Chrome\n  &:disabled {\n    opacity: 1;\n  }\n}\n\n// Remove the dropdown arrow only from text type inputs built with datalists in Chrome.\n// See https://stackoverflow.com/a/54997118\n\n[list]:not([type=\"date\"]):not([type=\"datetime-local\"]):not([type=\"month\"]):not([type=\"week\"]):not([type=\"time\"])::-webkit-calendar-picker-indicator {\n  display: none !important;\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n//    controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\n// 3. Opinionated: add \"hand\" cursor to non-disabled button elements.\n\nbutton,\n[type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button; // 2\n\n  @if $enable-button-pointers {\n    &:not(:disabled) {\n      cursor: pointer; // 3\n    }\n  }\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\n\n::-moz-focus-inner {\n  padding: 0;\n  border-style: none;\n}\n\n// 1. Textareas should really only resize vertically so they don't break their (horizontal) containers.\n\ntextarea {\n  resize: vertical; // 1\n}\n\n// 1. Browsers set a default `min-width: min-content;` on fieldsets,\n//    unlike e.g. `<div>`s, which have `min-width: 0;` by default.\n//    So we reset that to ensure fieldsets behave more like a standard block element.\n//    See https://github.com/twbs/bootstrap/issues/12359\n//    and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n// 2. Reset the default outline behavior of fieldsets so they don't affect page layout.\n\nfieldset {\n  min-width: 0; // 1\n  padding: 0; // 2\n  margin: 0; // 2\n  border: 0; // 2\n}\n\n// 1. By using `float: left`, the legend will behave like a block element.\n//    This way the border of a fieldset wraps around the legend if present.\n// 2. Fix wrapping bug.\n//    See https://github.com/twbs/bootstrap/issues/29712\n\nlegend {\n  float: left; // 1\n  width: 100%;\n  padding: 0;\n  margin-bottom: $legend-margin-bottom;\n  @include font-size($legend-font-size);\n  font-weight: $legend-font-weight;\n  line-height: inherit;\n\n  + * {\n    clear: left; // 2\n  }\n}\n\n// Fix height of inputs with a type of datetime-local, date, month, week, or time\n// See https://github.com/twbs/bootstrap/issues/18842\n\n::-webkit-datetime-edit-fields-wrapper,\n::-webkit-datetime-edit-text,\n::-webkit-datetime-edit-minute,\n::-webkit-datetime-edit-hour-field,\n::-webkit-datetime-edit-day-field,\n::-webkit-datetime-edit-month-field,\n::-webkit-datetime-edit-year-field {\n  padding: 0;\n}\n\n::-webkit-inner-spin-button {\n  height: auto;\n}\n\n// 1. This overrides the extra rounded corners on search inputs in iOS so that our\n//    `.form-control` class can properly style them. Note that this cannot simply\n//    be added to `.form-control` as it's not specific enough. For details, see\n//    https://github.com/twbs/bootstrap/issues/11586.\n// 2. Correct the outline style in Safari.\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; // 1\n  outline-offset: -2px; // 2\n}\n\n// 1. A few input types should stay LTR\n// See https://rtlstyling.com/posts/rtl-styling#form-inputs\n// 2. RTL only output\n// See https://rtlcss.com/learn/usage-guide/control-directives/#raw\n\n/* rtl:raw:\n[type=\"tel\"],\n[type=\"url\"],\n[type=\"email\"],\n[type=\"number\"] {\n  direction: ltr;\n}\n*/\n\n// Remove the inner padding in Chrome and Safari on macOS.\n\n::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n// Remove padding around color pickers in webkit browsers\n\n::-webkit-color-swatch-wrapper {\n  padding: 0;\n}\n\n\n// 1. Inherit font family and line height for file input buttons\n// 2. Correct the inability to style clickable types in iOS and Safari.\n\n::file-selector-button {\n  font: inherit; // 1\n  -webkit-appearance: button; // 2\n}\n\n// Correct element displays\n\noutput {\n  display: inline-block;\n}\n\n// Remove border from iframe\n\niframe {\n  border: 0;\n}\n\n// Summary\n//\n// 1. Add the correct display in all browsers\n\nsummary {\n  display: list-item; // 1\n  cursor: pointer;\n}\n\n\n// Progress\n//\n// Add the correct vertical alignment in Chrome, Firefox, and Opera.\n\nprogress {\n  vertical-align: baseline;\n}\n\n\n// Hidden attribute\n//\n// Always hide an element with the `hidden` HTML attribute.\n\n[hidden] {\n  display: none !important;\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n$accordion-button-focus-border-color:     $input-focus-border-color !default;\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-active-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n","// stylelint-disable property-disallowed-list\n// Single side border-radius\n\n// Helper function to replace negative values with 0\n@function valid-radius($radius) {\n  $return: ();\n  @each $value in $radius {\n    @if type-of($value) == number {\n      $return: append($return, max($value, 0));\n    } @else {\n      $return: append($return, $value);\n    }\n  }\n  @return $return;\n}\n\n// scss-docs-start border-radius-mixins\n@mixin border-radius($radius: $border-radius, $fallback-border-radius: false) {\n  @if $enable-rounded {\n    border-radius: valid-radius($radius);\n  }\n  @else if $fallback-border-radius != false {\n    border-radius: $fallback-border-radius;\n  }\n}\n\n@mixin border-top-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-left-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-top-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-top-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-end-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-right-radius: valid-radius($radius);\n  }\n}\n\n@mixin border-bottom-start-radius($radius: $border-radius) {\n  @if $enable-rounded {\n    border-bottom-left-radius: valid-radius($radius);\n  }\n}\n// scss-docs-end border-radius-mixins\n","//\n// Headings\n//\n.h1 {\n  @extend h1;\n}\n\n.h2 {\n  @extend h2;\n}\n\n.h3 {\n  @extend h3;\n}\n\n.h4 {\n  @extend h4;\n}\n\n.h5 {\n  @extend h5;\n}\n\n.h6 {\n  @extend h6;\n}\n\n\n.lead {\n  @include font-size($lead-font-size);\n  font-weight: $lead-font-weight;\n}\n\n// Type display classes\n@each $display, $font-size in $display-font-sizes {\n  .display-#{$display} {\n    @include font-size($font-size);\n    font-family: $display-font-family;\n    font-style: $display-font-style;\n    font-weight: $display-font-weight;\n    line-height: $display-line-height;\n  }\n}\n\n//\n// Emphasis\n//\n.small {\n  @extend small;\n}\n\n.mark {\n  @extend mark;\n}\n\n//\n// Lists\n//\n\n.list-unstyled {\n  @include list-unstyled();\n}\n\n// Inline turns list items into inline-block\n.list-inline {\n  @include list-unstyled();\n}\n.list-inline-item {\n  display: inline-block;\n\n  &:not(:last-child) {\n    margin-right: $list-inline-padding;\n  }\n}\n\n\n//\n// Misc\n//\n\n// Builds on `abbr`\n.initialism {\n  @include font-size($initialism-font-size);\n  text-transform: uppercase;\n}\n\n// Blockquotes\n.blockquote {\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-font-size);\n\n  > :last-child {\n    margin-bottom: 0;\n  }\n}\n\n.blockquote-footer {\n  margin-top: -$blockquote-margin-y;\n  margin-bottom: $blockquote-margin-y;\n  @include font-size($blockquote-footer-font-size);\n  color: $blockquote-footer-color;\n\n  &::before {\n    content: \"\\2014\\00A0\"; // em dash, nbsp\n  }\n}\n","// Lists\n\n// Unstyled keeps list items block level, just removes default browser padding and list-style\n@mixin list-unstyled {\n  padding-left: 0;\n  list-style: none;\n}\n","// Override bootstrap variables\n$spacer: 1rem;\n\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1400px,\n);\n\n$grid-breakpoints: (\n  xs: 0,\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1200px,\n);\n\n$dropdown-link-hover-bg: var(--pst-color-surface);\n// --pst-color-surface can also be assigned to the dark variant because it is\n// scoped to different values depending on light/dark theme\n$dropdown-dark-link-hover-bg: var(--pst-color-surface);\n$dropdown-link-active-bg: var(--pst-color-surface);\n$dropdown-dark-link-active-bg: var(--pst-color-surface);\n","// Responsive images (ensure images don't scale beyond their parents)\n//\n// This is purposefully opt-in via an explicit class rather than being the default for all `<img>`s.\n// We previously tried the \"images are responsive by default\" approach in Bootstrap v2,\n// and abandoned it in Bootstrap v3 because it breaks lots of third-party widgets (including Google Maps)\n// which weren't expecting the images within themselves to be involuntarily resized.\n// See also https://github.com/twbs/bootstrap/issues/18178\n.img-fluid {\n  @include img-fluid();\n}\n\n\n// Image thumbnails\n.img-thumbnail {\n  padding: $thumbnail-padding;\n  background-color: $thumbnail-bg;\n  border: $thumbnail-border-width solid $thumbnail-border-color;\n  @include border-radius($thumbnail-border-radius);\n  @include box-shadow($thumbnail-box-shadow);\n\n  // Keep them at most 100% wide\n  @include img-fluid();\n}\n\n//\n// Figures\n//\n\n.figure {\n  // Ensures the caption's text aligns with the image.\n  display: inline-block;\n}\n\n.figure-img {\n  margin-bottom: $spacer * .5;\n  line-height: 1;\n}\n\n.figure-caption {\n  @include font-size($figure-caption-font-size);\n  color: $figure-caption-color;\n}\n","// Image Mixins\n// - Responsive image\n// - Retina image\n\n\n// Responsive image\n//\n// Keep images from scaling beyond the width of their parents.\n\n@mixin img-fluid {\n  // Part 1: Set a maximum relative to the parent\n  max-width: 100%;\n  // Part 2: Override the height to auto, otherwise images will be stretched\n  // when setting a width and height attribute on the img element.\n  height: auto;\n}\n","// Container widths\n//\n// Set the container width, and override it for fixed navbars in media queries.\n\n@if $enable-container-classes {\n  // Single container class with breakpoint max-widths\n  .container,\n  // 100% wide container at all breakpoints\n  .container-fluid {\n    @include make-container();\n  }\n\n  // Responsive containers that are 100% wide until a breakpoint\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    .container-#{$breakpoint} {\n      @extend .container-fluid;\n    }\n\n    @include media-breakpoint-up($breakpoint, $grid-breakpoints) {\n      %responsive-container-#{$breakpoint} {\n        max-width: $container-max-width;\n      }\n\n      // Extend each breakpoint which is smaller or equal to the current breakpoint\n      $extend-breakpoint: true;\n\n      @each $name, $width in $grid-breakpoints {\n        @if ($extend-breakpoint) {\n          .container#{breakpoint-infix($name, $grid-breakpoints)} {\n            @extend %responsive-container-#{$breakpoint};\n          }\n\n          // Once the current breakpoint is reached, stop extending\n          @if ($breakpoint == $name) {\n            $extend-breakpoint: false;\n          }\n        }\n      }\n    }\n  }\n}\n","// Container mixins\n\n@mixin make-container($gutter: $container-padding-x) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  width: 100%;\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-right: auto;\n  margin-left: auto;\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n","// Row\n//\n// Rows contain your columns.\n\n:root {\n  @each $name, $value in $grid-breakpoints {\n    --#{$prefix}breakpoint-#{$name}: #{$value};\n  }\n}\n\n@if $enable-grid-classes {\n  .row {\n    @include make-row();\n\n    > * {\n      @include make-col-ready();\n    }\n  }\n}\n\n@if $enable-cssgrid {\n  .grid {\n    display: grid;\n    grid-template-rows: repeat(var(--#{$prefix}rows, 1), 1fr);\n    grid-template-columns: repeat(var(--#{$prefix}columns, #{$grid-columns}), 1fr);\n    gap: var(--#{$prefix}gap, #{$grid-gutter-width});\n\n    @include make-cssgrid();\n  }\n}\n\n\n// Columns\n//\n// Common styles for small and large grid columns\n\n@if $enable-grid-classes {\n  @include make-grid-columns();\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is and invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Basic Bootstrap table\n//\n\n.table {\n  // Reset needed for nesting tables\n  --#{$prefix}table-color-type: initial;\n  --#{$prefix}table-bg-type: initial;\n  --#{$prefix}table-color-state: initial;\n  --#{$prefix}table-bg-state: initial;\n  // End of reset\n  --#{$prefix}table-color: #{$table-color};\n  --#{$prefix}table-bg: #{$table-bg};\n  --#{$prefix}table-border-color: #{$table-border-color};\n  --#{$prefix}table-accent-bg: #{$table-accent-bg};\n  --#{$prefix}table-striped-color: #{$table-striped-color};\n  --#{$prefix}table-striped-bg: #{$table-striped-bg};\n  --#{$prefix}table-active-color: #{$table-active-color};\n  --#{$prefix}table-active-bg: #{$table-active-bg};\n  --#{$prefix}table-hover-color: #{$table-hover-color};\n  --#{$prefix}table-hover-bg: #{$table-hover-bg};\n\n  width: 100%;\n  margin-bottom: $spacer;\n  vertical-align: $table-cell-vertical-align;\n  border-color: var(--#{$prefix}table-border-color);\n\n  // Target th & td\n  // We need the child combinator to prevent styles leaking to nested tables which doesn't have a `.table` class.\n  // We use the universal selectors here to simplify the selector (else we would need 6 different selectors).\n  // Another advantage is that this generates less code and makes the selector less specific making it easier to override.\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y $table-cell-padding-x;\n    // Following the precept of cascades: https://codepen.io/miriamsuzanne/full/vYNgodb\n    color: var(--#{$prefix}table-color-state, var(--#{$prefix}table-color-type, var(--#{$prefix}table-color)));\n    background-color: var(--#{$prefix}table-bg);\n    border-bottom-width: $table-border-width;\n    box-shadow: inset 0 0 0 9999px var(--#{$prefix}table-bg-state, var(--#{$prefix}table-bg-type, var(--#{$prefix}table-accent-bg)));\n  }\n\n  > tbody {\n    vertical-align: inherit;\n  }\n\n  > thead {\n    vertical-align: bottom;\n  }\n}\n\n.table-group-divider {\n  border-top: calc(#{$table-border-width} * 2) solid $table-group-separator-color; // stylelint-disable-line function-disallowed-list\n}\n\n//\n// Change placement of captions with a class\n//\n\n.caption-top {\n  caption-side: top;\n}\n\n\n//\n// Condensed table w/ half padding\n//\n\n.table-sm {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    padding: $table-cell-padding-y-sm $table-cell-padding-x-sm;\n  }\n}\n\n\n// Border versions\n//\n// Add or remove borders all around the table and between all the columns.\n//\n// When borders are added on all sides of the cells, the corners can render odd when\n// these borders do not have the same color or if they are semi-transparent.\n// Therefor we add top and border bottoms to the `tr`s and left and right borders\n// to the `td`s or `th`s\n\n.table-bordered {\n  > :not(caption) > * {\n    border-width: $table-border-width 0;\n\n    // stylelint-disable-next-line selector-max-universal\n    > * {\n      border-width: 0 $table-border-width;\n    }\n  }\n}\n\n.table-borderless {\n  // stylelint-disable-next-line selector-max-universal\n  > :not(caption) > * > * {\n    border-bottom-width: 0;\n  }\n\n  > :not(:first-child) {\n    border-top-width: 0;\n  }\n}\n\n// Zebra-striping\n//\n// Default zebra-stripe styles (alternating gray and transparent backgrounds)\n\n// For rows\n.table-striped {\n  > tbody > tr:nth-of-type(#{$table-striped-order}) > * {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// For columns\n.table-striped-columns {\n  > :not(caption) > tr > :nth-child(#{$table-striped-columns-order}) {\n    --#{$prefix}table-color-type: var(--#{$prefix}table-striped-color);\n    --#{$prefix}table-bg-type: var(--#{$prefix}table-striped-bg);\n  }\n}\n\n// Active table\n//\n// The `.table-active` class can be added to highlight rows or cells\n\n.table-active {\n  --#{$prefix}table-color-state: var(--#{$prefix}table-active-color);\n  --#{$prefix}table-bg-state: var(--#{$prefix}table-active-bg);\n}\n\n// Hover effect\n//\n// Placed here since it has to come after the potential zebra striping\n\n.table-hover {\n  > tbody > tr:hover > * {\n    --#{$prefix}table-color-state: var(--#{$prefix}table-hover-color);\n    --#{$prefix}table-bg-state: var(--#{$prefix}table-hover-bg);\n  }\n}\n\n\n// Table variants\n//\n// Table variants set the table cell backgrounds, border colors\n// and the colors of the striped, hovered & active tables\n\n@each $color, $value in $table-variants {\n  @include table-variant($color, $value);\n}\n\n// Responsive tables\n//\n// Generate series of `.table-responsive-*` classes for configuring the screen\n// size of where your table will overflow.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n  @include media-breakpoint-down($breakpoint) {\n    .table-responsive#{$infix} {\n      overflow-x: auto;\n      -webkit-overflow-scrolling: touch;\n    }\n  }\n}\n","// scss-docs-start table-variant\n@mixin table-variant($state, $background) {\n  .table-#{$state} {\n    $color: color-contrast(opaque($body-bg, $background));\n    $hover-bg: mix($color, $background, percentage($table-hover-bg-factor));\n    $striped-bg: mix($color, $background, percentage($table-striped-bg-factor));\n    $active-bg: mix($color, $background, percentage($table-active-bg-factor));\n    $table-border-color: mix($color, $background, percentage($table-border-factor));\n\n    --#{$prefix}table-color: #{$color};\n    --#{$prefix}table-bg: #{$background};\n    --#{$prefix}table-border-color: #{$table-border-color};\n    --#{$prefix}table-striped-bg: #{$striped-bg};\n    --#{$prefix}table-striped-color: #{color-contrast($striped-bg)};\n    --#{$prefix}table-active-bg: #{$active-bg};\n    --#{$prefix}table-active-color: #{color-contrast($active-bg)};\n    --#{$prefix}table-hover-bg: #{$hover-bg};\n    --#{$prefix}table-hover-color: #{color-contrast($hover-bg)};\n\n    color: var(--#{$prefix}table-color);\n    border-color: var(--#{$prefix}table-border-color);\n  }\n}\n// scss-docs-end table-variant\n","//\n// Labels\n//\n\n.form-label {\n  margin-bottom: $form-label-margin-bottom;\n  @include font-size($form-label-font-size);\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  color: $form-label-color;\n}\n\n// For use with horizontal and inline forms, when you need the label (or legend)\n// text to align with the form controls.\n.col-form-label {\n  padding-top: add($input-padding-y, $input-border-width);\n  padding-bottom: add($input-padding-y, $input-border-width);\n  margin-bottom: 0; // Override the `<legend>` default\n  @include font-size(inherit); // Override the `<legend>` default\n  font-style: $form-label-font-style;\n  font-weight: $form-label-font-weight;\n  line-height: $input-line-height;\n  color: $form-label-color;\n}\n\n.col-form-label-lg {\n  padding-top: add($input-padding-y-lg, $input-border-width);\n  padding-bottom: add($input-padding-y-lg, $input-border-width);\n  @include font-size($input-font-size-lg);\n}\n\n.col-form-label-sm {\n  padding-top: add($input-padding-y-sm, $input-border-width);\n  padding-bottom: add($input-padding-y-sm, $input-border-width);\n  @include font-size($input-font-size-sm);\n}\n","//\n// Form text\n//\n\n.form-text {\n  margin-top: $form-text-margin-top;\n  @include font-size($form-text-font-size);\n  font-style: $form-text-font-style;\n  font-weight: $form-text-font-weight;\n  color: $form-text-color;\n}\n","//\n// General form controls (plus a few specific high-level interventions)\n//\n\n.form-control {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y $input-padding-x;\n  font-family: $input-font-family;\n  @include font-size($input-font-size);\n  font-weight: $input-font-weight;\n  line-height: $input-line-height;\n  color: $input-color;\n  appearance: none; // Fix appearance for date inputs in Safari\n  background-color: $input-bg;\n  background-clip: padding-box;\n  border: $input-border-width solid $input-border-color;\n\n  // Note: This has no effect on <select>s in some browsers, due to the limited stylability of `<select>`s in CSS.\n  @include border-radius($input-border-radius, 0);\n\n  @include box-shadow($input-box-shadow);\n  @include transition($input-transition);\n\n  &[type=\"file\"] {\n    overflow: hidden; // prevent pseudo element button overlap\n\n    &:not(:disabled):not([readonly]) {\n      cursor: pointer;\n    }\n  }\n\n  // Customize the `:focus` state to imitate native WebKit styles.\n  &:focus {\n    color: $input-focus-color;\n    background-color: $input-focus-bg;\n    border-color: $input-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($input-box-shadow, $input-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $input-focus-box-shadow;\n    }\n  }\n\n  &::-webkit-date-and-time-value {\n    // On Android Chrome, form-control's \"width: 100%\" makes the input width too small\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    //\n    // On iOS Safari, form-control's \"appearance: none\" + \"width: 100%\" makes the input width too small\n    // Tested under iOS 16.2 / Safari 16.2\n    min-width: 85px; // Seems to be a good minimum safe width\n\n    // Add some height to date inputs on iOS\n    // https://github.com/twbs/bootstrap/issues/23307\n    // TODO: we can remove this workaround once https://bugs.webkit.org/show_bug.cgi?id=198959 is resolved\n    // Multiply line-height by 1em if it has no unit\n    height: if(unit($input-line-height) == \"\", $input-line-height * 1em, $input-line-height);\n\n    // Android Chrome type=\"date\" is taller than the other inputs\n    // because of \"margin: 1px 24px 1px 4px\" inside the shadow DOM\n    // Tested under Android 11 / Chrome 89, Android 12 / Chrome 100, Android 13 / Chrome 109\n    margin: 0;\n  }\n\n  // Prevent excessive date input height in Webkit\n  // https://github.com/twbs/bootstrap/issues/34433\n  &::-webkit-datetime-edit {\n    display: block;\n    padding: 0;\n  }\n\n  // Placeholder\n  &::placeholder {\n    color: $input-placeholder-color;\n    // Override Firefox's unusual default opacity; see https://github.com/twbs/bootstrap/pull/11526.\n    opacity: 1;\n  }\n\n  // Disabled inputs\n  //\n  // HTML5 says that controls under a fieldset > legend:first-child won't be\n  // disabled if the fieldset is disabled. Due to implementation difficulty, we\n  // don't honor that edge case; we style them as disabled anyway.\n  &:disabled {\n    color: $input-disabled-color;\n    background-color: $input-disabled-bg;\n    border-color: $input-disabled-border-color;\n    // iOS fix for unreadable disabled content; see https://github.com/twbs/bootstrap/issues/11655.\n    opacity: 1;\n  }\n\n  // File input buttons theming\n  &::file-selector-button {\n    padding: $input-padding-y $input-padding-x;\n    margin: (-$input-padding-y) (-$input-padding-x);\n    margin-inline-end: $input-padding-x;\n    color: $form-file-button-color;\n    @include gradient-bg($form-file-button-bg);\n    pointer-events: none;\n    border-color: inherit;\n    border-style: solid;\n    border-width: 0;\n    border-inline-end-width: $input-border-width;\n    border-radius: 0; // stylelint-disable-line property-disallowed-list\n    @include transition($btn-transition);\n  }\n\n  &:hover:not(:disabled):not([readonly])::file-selector-button {\n    background-color: $form-file-button-hover-bg;\n  }\n}\n\n// Readonly controls as plain text\n//\n// Apply class to a readonly input to make it appear like regular plain\n// text (without any border, background color, focus indicator)\n\n.form-control-plaintext {\n  display: block;\n  width: 100%;\n  padding: $input-padding-y 0;\n  margin-bottom: 0; // match inputs if this class comes on inputs with default margins\n  line-height: $input-line-height;\n  color: $input-plaintext-color;\n  background-color: transparent;\n  border: solid transparent;\n  border-width: $input-border-width 0;\n\n  &:focus {\n    outline: 0;\n  }\n\n  &.form-control-sm,\n  &.form-control-lg {\n    padding-right: 0;\n    padding-left: 0;\n  }\n}\n\n// Form control sizing\n//\n// Build on `.form-control` with modifier classes to decrease or increase the\n// height and font-size of form controls.\n//\n// Repeated in `_input_group.scss` to avoid Sass extend issues.\n\n.form-control-sm {\n  min-height: $input-height-sm;\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n\n  &::file-selector-button {\n    padding: $input-padding-y-sm $input-padding-x-sm;\n    margin: (-$input-padding-y-sm) (-$input-padding-x-sm);\n    margin-inline-end: $input-padding-x-sm;\n  }\n}\n\n.form-control-lg {\n  min-height: $input-height-lg;\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n\n  &::file-selector-button {\n    padding: $input-padding-y-lg $input-padding-x-lg;\n    margin: (-$input-padding-y-lg) (-$input-padding-x-lg);\n    margin-inline-end: $input-padding-x-lg;\n  }\n}\n\n// Make sure textareas don't shrink too much when resized\n// https://github.com/twbs/bootstrap/pull/29124\n// stylelint-disable selector-no-qualifying-type\ntextarea {\n  &.form-control {\n    min-height: $input-height;\n  }\n\n  &.form-control-sm {\n    min-height: $input-height-sm;\n  }\n\n  &.form-control-lg {\n    min-height: $input-height-lg;\n  }\n}\n// stylelint-enable selector-no-qualifying-type\n\n.form-control-color {\n  width: $form-color-width;\n  height: $input-height;\n  padding: $input-padding-y;\n\n  &:not(:disabled):not([readonly]) {\n    cursor: pointer;\n  }\n\n  &::-moz-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &::-webkit-color-swatch {\n    border: 0 !important; // stylelint-disable-line declaration-no-important\n    @include border-radius($input-border-radius);\n  }\n\n  &.form-control-sm { height: $input-height-sm; }\n  &.form-control-lg { height: $input-height-lg; }\n}\n","// stylelint-disable property-disallowed-list\n@mixin transition($transition...) {\n  @if length($transition) == 0 {\n    $transition: $transition-base;\n  }\n\n  @if length($transition) > 1 {\n    @each $value in $transition {\n      @if $value == null or $value == none {\n        @warn \"The keyword 'none' or 'null' must be used as a single argument.\";\n      }\n    }\n  }\n\n  @if $enable-transitions {\n    @if nth($transition, 1) != null {\n      transition: $transition;\n    }\n\n    @if $enable-reduced-motion and nth($transition, 1) != null and nth($transition, 1) != none {\n      @media (prefers-reduced-motion: reduce) {\n        transition: none;\n      }\n    }\n  }\n}\n","// Gradients\n\n// scss-docs-start gradient-bg-mixin\n@mixin gradient-bg($color: null) {\n  background-color: $color;\n\n  @if $enable-gradients {\n    background-image: var(--#{$prefix}gradient);\n  }\n}\n// scss-docs-end gradient-bg-mixin\n\n// scss-docs-start gradient-mixins\n// Horizontal gradient, from left to right\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-x($start-color: $gray-700, $end-color: $gray-800, $start-percent: 0%, $end-percent: 100%) {\n  background-image: linear-gradient(to right, $start-color $start-percent, $end-color $end-percent);\n}\n\n// Vertical gradient, from top to bottom\n//\n// Creates two color stops, start and end, by specifying a color and position for each color stop.\n@mixin gradient-y($start-color: $gray-700, $end-color: $gray-800, $start-percent: null, $end-percent: null) {\n  background-image: linear-gradient(to bottom, $start-color $start-percent, $end-color $end-percent);\n}\n\n@mixin gradient-directional($start-color: $gray-700, $end-color: $gray-800, $deg: 45deg) {\n  background-image: linear-gradient($deg, $start-color, $end-color);\n}\n\n@mixin gradient-x-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient(to right, $start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-y-three-colors($start-color: $blue, $mid-color: $purple, $color-stop: 50%, $end-color: $red) {\n  background-image: linear-gradient($start-color, $mid-color $color-stop, $end-color);\n}\n\n@mixin gradient-radial($inner-color: $gray-700, $outer-color: $gray-800) {\n  background-image: radial-gradient(circle, $inner-color, $outer-color);\n}\n\n@mixin gradient-striped($color: rgba($white, .15), $angle: 45deg) {\n  background-image: linear-gradient($angle, $color 25%, transparent 25%, transparent 50%, $color 50%, $color 75%, transparent 75%, transparent);\n}\n// scss-docs-end gradient-mixins\n","// Select\n//\n// Replaces the browser default select with a custom one, mostly pulled from\n// https://primer.github.io/.\n\n.form-select {\n  --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator)};\n\n  display: block;\n  width: 100%;\n  padding: $form-select-padding-y $form-select-indicator-padding $form-select-padding-y $form-select-padding-x;\n  font-family: $form-select-font-family;\n  @include font-size($form-select-font-size);\n  font-weight: $form-select-font-weight;\n  line-height: $form-select-line-height;\n  color: $form-select-color;\n  appearance: none;\n  background-color: $form-select-bg;\n  background-image: var(--#{$prefix}form-select-bg-img), var(--#{$prefix}form-select-bg-icon, none);\n  background-repeat: no-repeat;\n  background-position: $form-select-bg-position;\n  background-size: $form-select-bg-size;\n  border: $form-select-border-width solid $form-select-border-color;\n  @include border-radius($form-select-border-radius, 0);\n  @include box-shadow($form-select-box-shadow);\n  @include transition($form-select-transition);\n\n  &:focus {\n    border-color: $form-select-focus-border-color;\n    outline: 0;\n    @if $enable-shadows {\n      @include box-shadow($form-select-box-shadow, $form-select-focus-box-shadow);\n    } @else {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      box-shadow: $form-select-focus-box-shadow;\n    }\n  }\n\n  &[multiple],\n  &[size]:not([size=\"1\"]) {\n    padding-right: $form-select-padding-x;\n    background-image: none;\n  }\n\n  &:disabled {\n    color: $form-select-disabled-color;\n    background-color: $form-select-disabled-bg;\n    border-color: $form-select-disabled-border-color;\n  }\n\n  // Remove outline from select box in FF\n  &:-moz-focusring {\n    color: transparent;\n    text-shadow: 0 0 0 $form-select-color;\n  }\n}\n\n.form-select-sm {\n  padding-top: $form-select-padding-y-sm;\n  padding-bottom: $form-select-padding-y-sm;\n  padding-left: $form-select-padding-x-sm;\n  @include font-size($form-select-font-size-sm);\n  @include border-radius($form-select-border-radius-sm);\n}\n\n.form-select-lg {\n  padding-top: $form-select-padding-y-lg;\n  padding-bottom: $form-select-padding-y-lg;\n  padding-left: $form-select-padding-x-lg;\n  @include font-size($form-select-font-size-lg);\n  @include border-radius($form-select-border-radius-lg);\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-select {\n      --#{$prefix}form-select-bg-img: #{escape-svg($form-select-indicator-dark)};\n    }\n  }\n}\n","//\n// Check/radio\n//\n\n.form-check {\n  display: block;\n  min-height: $form-check-min-height;\n  padding-left: $form-check-padding-start;\n  margin-bottom: $form-check-margin-bottom;\n\n  .form-check-input {\n    float: left;\n    margin-left: $form-check-padding-start * -1;\n  }\n}\n\n.form-check-reverse {\n  padding-right: $form-check-padding-start;\n  padding-left: 0;\n  text-align: right;\n\n  .form-check-input {\n    float: right;\n    margin-right: $form-check-padding-start * -1;\n    margin-left: 0;\n  }\n}\n\n.form-check-input {\n  --#{$prefix}form-check-bg: #{$form-check-input-bg};\n\n  flex-shrink: 0;\n  width: $form-check-input-width;\n  height: $form-check-input-width;\n  margin-top: ($line-height-base - $form-check-input-width) * .5; // line-height minus check height\n  vertical-align: top;\n  appearance: none;\n  background-color: var(--#{$prefix}form-check-bg);\n  background-image: var(--#{$prefix}form-check-bg-image);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: contain;\n  border: $form-check-input-border;\n  print-color-adjust: exact; // Keep themed appearance for print\n  @include transition($form-check-transition);\n\n  &[type=\"checkbox\"] {\n    @include border-radius($form-check-input-border-radius);\n  }\n\n  &[type=\"radio\"] {\n    // stylelint-disable-next-line property-disallowed-list\n    border-radius: $form-check-radio-border-radius;\n  }\n\n  &:active {\n    filter: $form-check-input-active-filter;\n  }\n\n  &:focus {\n    border-color: $form-check-input-focus-border;\n    outline: 0;\n    box-shadow: $form-check-input-focus-box-shadow;\n  }\n\n  &:checked {\n    background-color: $form-check-input-checked-bg-color;\n    border-color: $form-check-input-checked-border-color;\n\n    &[type=\"checkbox\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-checked-bg-image)};\n      }\n    }\n\n    &[type=\"radio\"] {\n      @if $enable-gradients {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-check-bg-image: #{escape-svg($form-check-radio-checked-bg-image)};\n      }\n    }\n  }\n\n  &[type=\"checkbox\"]:indeterminate {\n    background-color: $form-check-input-indeterminate-bg-color;\n    border-color: $form-check-input-indeterminate-border-color;\n\n    @if $enable-gradients {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)}, var(--#{$prefix}gradient);\n    } @else {\n      --#{$prefix}form-check-bg-image: #{escape-svg($form-check-input-indeterminate-bg-image)};\n    }\n  }\n\n  &:disabled {\n    pointer-events: none;\n    filter: none;\n    opacity: $form-check-input-disabled-opacity;\n  }\n\n  // Use disabled attribute in addition of :disabled pseudo-class\n  // See: https://github.com/twbs/bootstrap/issues/28247\n  &[disabled],\n  &:disabled {\n    ~ .form-check-label {\n      cursor: default;\n      opacity: $form-check-label-disabled-opacity;\n    }\n  }\n}\n\n.form-check-label {\n  color: $form-check-label-color;\n  cursor: $form-check-label-cursor;\n}\n\n//\n// Switch\n//\n\n.form-switch {\n  padding-left: $form-switch-padding-start;\n\n  .form-check-input {\n    --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image)};\n\n    width: $form-switch-width;\n    margin-left: $form-switch-padding-start * -1;\n    background-image: var(--#{$prefix}form-switch-bg);\n    background-position: left center;\n    @include border-radius($form-switch-border-radius);\n    @include transition($form-switch-transition);\n\n    &:focus {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-focus-bg-image)};\n    }\n\n    &:checked {\n      background-position: $form-switch-checked-bg-position;\n\n      @if $enable-gradients {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)}, var(--#{$prefix}gradient);\n      } @else {\n        --#{$prefix}form-switch-bg: #{escape-svg($form-switch-checked-bg-image)};\n      }\n    }\n  }\n\n  &.form-check-reverse {\n    padding-right: $form-switch-padding-start;\n    padding-left: 0;\n\n    .form-check-input {\n      margin-right: $form-switch-padding-start * -1;\n      margin-left: 0;\n    }\n  }\n}\n\n.form-check-inline {\n  display: inline-block;\n  margin-right: $form-check-inline-margin-end;\n}\n\n.btn-check {\n  position: absolute;\n  clip: rect(0, 0, 0, 0);\n  pointer-events: none;\n\n  &[disabled],\n  &:disabled {\n    + .btn {\n      pointer-events: none;\n      filter: none;\n      opacity: $form-check-btn-check-disabled-opacity;\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .form-switch .form-check-input:not(:checked):not(:focus) {\n      --#{$prefix}form-switch-bg: #{escape-svg($form-switch-bg-image-dark)};\n    }\n  }\n}\n","// Range\n//\n// Style range inputs the same across browsers. Vendor-specific rules for pseudo\n// elements cannot be mixed. As such, there are no shared styles for focus or\n// active states on prefixed selectors.\n\n.form-range {\n  width: 100%;\n  height: add($form-range-thumb-height, $form-range-thumb-focus-box-shadow-width * 2);\n  padding: 0; // Need to reset padding\n  appearance: none;\n  background-color: transparent;\n\n  &:focus {\n    outline: 0;\n\n    // Pseudo-elements must be split across multiple rulesets to have an effect.\n    // No box-shadow() mixin for focus accessibility.\n    &::-webkit-slider-thumb { box-shadow: $form-range-thumb-focus-box-shadow; }\n    &::-moz-range-thumb     { box-shadow: $form-range-thumb-focus-box-shadow; }\n  }\n\n  &::-moz-focus-outer {\n    border: 0;\n  }\n\n  &::-webkit-slider-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    margin-top: ($form-range-track-height - $form-range-thumb-height) * .5; // Webkit specific\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-webkit-slider-runnable-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent; // Why?\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent;\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &::-moz-range-thumb {\n    width: $form-range-thumb-width;\n    height: $form-range-thumb-height;\n    appearance: none;\n    @include gradient-bg($form-range-thumb-bg);\n    border: $form-range-thumb-border;\n    @include border-radius($form-range-thumb-border-radius);\n    @include box-shadow($form-range-thumb-box-shadow);\n    @include transition($form-range-thumb-transition);\n\n    &:active {\n      @include gradient-bg($form-range-thumb-active-bg);\n    }\n  }\n\n  &::-moz-range-track {\n    width: $form-range-track-width;\n    height: $form-range-track-height;\n    color: transparent;\n    cursor: $form-range-track-cursor;\n    background-color: $form-range-track-bg;\n    border-color: transparent; // Firefox specific?\n    @include border-radius($form-range-track-border-radius);\n    @include box-shadow($form-range-track-box-shadow);\n  }\n\n  &:disabled {\n    pointer-events: none;\n\n    &::-webkit-slider-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n\n    &::-moz-range-thumb {\n      background-color: $form-range-thumb-disabled-bg;\n    }\n  }\n}\n",".form-floating {\n  position: relative;\n\n  > .form-control,\n  > .form-control-plaintext,\n  > .form-select {\n    height: $form-floating-height;\n    min-height: $form-floating-height;\n    line-height: $form-floating-line-height;\n  }\n\n  > label {\n    position: absolute;\n    top: 0;\n    left: 0;\n    z-index: 2;\n    height: 100%; // allow textareas\n    padding: $form-floating-padding-y $form-floating-padding-x;\n    overflow: hidden;\n    text-align: start;\n    text-overflow: ellipsis;\n    white-space: nowrap;\n    pointer-events: none;\n    border: $input-border-width solid transparent; // Required for aligning label's text with the input as it affects inner box model\n    transform-origin: 0 0;\n    @include transition($form-floating-transition);\n  }\n\n  > .form-control,\n  > .form-control-plaintext {\n    padding: $form-floating-padding-y $form-floating-padding-x;\n\n    &::placeholder {\n      color: transparent;\n    }\n\n    &:focus,\n    &:not(:placeholder-shown) {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n    // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n    &:-webkit-autofill {\n      padding-top: $form-floating-input-padding-t;\n      padding-bottom: $form-floating-input-padding-b;\n    }\n  }\n\n  > .form-select {\n    padding-top: $form-floating-input-padding-t;\n    padding-bottom: $form-floating-input-padding-b;\n  }\n\n  > .form-control:focus,\n  > .form-control:not(:placeholder-shown),\n  > .form-control-plaintext,\n  > .form-select {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n\n      &::after {\n        position: absolute;\n        inset: $form-floating-padding-y ($form-floating-padding-x * .5);\n        z-index: -1;\n        height: $form-floating-label-height;\n        content: \"\";\n        background-color: $input-bg;\n        @include border-radius($input-border-radius);\n      }\n    }\n  }\n  // Duplicated because `:-webkit-autofill` invalidates other selectors when grouped\n  > .form-control:-webkit-autofill {\n    ~ label {\n      color: rgba(var(--#{$prefix}body-color-rgb), #{$form-floating-label-opacity});\n      transform: $form-floating-label-transform;\n    }\n  }\n\n  > .form-control-plaintext {\n    ~ label {\n      border-width: $input-border-width 0; // Required to properly position label text - as explained above\n    }\n  }\n\n  > :disabled ~ label,\n  > .form-control:disabled ~ label { // Required for `.form-control`s because of specificity\n    color: $form-floating-label-disabled-color;\n\n    &::after {\n      background-color: $input-disabled-bg;\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.input-group {\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // For form validation feedback\n  align-items: stretch;\n  width: 100%;\n\n  > .form-control,\n  > .form-select,\n  > .form-floating {\n    position: relative; // For focus state's z-index\n    flex: 1 1 auto;\n    width: 1%;\n    min-width: 0; // https://stackoverflow.com/questions/36247140/why-dont-flex-items-shrink-past-content-size\n  }\n\n  // Bring the \"active\" form control to the top of surrounding elements\n  > .form-control:focus,\n  > .form-select:focus,\n  > .form-floating:focus-within {\n    z-index: 5;\n  }\n\n  // Ensure buttons are always above inputs for more visually pleasing borders.\n  // This isn't needed for `.input-group-text` since it shares the same border-color\n  // as our inputs.\n  .btn {\n    position: relative;\n    z-index: 2;\n\n    &:focus {\n      z-index: 5;\n    }\n  }\n}\n\n\n// Textual addons\n//\n// Serves as a catch-all element for any text or radio/checkbox input you wish\n// to prepend or append to an input.\n\n.input-group-text {\n  display: flex;\n  align-items: center;\n  padding: $input-group-addon-padding-y $input-group-addon-padding-x;\n  @include font-size($input-font-size); // Match inputs\n  font-weight: $input-group-addon-font-weight;\n  line-height: $input-line-height;\n  color: $input-group-addon-color;\n  text-align: center;\n  white-space: nowrap;\n  background-color: $input-group-addon-bg;\n  border: $input-border-width solid $input-group-addon-border-color;\n  @include border-radius($input-border-radius);\n}\n\n\n// Sizing\n//\n// Remix the default form control sizing classes into new ones for easier\n// manipulation.\n\n.input-group-lg > .form-control,\n.input-group-lg > .form-select,\n.input-group-lg > .input-group-text,\n.input-group-lg > .btn {\n  padding: $input-padding-y-lg $input-padding-x-lg;\n  @include font-size($input-font-size-lg);\n  @include border-radius($input-border-radius-lg);\n}\n\n.input-group-sm > .form-control,\n.input-group-sm > .form-select,\n.input-group-sm > .input-group-text,\n.input-group-sm > .btn {\n  padding: $input-padding-y-sm $input-padding-x-sm;\n  @include font-size($input-font-size-sm);\n  @include border-radius($input-border-radius-sm);\n}\n\n.input-group-lg > .form-select,\n.input-group-sm > .form-select {\n  padding-right: $form-select-padding-x + $form-select-indicator-padding;\n}\n\n\n// Rounded corners\n//\n// These rulesets must come after the sizing ones to properly override sm and lg\n// border-radius values when extending. They're more specific than we'd like\n// with the `.input-group >` part, but without it, we cannot override the sizing.\n\n// stylelint-disable-next-line no-duplicate-selectors\n.input-group {\n  &:not(.has-validation) {\n    > :not(:last-child):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 3),\n    > .form-floating:not(:last-child) > .form-control,\n    > .form-floating:not(:last-child) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  &.has-validation {\n    > :nth-last-child(n + 3):not(.dropdown-toggle):not(.dropdown-menu):not(.form-floating),\n    > .dropdown-toggle:nth-last-child(n + 4),\n    > .form-floating:nth-last-child(n + 3) > .form-control,\n    > .form-floating:nth-last-child(n + 3) > .form-select {\n      @include border-end-radius(0);\n    }\n  }\n\n  $validation-messages: \"\";\n  @each $state in map-keys($form-validation-states) {\n    $validation-messages: $validation-messages + \":not(.\" + unquote($state) + \"-tooltip)\" + \":not(.\" + unquote($state) + \"-feedback)\";\n  }\n\n  > :not(:first-child):not(.dropdown-menu)#{$validation-messages} {\n    margin-left: calc(#{$input-border-width} * -1); // stylelint-disable-line function-disallowed-list\n    @include border-start-radius(0);\n  }\n\n  > .form-floating:not(:first-child) > .form-control,\n  > .form-floating:not(:first-child) > .form-select {\n    @include border-start-radius(0);\n  }\n}\n","// This mixin uses an `if()` technique to be compatible with Dart Sass\n// See https://github.com/sass/sass/issues/1873#issuecomment-152293725 for more details\n\n// scss-docs-start form-validation-mixins\n@mixin form-validation-state-selector($state) {\n  @if ($state == \"valid\" or $state == \"invalid\") {\n    .was-validated #{if(&, \"&\", \"\")}:#{$state},\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  } @else {\n    #{if(&, \"&\", \"\")}.is-#{$state} {\n      @content;\n    }\n  }\n}\n\n@mixin form-validation-state(\n  $state,\n  $color,\n  $icon,\n  $tooltip-color: color-contrast($color),\n  $tooltip-bg-color: rgba($color, $form-feedback-tooltip-opacity),\n  $focus-box-shadow: 0 0 $input-btn-focus-blur $input-focus-width rgba($color, $input-btn-focus-color-opacity),\n  $border-color: $color\n) {\n  .#{$state}-feedback {\n    display: none;\n    width: 100%;\n    margin-top: $form-feedback-margin-top;\n    @include font-size($form-feedback-font-size);\n    font-style: $form-feedback-font-style;\n    color: $color;\n  }\n\n  .#{$state}-tooltip {\n    position: absolute;\n    top: 100%;\n    z-index: 5;\n    display: none;\n    max-width: 100%; // Contain to parent when possible\n    padding: $form-feedback-tooltip-padding-y $form-feedback-tooltip-padding-x;\n    margin-top: .1rem;\n    @include font-size($form-feedback-tooltip-font-size);\n    line-height: $form-feedback-tooltip-line-height;\n    color: $tooltip-color;\n    background-color: $tooltip-bg-color;\n    @include border-radius($form-feedback-tooltip-border-radius);\n  }\n\n  @include form-validation-state-selector($state) {\n    ~ .#{$state}-feedback,\n    ~ .#{$state}-tooltip {\n      display: block;\n    }\n  }\n\n  .form-control {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-image: escape-svg($icon);\n        background-repeat: no-repeat;\n        background-position: right $input-height-inner-quarter center;\n        background-size: $input-height-inner-half $input-height-inner-half;\n      }\n\n      &:focus {\n        border-color: $border-color;\n        box-shadow: $focus-box-shadow;\n      }\n    }\n  }\n\n  // stylelint-disable-next-line selector-no-qualifying-type\n  textarea.form-control {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        padding-right: $input-height-inner;\n        background-position: top $input-height-inner-quarter right $input-height-inner-quarter;\n      }\n    }\n  }\n\n  .form-select {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      @if $enable-validation-icons {\n        &:not([multiple]):not([size]),\n        &:not([multiple])[size=\"1\"] {\n          --#{$prefix}form-select-bg-icon: #{escape-svg($icon)};\n          padding-right: $form-select-feedback-icon-padding-end;\n          background-position: $form-select-bg-position, $form-select-feedback-icon-position;\n          background-size: $form-select-bg-size, $form-select-feedback-icon-size;\n        }\n      }\n\n      &:focus {\n        border-color: $border-color;\n        box-shadow: $focus-box-shadow;\n      }\n    }\n  }\n\n  .form-control-color {\n    @include form-validation-state-selector($state) {\n      @if $enable-validation-icons {\n        width: add($form-color-width, $input-height-inner);\n      }\n    }\n  }\n\n  .form-check-input {\n    @include form-validation-state-selector($state) {\n      border-color: $border-color;\n\n      &:checked {\n        background-color: $color;\n      }\n\n      &:focus {\n        box-shadow: $focus-box-shadow;\n      }\n\n      ~ .form-check-label {\n        color: $color;\n      }\n    }\n  }\n  .form-check-inline .form-check-input {\n    ~ .#{$state}-feedback {\n      margin-left: .5em;\n    }\n  }\n\n  .input-group {\n    > .form-control:not(:focus),\n    > .form-select:not(:focus),\n    > .form-floating:not(:focus-within) {\n      @include form-validation-state-selector($state) {\n        @if $state == \"valid\" {\n          z-index: 3;\n        } @else if $state == \"invalid\" {\n          z-index: 4;\n        }\n      }\n    }\n  }\n}\n// scss-docs-end form-validation-mixins\n","//\n// Base styles\n//\n\n.btn {\n  // scss-docs-start btn-css-vars\n  --#{$prefix}btn-padding-x: #{$btn-padding-x};\n  --#{$prefix}btn-padding-y: #{$btn-padding-y};\n  --#{$prefix}btn-font-family: #{$btn-font-family};\n  @include rfs($btn-font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-font-weight: #{$btn-font-weight};\n  --#{$prefix}btn-line-height: #{$btn-line-height};\n  --#{$prefix}btn-color: #{$btn-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-width: #{$btn-border-width};\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-border-radius: #{$btn-border-radius};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-box-shadow: #{$btn-box-shadow};\n  --#{$prefix}btn-disabled-opacity: #{$btn-disabled-opacity};\n  --#{$prefix}btn-focus-box-shadow: 0 0 0 #{$btn-focus-width} rgba(var(--#{$prefix}btn-focus-shadow-rgb), .5);\n  // scss-docs-end btn-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}btn-padding-y) var(--#{$prefix}btn-padding-x);\n  font-family: var(--#{$prefix}btn-font-family);\n  @include font-size(var(--#{$prefix}btn-font-size));\n  font-weight: var(--#{$prefix}btn-font-weight);\n  line-height: var(--#{$prefix}btn-line-height);\n  color: var(--#{$prefix}btn-color);\n  text-align: center;\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: $btn-white-space;\n  vertical-align: middle;\n  cursor: if($enable-button-pointers, pointer, null);\n  user-select: none;\n  border: var(--#{$prefix}btn-border-width) solid var(--#{$prefix}btn-border-color);\n  @include border-radius(var(--#{$prefix}btn-border-radius));\n  @include gradient-bg(var(--#{$prefix}btn-bg));\n  @include box-shadow(var(--#{$prefix}btn-box-shadow));\n  @include transition($btn-transition);\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}btn-hover-bg);\n    border-color: var(--#{$prefix}btn-hover-border-color);\n  }\n\n  .btn-check + &:hover {\n    // override for the checkbox/radio buttons\n    color: var(--#{$prefix}btn-color);\n    background-color: var(--#{$prefix}btn-bg);\n    border-color: var(--#{$prefix}btn-border-color);\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-hover-color);\n    @include gradient-bg(var(--#{$prefix}btn-hover-bg));\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:focus-visible + & {\n    border-color: var(--#{$prefix}btn-hover-border-color);\n    outline: 0;\n    // Avoid using mixin so we can pass custom focus shadow properly\n    @if $enable-shadows {\n      box-shadow: var(--#{$prefix}btn-box-shadow), var(--#{$prefix}btn-focus-box-shadow);\n    } @else {\n      box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n    }\n  }\n\n  .btn-check:checked + &,\n  :not(.btn-check) + &:active,\n  &:first-child:active,\n  &.active,\n  &.show {\n    color: var(--#{$prefix}btn-active-color);\n    background-color: var(--#{$prefix}btn-active-bg);\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-active-border-color);\n    @include box-shadow(var(--#{$prefix}btn-active-shadow));\n\n    &:focus-visible {\n      // Avoid using mixin so we can pass custom focus shadow properly\n      @if $enable-shadows {\n        box-shadow: var(--#{$prefix}btn-active-shadow), var(--#{$prefix}btn-focus-box-shadow);\n      } @else {\n        box-shadow: var(--#{$prefix}btn-focus-box-shadow);\n      }\n    }\n  }\n\n  &:disabled,\n  &.disabled,\n  fieldset:disabled & {\n    color: var(--#{$prefix}btn-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}btn-disabled-bg);\n    background-image: if($enable-gradients, none, null);\n    border-color: var(--#{$prefix}btn-disabled-border-color);\n    opacity: var(--#{$prefix}btn-disabled-opacity);\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Alternate buttons\n//\n\n// scss-docs-start btn-variant-loops\n@each $color, $value in $theme-colors {\n  .btn-#{$color} {\n    @if $color == \"light\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: shade-color($value, $btn-hover-bg-shade-amount),\n        $hover-border: shade-color($value, $btn-hover-border-shade-amount),\n        $active-background: shade-color($value, $btn-active-bg-shade-amount),\n        $active-border: shade-color($value, $btn-active-border-shade-amount)\n      );\n    } @else if $color == \"dark\" {\n      @include button-variant(\n        $value,\n        $value,\n        $hover-background: tint-color($value, $btn-hover-bg-tint-amount),\n        $hover-border: tint-color($value, $btn-hover-border-tint-amount),\n        $active-background: tint-color($value, $btn-active-bg-tint-amount),\n        $active-border: tint-color($value, $btn-active-border-tint-amount)\n      );\n    } @else {\n      @include button-variant($value, $value);\n    }\n  }\n}\n\n@each $color, $value in $theme-colors {\n  .btn-outline-#{$color} {\n    @include button-outline-variant($value);\n  }\n}\n// scss-docs-end btn-variant-loops\n\n\n//\n// Link buttons\n//\n\n// Make a button look and behave like a link\n.btn-link {\n  --#{$prefix}btn-font-weight: #{$font-weight-normal};\n  --#{$prefix}btn-color: #{$btn-link-color};\n  --#{$prefix}btn-bg: transparent;\n  --#{$prefix}btn-border-color: transparent;\n  --#{$prefix}btn-hover-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-hover-border-color: transparent;\n  --#{$prefix}btn-active-color: #{$btn-link-hover-color};\n  --#{$prefix}btn-active-border-color: transparent;\n  --#{$prefix}btn-disabled-color: #{$btn-link-disabled-color};\n  --#{$prefix}btn-disabled-border-color: transparent;\n  --#{$prefix}btn-box-shadow: 0 0 0 #000; // Can't use `none` as keyword negates all values when used with multiple shadows\n  --#{$prefix}btn-focus-shadow-rgb: #{$btn-link-focus-shadow-rgb};\n\n  text-decoration: $link-decoration;\n  @if $enable-gradients {\n    background-image: none;\n  }\n\n  &:hover,\n  &:focus-visible {\n    text-decoration: $link-hover-decoration;\n  }\n\n  &:focus-visible {\n    color: var(--#{$prefix}btn-color);\n  }\n\n  &:hover {\n    color: var(--#{$prefix}btn-hover-color);\n  }\n\n  // No need for an active state here\n}\n\n\n//\n// Button Sizes\n//\n\n.btn-lg {\n  @include button-size($btn-padding-y-lg, $btn-padding-x-lg, $btn-font-size-lg, $btn-border-radius-lg);\n}\n\n.btn-sm {\n  @include button-size($btn-padding-y-sm, $btn-padding-x-sm, $btn-font-size-sm, $btn-border-radius-sm);\n}\n","// Button variants\n//\n// Easily pump out default styles, as well as :hover, :focus, :active,\n// and disabled options for all buttons\n\n// scss-docs-start btn-variant-mixin\n@mixin button-variant(\n  $background,\n  $border,\n  $color: color-contrast($background),\n  $hover-background: if($color == $color-contrast-light, shade-color($background, $btn-hover-bg-shade-amount), tint-color($background, $btn-hover-bg-tint-amount)),\n  $hover-border: if($color == $color-contrast-light, shade-color($border, $btn-hover-border-shade-amount), tint-color($border, $btn-hover-border-tint-amount)),\n  $hover-color: color-contrast($hover-background),\n  $active-background: if($color == $color-contrast-light, shade-color($background, $btn-active-bg-shade-amount), tint-color($background, $btn-active-bg-tint-amount)),\n  $active-border: if($color == $color-contrast-light, shade-color($border, $btn-active-border-shade-amount), tint-color($border, $btn-active-border-tint-amount)),\n  $active-color: color-contrast($active-background),\n  $disabled-background: $background,\n  $disabled-border: $border,\n  $disabled-color: color-contrast($disabled-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-bg: #{$background};\n  --#{$prefix}btn-border-color: #{$border};\n  --#{$prefix}btn-hover-color: #{$hover-color};\n  --#{$prefix}btn-hover-bg: #{$hover-background};\n  --#{$prefix}btn-hover-border-color: #{$hover-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb(mix($color, $border, 15%))};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$disabled-color};\n  --#{$prefix}btn-disabled-bg: #{$disabled-background};\n  --#{$prefix}btn-disabled-border-color: #{$disabled-border};\n}\n// scss-docs-end btn-variant-mixin\n\n// scss-docs-start btn-outline-variant-mixin\n@mixin button-outline-variant(\n  $color,\n  $color-hover: color-contrast($color),\n  $active-background: $color,\n  $active-border: $color,\n  $active-color: color-contrast($active-background)\n) {\n  --#{$prefix}btn-color: #{$color};\n  --#{$prefix}btn-border-color: #{$color};\n  --#{$prefix}btn-hover-color: #{$color-hover};\n  --#{$prefix}btn-hover-bg: #{$active-background};\n  --#{$prefix}btn-hover-border-color: #{$active-border};\n  --#{$prefix}btn-focus-shadow-rgb: #{to-rgb($color)};\n  --#{$prefix}btn-active-color: #{$active-color};\n  --#{$prefix}btn-active-bg: #{$active-background};\n  --#{$prefix}btn-active-border-color: #{$active-border};\n  --#{$prefix}btn-active-shadow: #{$btn-active-box-shadow};\n  --#{$prefix}btn-disabled-color: #{$color};\n  --#{$prefix}btn-disabled-bg: transparent;\n  --#{$prefix}btn-disabled-border-color: #{$color};\n  --#{$prefix}gradient: none;\n}\n// scss-docs-end btn-outline-variant-mixin\n\n// scss-docs-start btn-size-mixin\n@mixin button-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}btn-padding-y: #{$padding-y};\n  --#{$prefix}btn-padding-x: #{$padding-x};\n  @include rfs($font-size, --#{$prefix}btn-font-size);\n  --#{$prefix}btn-border-radius: #{$border-radius};\n}\n// scss-docs-end btn-size-mixin\n",".fade {\n  @include transition($transition-fade);\n\n  &:not(.show) {\n    opacity: 0;\n  }\n}\n\n// scss-docs-start collapse-classes\n.collapse {\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.collapsing {\n  height: 0;\n  overflow: hidden;\n  @include transition($transition-collapse);\n\n  &.collapse-horizontal {\n    width: 0;\n    height: auto;\n    @include transition($transition-collapse-width);\n  }\n}\n// scss-docs-end collapse-classes\n","// The dropdown wrapper (`<div>`)\n.dropup,\n.dropend,\n.dropdown,\n.dropstart,\n.dropup-center,\n.dropdown-center {\n  position: relative;\n}\n\n.dropdown-toggle {\n  white-space: nowrap;\n\n  // Generate the caret automatically\n  @include caret();\n}\n\n// The dropdown menu\n.dropdown-menu {\n  // scss-docs-start dropdown-css-vars\n  --#{$prefix}dropdown-zindex: #{$zindex-dropdown};\n  --#{$prefix}dropdown-min-width: #{$dropdown-min-width};\n  --#{$prefix}dropdown-padding-x: #{$dropdown-padding-x};\n  --#{$prefix}dropdown-padding-y: #{$dropdown-padding-y};\n  --#{$prefix}dropdown-spacer: #{$dropdown-spacer};\n  @include rfs($dropdown-font-size, --#{$prefix}dropdown-font-size);\n  --#{$prefix}dropdown-color: #{$dropdown-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-border-color};\n  --#{$prefix}dropdown-border-radius: #{$dropdown-border-radius};\n  --#{$prefix}dropdown-border-width: #{$dropdown-border-width};\n  --#{$prefix}dropdown-inner-border-radius: #{$dropdown-inner-border-radius};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-divider-bg};\n  --#{$prefix}dropdown-divider-margin-y: #{$dropdown-divider-margin-y};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-link-hover-color};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-link-disabled-color};\n  --#{$prefix}dropdown-item-padding-x: #{$dropdown-item-padding-x};\n  --#{$prefix}dropdown-item-padding-y: #{$dropdown-item-padding-y};\n  --#{$prefix}dropdown-header-color: #{$dropdown-header-color};\n  --#{$prefix}dropdown-header-padding-x: #{$dropdown-header-padding-x};\n  --#{$prefix}dropdown-header-padding-y: #{$dropdown-header-padding-y};\n  // scss-docs-end dropdown-css-vars\n\n  position: absolute;\n  z-index: var(--#{$prefix}dropdown-zindex);\n  display: none; // none by default, but block on \"open\" of the menu\n  min-width: var(--#{$prefix}dropdown-min-width);\n  padding: var(--#{$prefix}dropdown-padding-y) var(--#{$prefix}dropdown-padding-x);\n  margin: 0; // Override default margin of ul\n  @include font-size(var(--#{$prefix}dropdown-font-size));\n  color: var(--#{$prefix}dropdown-color);\n  text-align: left; // Ensures proper alignment if parent has it changed (e.g., modal footer)\n  list-style: none;\n  background-color: var(--#{$prefix}dropdown-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}dropdown-border-width) solid var(--#{$prefix}dropdown-border-color);\n  @include border-radius(var(--#{$prefix}dropdown-border-radius));\n  @include box-shadow(var(--#{$prefix}dropdown-box-shadow));\n\n  &[data-bs-popper] {\n    top: 100%;\n    left: 0;\n    margin-top: var(--#{$prefix}dropdown-spacer);\n  }\n\n  @if $dropdown-padding-y == 0 {\n    > .dropdown-item:first-child,\n    > li:first-child .dropdown-item {\n      @include border-top-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n    > .dropdown-item:last-child,\n    > li:last-child .dropdown-item {\n      @include border-bottom-radius(var(--#{$prefix}dropdown-inner-border-radius));\n    }\n\n  }\n}\n\n// scss-docs-start responsive-breakpoints\n// We deliberately hardcode the `bs-` prefix because we check\n// this custom property in JS to determine Popper's positioning\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .dropdown-menu#{$infix}-start {\n      --bs-position: start;\n\n      &[data-bs-popper] {\n        right: auto;\n        left: 0;\n      }\n    }\n\n    .dropdown-menu#{$infix}-end {\n      --bs-position: end;\n\n      &[data-bs-popper] {\n        right: 0;\n        left: auto;\n      }\n    }\n  }\n}\n// scss-docs-end responsive-breakpoints\n\n// Allow for dropdowns to go bottom up (aka, dropup-menu)\n// Just add .dropup after the standard .dropdown class and you're set.\n.dropup {\n  .dropdown-menu[data-bs-popper] {\n    top: auto;\n    bottom: 100%;\n    margin-top: 0;\n    margin-bottom: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(up);\n  }\n}\n\n.dropend {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: auto;\n    left: 100%;\n    margin-top: 0;\n    margin-left: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(end);\n    &::after {\n      vertical-align: 0;\n    }\n  }\n}\n\n.dropstart {\n  .dropdown-menu[data-bs-popper] {\n    top: 0;\n    right: 100%;\n    left: auto;\n    margin-top: 0;\n    margin-right: var(--#{$prefix}dropdown-spacer);\n  }\n\n  .dropdown-toggle {\n    @include caret(start);\n    &::before {\n      vertical-align: 0;\n    }\n  }\n}\n\n\n// Dividers (basically an `<hr>`) within the dropdown\n.dropdown-divider {\n  height: 0;\n  margin: var(--#{$prefix}dropdown-divider-margin-y) 0;\n  overflow: hidden;\n  border-top: 1px solid var(--#{$prefix}dropdown-divider-bg);\n  opacity: 1; // Revisit in v6 to de-dupe styles that conflict with <hr> element\n}\n\n// Links, buttons, and more within the dropdown menu\n//\n// `<button>`-specific styles are denoted with `// For <button>s`\n.dropdown-item {\n  display: block;\n  width: 100%; // For `<button>`s\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  clear: both;\n  font-weight: $font-weight-normal;\n  color: var(--#{$prefix}dropdown-link-color);\n  text-align: inherit; // For `<button>`s\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap; // prevent links from randomly breaking onto new lines\n  background-color: transparent; // For `<button>`s\n  border: 0; // For `<button>`s\n  @include border-radius(var(--#{$prefix}dropdown-item-border-radius, 0));\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}dropdown-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    @include gradient-bg(var(--#{$prefix}dropdown-link-hover-bg));\n  }\n\n  &.active,\n  &:active {\n    color: var(--#{$prefix}dropdown-link-active-color);\n    text-decoration: none;\n    @include gradient-bg(var(--#{$prefix}dropdown-link-active-bg));\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}dropdown-link-disabled-color);\n    pointer-events: none;\n    background-color: transparent;\n    // Remove CSS gradients if they're enabled\n    background-image: if($enable-gradients, none, null);\n  }\n}\n\n.dropdown-menu.show {\n  display: block;\n}\n\n// Dropdown section headers\n.dropdown-header {\n  display: block;\n  padding: var(--#{$prefix}dropdown-header-padding-y) var(--#{$prefix}dropdown-header-padding-x);\n  margin-bottom: 0; // for use with heading elements\n  @include font-size($font-size-sm);\n  color: var(--#{$prefix}dropdown-header-color);\n  white-space: nowrap; // as with > li > a\n}\n\n// Dropdown text\n.dropdown-item-text {\n  display: block;\n  padding: var(--#{$prefix}dropdown-item-padding-y) var(--#{$prefix}dropdown-item-padding-x);\n  color: var(--#{$prefix}dropdown-link-color);\n}\n\n// Dark dropdowns\n.dropdown-menu-dark {\n  // scss-docs-start dropdown-dark-css-vars\n  --#{$prefix}dropdown-color: #{$dropdown-dark-color};\n  --#{$prefix}dropdown-bg: #{$dropdown-dark-bg};\n  --#{$prefix}dropdown-border-color: #{$dropdown-dark-border-color};\n  --#{$prefix}dropdown-box-shadow: #{$dropdown-dark-box-shadow};\n  --#{$prefix}dropdown-link-color: #{$dropdown-dark-link-color};\n  --#{$prefix}dropdown-link-hover-color: #{$dropdown-dark-link-hover-color};\n  --#{$prefix}dropdown-divider-bg: #{$dropdown-dark-divider-bg};\n  --#{$prefix}dropdown-link-hover-bg: #{$dropdown-dark-link-hover-bg};\n  --#{$prefix}dropdown-link-active-color: #{$dropdown-dark-link-active-color};\n  --#{$prefix}dropdown-link-active-bg: #{$dropdown-dark-link-active-bg};\n  --#{$prefix}dropdown-link-disabled-color: #{$dropdown-dark-link-disabled-color};\n  --#{$prefix}dropdown-header-color: #{$dropdown-dark-header-color};\n  // scss-docs-end dropdown-dark-css-vars\n}\n","// scss-docs-start caret-mixins\n@mixin caret-down($width: $caret-width) {\n  border-top: $width solid;\n  border-right: $width solid transparent;\n  border-bottom: 0;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-up($width: $caret-width) {\n  border-top: 0;\n  border-right: $width solid transparent;\n  border-bottom: $width solid;\n  border-left: $width solid transparent;\n}\n\n@mixin caret-end($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: 0;\n  border-bottom: $width solid transparent;\n  border-left: $width solid;\n}\n\n@mixin caret-start($width: $caret-width) {\n  border-top: $width solid transparent;\n  border-right: $width solid;\n  border-bottom: $width solid transparent;\n}\n\n@mixin caret(\n  $direction: down,\n  $width: $caret-width,\n  $spacing: $caret-spacing,\n  $vertical-align: $caret-vertical-align\n) {\n  @if $enable-caret {\n    &::after {\n      display: inline-block;\n      margin-left: $spacing;\n      vertical-align: $vertical-align;\n      content: \"\";\n      @if $direction == down {\n        @include caret-down($width);\n      } @else if $direction == up {\n        @include caret-up($width);\n      } @else if $direction == end {\n        @include caret-end($width);\n      }\n    }\n\n    @if $direction == start {\n      &::after {\n        display: none;\n      }\n\n      &::before {\n        display: inline-block;\n        margin-right: $spacing;\n        vertical-align: $vertical-align;\n        content: \"\";\n        @include caret-start($width);\n      }\n    }\n\n    &:empty::after {\n      margin-left: 0;\n    }\n  }\n}\n// scss-docs-end caret-mixins\n","// Make the div behave like a button\n.btn-group,\n.btn-group-vertical {\n  position: relative;\n  display: inline-flex;\n  vertical-align: middle; // match .btn alignment given font-size hack above\n\n  > .btn {\n    position: relative;\n    flex: 1 1 auto;\n  }\n\n  // Bring the hover, focused, and \"active\" buttons to the front to overlay\n  // the borders properly\n  > .btn-check:checked + .btn,\n  > .btn-check:focus + .btn,\n  > .btn:hover,\n  > .btn:focus,\n  > .btn:active,\n  > .btn.active {\n    z-index: 1;\n  }\n}\n\n// Optional: Group multiple button groups together for a toolbar\n.btn-toolbar {\n  display: flex;\n  flex-wrap: wrap;\n  justify-content: flex-start;\n\n  .input-group {\n    width: auto;\n  }\n}\n\n.btn-group {\n  @include border-radius($btn-border-radius);\n\n  // Prevent double borders when buttons are next to each other\n  > :not(.btn-check:first-child) + .btn,\n  > .btn-group:not(:first-child) {\n    margin-left: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn.dropdown-toggle-split:first-child,\n  > .btn-group:not(:last-child) > .btn {\n    @include border-end-radius(0);\n  }\n\n  // The left radius should be 0 if the button is:\n  // - the \"third or more\" child\n  // - the second child and the previous element isn't `.btn-check` (making it the first child visually)\n  // - part of a btn-group which isn't the first child\n  > .btn:nth-child(n + 3),\n  > :not(.btn-check) + .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-start-radius(0);\n  }\n}\n\n// Sizing\n//\n// Remix the default button sizing classes into new ones for easier manipulation.\n\n.btn-group-sm > .btn { @extend .btn-sm; }\n.btn-group-lg > .btn { @extend .btn-lg; }\n\n\n//\n// Split button dropdowns\n//\n\n.dropdown-toggle-split {\n  padding-right: $btn-padding-x * .75;\n  padding-left: $btn-padding-x * .75;\n\n  &::after,\n  .dropup &::after,\n  .dropend &::after {\n    margin-left: 0;\n  }\n\n  .dropstart &::before {\n    margin-right: 0;\n  }\n}\n\n.btn-sm + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-sm * .75;\n  padding-left: $btn-padding-x-sm * .75;\n}\n\n.btn-lg + .dropdown-toggle-split {\n  padding-right: $btn-padding-x-lg * .75;\n  padding-left: $btn-padding-x-lg * .75;\n}\n\n\n// The clickable button for toggling the menu\n// Set the same inset shadow as the :active state\n.btn-group.show .dropdown-toggle {\n  @include box-shadow($btn-active-box-shadow);\n\n  // Show no shadow for `.btn-link` since it has no other button styles.\n  &.btn-link {\n    @include box-shadow(none);\n  }\n}\n\n\n//\n// Vertical button groups\n//\n\n.btn-group-vertical {\n  flex-direction: column;\n  align-items: flex-start;\n  justify-content: center;\n\n  > .btn,\n  > .btn-group {\n    width: 100%;\n  }\n\n  > .btn:not(:first-child),\n  > .btn-group:not(:first-child) {\n    margin-top: calc(#{$btn-border-width} * -1); // stylelint-disable-line function-disallowed-list\n  }\n\n  // Reset rounded corners\n  > .btn:not(:last-child):not(.dropdown-toggle),\n  > .btn-group:not(:last-child) > .btn {\n    @include border-bottom-radius(0);\n  }\n\n  > .btn ~ .btn,\n  > .btn-group:not(:first-child) > .btn {\n    @include border-top-radius(0);\n  }\n}\n","// Base class\n//\n// Kickstart any navigation component with a set of style resets. Works with\n// `<nav>`s, `<ul>`s or `<ol>`s.\n\n.nav {\n  // scss-docs-start nav-css-vars\n  --#{$prefix}nav-link-padding-x: #{$nav-link-padding-x};\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: #{$nav-link-color};\n  --#{$prefix}nav-link-hover-color: #{$nav-link-hover-color};\n  --#{$prefix}nav-link-disabled-color: #{$nav-link-disabled-color};\n  // scss-docs-end nav-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n}\n\n.nav-link {\n  display: block;\n  padding: var(--#{$prefix}nav-link-padding-y) var(--#{$prefix}nav-link-padding-x);\n  @include font-size(var(--#{$prefix}nav-link-font-size));\n  font-weight: var(--#{$prefix}nav-link-font-weight);\n  color: var(--#{$prefix}nav-link-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background: none;\n  border: 0;\n  @include transition($nav-link-transition);\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}nav-link-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  &:focus-visible {\n    outline: 0;\n    box-shadow: $nav-link-focus-box-shadow;\n  }\n\n  // Disabled state lightens text\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}nav-link-disabled-color);\n    pointer-events: none;\n    cursor: default;\n  }\n}\n\n//\n// Tabs\n//\n\n.nav-tabs {\n  // scss-docs-start nav-tabs-css-vars\n  --#{$prefix}nav-tabs-border-width: #{$nav-tabs-border-width};\n  --#{$prefix}nav-tabs-border-color: #{$nav-tabs-border-color};\n  --#{$prefix}nav-tabs-border-radius: #{$nav-tabs-border-radius};\n  --#{$prefix}nav-tabs-link-hover-border-color: #{$nav-tabs-link-hover-border-color};\n  --#{$prefix}nav-tabs-link-active-color: #{$nav-tabs-link-active-color};\n  --#{$prefix}nav-tabs-link-active-bg: #{$nav-tabs-link-active-bg};\n  --#{$prefix}nav-tabs-link-active-border-color: #{$nav-tabs-link-active-border-color};\n  // scss-docs-end nav-tabs-css-vars\n\n  border-bottom: var(--#{$prefix}nav-tabs-border-width) solid var(--#{$prefix}nav-tabs-border-color);\n\n  .nav-link {\n    margin-bottom: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    border: var(--#{$prefix}nav-tabs-border-width) solid transparent;\n    @include border-top-radius(var(--#{$prefix}nav-tabs-border-radius));\n\n    &:hover,\n    &:focus {\n      // Prevents active .nav-link tab overlapping focus outline of previous/next .nav-link\n      isolation: isolate;\n      border-color: var(--#{$prefix}nav-tabs-link-hover-border-color);\n    }\n  }\n\n  .nav-link.active,\n  .nav-item.show .nav-link {\n    color: var(--#{$prefix}nav-tabs-link-active-color);\n    background-color: var(--#{$prefix}nav-tabs-link-active-bg);\n    border-color: var(--#{$prefix}nav-tabs-link-active-border-color);\n  }\n\n  .dropdown-menu {\n    // Make dropdown border overlap tab border\n    margin-top: calc(-1 * var(--#{$prefix}nav-tabs-border-width)); // stylelint-disable-line function-disallowed-list\n    // Remove the top rounded corners here since there is a hard edge above the menu\n    @include border-top-radius(0);\n  }\n}\n\n\n//\n// Pills\n//\n\n.nav-pills {\n  // scss-docs-start nav-pills-css-vars\n  --#{$prefix}nav-pills-border-radius: #{$nav-pills-border-radius};\n  --#{$prefix}nav-pills-link-active-color: #{$nav-pills-link-active-color};\n  --#{$prefix}nav-pills-link-active-bg: #{$nav-pills-link-active-bg};\n  // scss-docs-end nav-pills-css-vars\n\n  .nav-link {\n    @include border-radius(var(--#{$prefix}nav-pills-border-radius));\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    color: var(--#{$prefix}nav-pills-link-active-color);\n    @include gradient-bg(var(--#{$prefix}nav-pills-link-active-bg));\n  }\n}\n\n\n//\n// Underline\n//\n\n.nav-underline {\n  // scss-docs-start nav-underline-css-vars\n  --#{$prefix}nav-underline-gap: #{$nav-underline-gap};\n  --#{$prefix}nav-underline-border-width: #{$nav-underline-border-width};\n  --#{$prefix}nav-underline-link-active-color: #{$nav-underline-link-active-color};\n  // scss-docs-end nav-underline-css-vars\n\n  gap: var(--#{$prefix}nav-underline-gap);\n\n  .nav-link {\n    padding-right: 0;\n    padding-left: 0;\n    border-bottom: var(--#{$prefix}nav-underline-border-width) solid transparent;\n\n    &:hover,\n    &:focus {\n      border-bottom-color: currentcolor;\n    }\n  }\n\n  .nav-link.active,\n  .show > .nav-link {\n    font-weight: $font-weight-bold;\n    color: var(--#{$prefix}nav-underline-link-active-color);\n    border-bottom-color: currentcolor;\n  }\n}\n\n\n//\n// Justified variants\n//\n\n.nav-fill {\n  > .nav-link,\n  .nav-item {\n    flex: 1 1 auto;\n    text-align: center;\n  }\n}\n\n.nav-justified {\n  > .nav-link,\n  .nav-item {\n    flex-basis: 0;\n    flex-grow: 1;\n    text-align: center;\n  }\n}\n\n.nav-fill,\n.nav-justified {\n  .nav-item .nav-link {\n    width: 100%; // Make sure button will grow\n  }\n}\n\n\n// Tabbable tabs\n//\n// Hide tabbable panes to start, show them when `.active`\n\n.tab-content {\n  > .tab-pane {\n    display: none;\n  }\n  > .active {\n    display: block;\n  }\n}\n","// Navbar\n//\n// Provide a static navbar from which we expand to create full-width, fixed, and\n// other navbar variations.\n\n.navbar {\n  // scss-docs-start navbar-css-vars\n  --#{$prefix}navbar-padding-x: #{if($navbar-padding-x == null, 0, $navbar-padding-x)};\n  --#{$prefix}navbar-padding-y: #{$navbar-padding-y};\n  --#{$prefix}navbar-color: #{$navbar-light-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-light-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-light-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-light-active-color};\n  --#{$prefix}navbar-brand-padding-y: #{$navbar-brand-padding-y};\n  --#{$prefix}navbar-brand-margin-end: #{$navbar-brand-margin-end};\n  --#{$prefix}navbar-brand-font-size: #{$navbar-brand-font-size};\n  --#{$prefix}navbar-brand-color: #{$navbar-light-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-light-brand-hover-color};\n  --#{$prefix}navbar-nav-link-padding-x: #{$navbar-nav-link-padding-x};\n  --#{$prefix}navbar-toggler-padding-y: #{$navbar-toggler-padding-y};\n  --#{$prefix}navbar-toggler-padding-x: #{$navbar-toggler-padding-x};\n  --#{$prefix}navbar-toggler-font-size: #{$navbar-toggler-font-size};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-light-toggler-icon-bg)};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-light-toggler-border-color};\n  --#{$prefix}navbar-toggler-border-radius: #{$navbar-toggler-border-radius};\n  --#{$prefix}navbar-toggler-focus-width: #{$navbar-toggler-focus-width};\n  --#{$prefix}navbar-toggler-transition: #{$navbar-toggler-transition};\n  // scss-docs-end navbar-css-vars\n\n  position: relative;\n  display: flex;\n  flex-wrap: wrap; // allow us to do the line break for collapsing content\n  align-items: center;\n  justify-content: space-between; // space out brand from logo\n  padding: var(--#{$prefix}navbar-padding-y) var(--#{$prefix}navbar-padding-x);\n  @include gradient-bg();\n\n  // Because flex properties aren't inherited, we need to redeclare these first\n  // few properties so that content nested within behave properly.\n  // The `flex-wrap` property is inherited to simplify the expanded navbars\n  %container-flex-properties {\n    display: flex;\n    flex-wrap: inherit;\n    align-items: center;\n    justify-content: space-between;\n  }\n\n  > .container,\n  > .container-fluid {\n    @extend %container-flex-properties;\n  }\n\n  @each $breakpoint, $container-max-width in $container-max-widths {\n    > .container#{breakpoint-infix($breakpoint, $container-max-widths)} {\n      @extend %container-flex-properties;\n    }\n  }\n}\n\n\n// Navbar brand\n//\n// Used for brand, project, or site names.\n\n.navbar-brand {\n  padding-top: var(--#{$prefix}navbar-brand-padding-y);\n  padding-bottom: var(--#{$prefix}navbar-brand-padding-y);\n  margin-right: var(--#{$prefix}navbar-brand-margin-end);\n  @include font-size(var(--#{$prefix}navbar-brand-font-size));\n  color: var(--#{$prefix}navbar-brand-color);\n  text-decoration: if($link-decoration == none, null, none);\n  white-space: nowrap;\n\n  &:hover,\n  &:focus {\n    color: var(--#{$prefix}navbar-brand-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n}\n\n\n// Navbar nav\n//\n// Custom navbar navigation (doesn't require `.nav`, but does make use of `.nav-link`).\n\n.navbar-nav {\n  // scss-docs-start navbar-nav-css-vars\n  --#{$prefix}nav-link-padding-x: 0;\n  --#{$prefix}nav-link-padding-y: #{$nav-link-padding-y};\n  @include rfs($nav-link-font-size, --#{$prefix}nav-link-font-size);\n  --#{$prefix}nav-link-font-weight: #{$nav-link-font-weight};\n  --#{$prefix}nav-link-color: var(--#{$prefix}navbar-color);\n  --#{$prefix}nav-link-hover-color: var(--#{$prefix}navbar-hover-color);\n  --#{$prefix}nav-link-disabled-color: var(--#{$prefix}navbar-disabled-color);\n  // scss-docs-end navbar-nav-css-vars\n\n  display: flex;\n  flex-direction: column; // cannot use `inherit` to get the `.navbar`s value\n  padding-left: 0;\n  margin-bottom: 0;\n  list-style: none;\n\n  .nav-link {\n    &.active,\n    &.show {\n      color: var(--#{$prefix}navbar-active-color);\n    }\n  }\n\n  .dropdown-menu {\n    position: static;\n  }\n}\n\n\n// Navbar text\n//\n//\n\n.navbar-text {\n  padding-top: $nav-link-padding-y;\n  padding-bottom: $nav-link-padding-y;\n  color: var(--#{$prefix}navbar-color);\n\n  a,\n  a:hover,\n  a:focus  {\n    color: var(--#{$prefix}navbar-active-color);\n  }\n}\n\n\n// Responsive navbar\n//\n// Custom styles for responsive collapsing and toggling of navbar contents.\n// Powered by the collapse Bootstrap JavaScript plugin.\n\n// When collapsed, prevent the toggleable navbar contents from appearing in\n// the default flexbox row orientation. Requires the use of `flex-wrap: wrap`\n// on the `.navbar` parent.\n.navbar-collapse {\n  flex-basis: 100%;\n  flex-grow: 1;\n  // For always expanded or extra full navbars, ensure content aligns itself\n  // properly vertically. Can be easily overridden with flex utilities.\n  align-items: center;\n}\n\n// Button for toggling the navbar when in its collapsed state\n.navbar-toggler {\n  padding: var(--#{$prefix}navbar-toggler-padding-y) var(--#{$prefix}navbar-toggler-padding-x);\n  @include font-size(var(--#{$prefix}navbar-toggler-font-size));\n  line-height: 1;\n  color: var(--#{$prefix}navbar-color);\n  background-color: transparent; // remove default button style\n  border: var(--#{$prefix}border-width) solid var(--#{$prefix}navbar-toggler-border-color); // remove default button style\n  @include border-radius(var(--#{$prefix}navbar-toggler-border-radius));\n  @include transition(var(--#{$prefix}navbar-toggler-transition));\n\n  &:hover {\n    text-decoration: none;\n  }\n\n  &:focus {\n    text-decoration: none;\n    outline: 0;\n    box-shadow: 0 0 0 var(--#{$prefix}navbar-toggler-focus-width);\n  }\n}\n\n// Keep as a separate element so folks can easily override it with another icon\n// or image file as needed.\n.navbar-toggler-icon {\n  display: inline-block;\n  width: 1.5em;\n  height: 1.5em;\n  vertical-align: middle;\n  background-image: var(--#{$prefix}navbar-toggler-icon-bg);\n  background-repeat: no-repeat;\n  background-position: center;\n  background-size: 100%;\n}\n\n.navbar-nav-scroll {\n  max-height: var(--#{$prefix}scroll-height, 75vh);\n  overflow-y: auto;\n}\n\n// scss-docs-start navbar-expand-loop\n// Generate series of `.navbar-expand-*` responsive classes for configuring\n// where your navbar collapses.\n.navbar-expand {\n  @each $breakpoint in map-keys($grid-breakpoints) {\n    $next: breakpoint-next($breakpoint, $grid-breakpoints);\n    $infix: breakpoint-infix($next, $grid-breakpoints);\n\n    // stylelint-disable-next-line scss/selector-no-union-class-name\n    &#{$infix} {\n      @include media-breakpoint-up($next) {\n        flex-wrap: nowrap;\n        justify-content: flex-start;\n\n        .navbar-nav {\n          flex-direction: row;\n\n          .dropdown-menu {\n            position: absolute;\n          }\n\n          .nav-link {\n            padding-right: var(--#{$prefix}navbar-nav-link-padding-x);\n            padding-left: var(--#{$prefix}navbar-nav-link-padding-x);\n          }\n        }\n\n        .navbar-nav-scroll {\n          overflow: visible;\n        }\n\n        .navbar-collapse {\n          display: flex !important; // stylelint-disable-line declaration-no-important\n          flex-basis: auto;\n        }\n\n        .navbar-toggler {\n          display: none;\n        }\n\n        .offcanvas {\n          // stylelint-disable declaration-no-important\n          position: static;\n          z-index: auto;\n          flex-grow: 1;\n          width: auto !important;\n          height: auto !important;\n          visibility: visible !important;\n          background-color: transparent !important;\n          border: 0 !important;\n          transform: none !important;\n          @include box-shadow(none);\n          @include transition(none);\n          // stylelint-enable declaration-no-important\n\n          .offcanvas-header {\n            display: none;\n          }\n\n          .offcanvas-body {\n            display: flex;\n            flex-grow: 0;\n            padding: 0;\n            overflow-y: visible;\n          }\n        }\n      }\n    }\n  }\n}\n// scss-docs-end navbar-expand-loop\n\n// Navbar themes\n//\n// Styles for switching between navbars with light or dark background.\n\n.navbar-light {\n  @include deprecate(\"`.navbar-light`\", \"v5.2.0\", \"v6.0.0\", true);\n}\n\n.navbar-dark,\n.navbar[data-bs-theme=\"dark\"] {\n  // scss-docs-start navbar-dark-css-vars\n  --#{$prefix}navbar-color: #{$navbar-dark-color};\n  --#{$prefix}navbar-hover-color: #{$navbar-dark-hover-color};\n  --#{$prefix}navbar-disabled-color: #{$navbar-dark-disabled-color};\n  --#{$prefix}navbar-active-color: #{$navbar-dark-active-color};\n  --#{$prefix}navbar-brand-color: #{$navbar-dark-brand-color};\n  --#{$prefix}navbar-brand-hover-color: #{$navbar-dark-brand-hover-color};\n  --#{$prefix}navbar-toggler-border-color: #{$navbar-dark-toggler-border-color};\n  --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n  // scss-docs-end navbar-dark-css-vars\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .navbar-toggler-icon {\n      --#{$prefix}navbar-toggler-icon-bg: #{escape-svg($navbar-dark-toggler-icon-bg)};\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.card {\n  // scss-docs-start card-css-vars\n  --#{$prefix}card-spacer-y: #{$card-spacer-y};\n  --#{$prefix}card-spacer-x: #{$card-spacer-x};\n  --#{$prefix}card-title-spacer-y: #{$card-title-spacer-y};\n  --#{$prefix}card-title-color: #{$card-title-color};\n  --#{$prefix}card-subtitle-color: #{$card-subtitle-color};\n  --#{$prefix}card-border-width: #{$card-border-width};\n  --#{$prefix}card-border-color: #{$card-border-color};\n  --#{$prefix}card-border-radius: #{$card-border-radius};\n  --#{$prefix}card-box-shadow: #{$card-box-shadow};\n  --#{$prefix}card-inner-border-radius: #{$card-inner-border-radius};\n  --#{$prefix}card-cap-padding-y: #{$card-cap-padding-y};\n  --#{$prefix}card-cap-padding-x: #{$card-cap-padding-x};\n  --#{$prefix}card-cap-bg: #{$card-cap-bg};\n  --#{$prefix}card-cap-color: #{$card-cap-color};\n  --#{$prefix}card-height: #{$card-height};\n  --#{$prefix}card-color: #{$card-color};\n  --#{$prefix}card-bg: #{$card-bg};\n  --#{$prefix}card-img-overlay-padding: #{$card-img-overlay-padding};\n  --#{$prefix}card-group-margin: #{$card-group-margin};\n  // scss-docs-end card-css-vars\n\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  min-width: 0; // See https://github.com/twbs/bootstrap/pull/22740#issuecomment-305868106\n  height: var(--#{$prefix}card-height);\n  color: var(--#{$prefix}body-color);\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}card-bg);\n  background-clip: border-box;\n  border: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n  @include border-radius(var(--#{$prefix}card-border-radius));\n  @include box-shadow(var(--#{$prefix}card-box-shadow));\n\n  > hr {\n    margin-right: 0;\n    margin-left: 0;\n  }\n\n  > .list-group {\n    border-top: inherit;\n    border-bottom: inherit;\n\n    &:first-child {\n      border-top-width: 0;\n      @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n\n    &:last-child  {\n      border-bottom-width: 0;\n      @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n    }\n  }\n\n  // Due to specificity of the above selector (`.card > .list-group`), we must\n  // use a child selector here to prevent double borders.\n  > .card-header + .list-group,\n  > .list-group + .card-footer {\n    border-top: 0;\n  }\n}\n\n.card-body {\n  // Enable `flex-grow: 1` for decks and groups so that card blocks take up\n  // as much space as possible, ensuring footers are aligned to the bottom.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}card-spacer-y) var(--#{$prefix}card-spacer-x);\n  color: var(--#{$prefix}card-color);\n}\n\n.card-title {\n  margin-bottom: var(--#{$prefix}card-title-spacer-y);\n  color: var(--#{$prefix}card-title-color);\n}\n\n.card-subtitle {\n  margin-top: calc(-.5 * var(--#{$prefix}card-title-spacer-y)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: 0;\n  color: var(--#{$prefix}card-subtitle-color);\n}\n\n.card-text:last-child {\n  margin-bottom: 0;\n}\n\n.card-link {\n  &:hover {\n    text-decoration: if($link-hover-decoration == underline, none, null);\n  }\n\n  + .card-link {\n    margin-left: var(--#{$prefix}card-spacer-x);\n  }\n}\n\n//\n// Optional textual caps\n//\n\n.card-header {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  margin-bottom: 0; // Removes the default margin-bottom of <hN>\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-bottom: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:first-child {\n    @include border-radius(var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius) 0 0);\n  }\n}\n\n.card-footer {\n  padding: var(--#{$prefix}card-cap-padding-y) var(--#{$prefix}card-cap-padding-x);\n  color: var(--#{$prefix}card-cap-color);\n  background-color: var(--#{$prefix}card-cap-bg);\n  border-top: var(--#{$prefix}card-border-width) solid var(--#{$prefix}card-border-color);\n\n  &:last-child {\n    @include border-radius(0 0 var(--#{$prefix}card-inner-border-radius) var(--#{$prefix}card-inner-border-radius));\n  }\n}\n\n\n//\n// Header navs\n//\n\n.card-header-tabs {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-bottom: calc(-1 * var(--#{$prefix}card-cap-padding-y)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  border-bottom: 0;\n\n  .nav-link.active {\n    background-color: var(--#{$prefix}card-bg);\n    border-bottom-color: var(--#{$prefix}card-bg);\n  }\n}\n\n.card-header-pills {\n  margin-right: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}card-cap-padding-x)); // stylelint-disable-line function-disallowed-list\n}\n\n// Card image\n.card-img-overlay {\n  position: absolute;\n  top: 0;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  padding: var(--#{$prefix}card-img-overlay-padding);\n  @include border-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-top,\n.card-img-bottom {\n  width: 100%; // Required because we use flexbox and this inherently applies align-self: stretch\n}\n\n.card-img,\n.card-img-top {\n  @include border-top-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n.card-img,\n.card-img-bottom {\n  @include border-bottom-radius(var(--#{$prefix}card-inner-border-radius));\n}\n\n\n//\n// Card groups\n//\n\n.card-group {\n  // The child selector allows nested `.card` within `.card-group`\n  // to display properly.\n  > .card {\n    margin-bottom: var(--#{$prefix}card-group-margin);\n  }\n\n  @include media-breakpoint-up(sm) {\n    display: flex;\n    flex-flow: row wrap;\n    // The child selector allows nested `.card` within `.card-group`\n    // to display properly.\n    > .card {\n      // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      flex: 1 0 0%;\n      margin-bottom: 0;\n\n      + .card {\n        margin-left: 0;\n        border-left: 0;\n      }\n\n      // Handle rounded corners\n      @if $enable-rounded {\n        &:not(:last-child) {\n          @include border-end-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-right-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-right-radius: 0;\n          }\n        }\n\n        &:not(:first-child) {\n          @include border-start-radius(0);\n\n          .card-img-top,\n          .card-header {\n            // stylelint-disable-next-line property-disallowed-list\n            border-top-left-radius: 0;\n          }\n          .card-img-bottom,\n          .card-footer {\n            // stylelint-disable-next-line property-disallowed-list\n            border-bottom-left-radius: 0;\n          }\n        }\n      }\n    }\n  }\n}\n","//\n// Base styles\n//\n\n.accordion {\n  // scss-docs-start accordion-css-vars\n  --#{$prefix}accordion-color: #{$accordion-color};\n  --#{$prefix}accordion-bg: #{$accordion-bg};\n  --#{$prefix}accordion-transition: #{$accordion-transition};\n  --#{$prefix}accordion-border-color: #{$accordion-border-color};\n  --#{$prefix}accordion-border-width: #{$accordion-border-width};\n  --#{$prefix}accordion-border-radius: #{$accordion-border-radius};\n  --#{$prefix}accordion-inner-border-radius: #{$accordion-inner-border-radius};\n  --#{$prefix}accordion-btn-padding-x: #{$accordion-button-padding-x};\n  --#{$prefix}accordion-btn-padding-y: #{$accordion-button-padding-y};\n  --#{$prefix}accordion-btn-color: #{$accordion-button-color};\n  --#{$prefix}accordion-btn-bg: #{$accordion-button-bg};\n  --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon)};\n  --#{$prefix}accordion-btn-icon-width: #{$accordion-icon-width};\n  --#{$prefix}accordion-btn-icon-transform: #{$accordion-icon-transform};\n  --#{$prefix}accordion-btn-icon-transition: #{$accordion-icon-transition};\n  --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon)};\n  --#{$prefix}accordion-btn-focus-border-color: #{$accordion-button-focus-border-color};\n  --#{$prefix}accordion-btn-focus-box-shadow: #{$accordion-button-focus-box-shadow};\n  --#{$prefix}accordion-body-padding-x: #{$accordion-body-padding-x};\n  --#{$prefix}accordion-body-padding-y: #{$accordion-body-padding-y};\n  --#{$prefix}accordion-active-color: #{$accordion-button-active-color};\n  --#{$prefix}accordion-active-bg: #{$accordion-button-active-bg};\n  // scss-docs-end accordion-css-vars\n}\n\n.accordion-button {\n  position: relative;\n  display: flex;\n  align-items: center;\n  width: 100%;\n  padding: var(--#{$prefix}accordion-btn-padding-y) var(--#{$prefix}accordion-btn-padding-x);\n  @include font-size($font-size-base);\n  color: var(--#{$prefix}accordion-btn-color);\n  text-align: left; // Reset button style\n  background-color: var(--#{$prefix}accordion-btn-bg);\n  border: 0;\n  @include border-radius(0);\n  overflow-anchor: none;\n  @include transition(var(--#{$prefix}accordion-transition));\n\n  &:not(.collapsed) {\n    color: var(--#{$prefix}accordion-active-color);\n    background-color: var(--#{$prefix}accordion-active-bg);\n    box-shadow: inset 0 calc(-1 * var(--#{$prefix}accordion-border-width)) 0 var(--#{$prefix}accordion-border-color); // stylelint-disable-line function-disallowed-list\n\n    &::after {\n      background-image: var(--#{$prefix}accordion-btn-active-icon);\n      transform: var(--#{$prefix}accordion-btn-icon-transform);\n    }\n  }\n\n  // Accordion icon\n  &::after {\n    flex-shrink: 0;\n    width: var(--#{$prefix}accordion-btn-icon-width);\n    height: var(--#{$prefix}accordion-btn-icon-width);\n    margin-left: auto;\n    content: \"\";\n    background-image: var(--#{$prefix}accordion-btn-icon);\n    background-repeat: no-repeat;\n    background-size: var(--#{$prefix}accordion-btn-icon-width);\n    @include transition(var(--#{$prefix}accordion-btn-icon-transition));\n  }\n\n  &:hover {\n    z-index: 2;\n  }\n\n  &:focus {\n    z-index: 3;\n    border-color: var(--#{$prefix}accordion-btn-focus-border-color);\n    outline: 0;\n    box-shadow: var(--#{$prefix}accordion-btn-focus-box-shadow);\n  }\n}\n\n.accordion-header {\n  margin-bottom: 0;\n}\n\n.accordion-item {\n  color: var(--#{$prefix}accordion-color);\n  background-color: var(--#{$prefix}accordion-bg);\n  border: var(--#{$prefix}accordion-border-width) solid var(--#{$prefix}accordion-border-color);\n\n  &:first-of-type {\n    @include border-top-radius(var(--#{$prefix}accordion-border-radius));\n\n    .accordion-button {\n      @include border-top-radius(var(--#{$prefix}accordion-inner-border-radius));\n    }\n  }\n\n  &:not(:first-of-type) {\n    border-top: 0;\n  }\n\n  // Only set a border-radius on the last item if the accordion is collapsed\n  &:last-of-type {\n    @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n\n    .accordion-button {\n      &.collapsed {\n        @include border-bottom-radius(var(--#{$prefix}accordion-inner-border-radius));\n      }\n    }\n\n    .accordion-collapse {\n      @include border-bottom-radius(var(--#{$prefix}accordion-border-radius));\n    }\n  }\n}\n\n.accordion-body {\n  padding: var(--#{$prefix}accordion-body-padding-y) var(--#{$prefix}accordion-body-padding-x);\n}\n\n\n// Flush accordion items\n//\n// Remove borders and border-radius to keep accordion items edge-to-edge.\n\n.accordion-flush {\n  .accordion-collapse {\n    border-width: 0;\n  }\n\n  .accordion-item {\n    border-right: 0;\n    border-left: 0;\n    @include border-radius(0);\n\n    &:first-child { border-top: 0; }\n    &:last-child { border-bottom: 0; }\n\n    .accordion-button {\n      &,\n      &.collapsed {\n        @include border-radius(0);\n      }\n    }\n  }\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .accordion-button::after {\n      --#{$prefix}accordion-btn-icon: #{escape-svg($accordion-button-icon-dark)};\n      --#{$prefix}accordion-btn-active-icon: #{escape-svg($accordion-button-active-icon-dark)};\n    }\n  }\n}\n",".breadcrumb {\n  // scss-docs-start breadcrumb-css-vars\n  --#{$prefix}breadcrumb-padding-x: #{$breadcrumb-padding-x};\n  --#{$prefix}breadcrumb-padding-y: #{$breadcrumb-padding-y};\n  --#{$prefix}breadcrumb-margin-bottom: #{$breadcrumb-margin-bottom};\n  @include rfs($breadcrumb-font-size, --#{$prefix}breadcrumb-font-size);\n  --#{$prefix}breadcrumb-bg: #{$breadcrumb-bg};\n  --#{$prefix}breadcrumb-border-radius: #{$breadcrumb-border-radius};\n  --#{$prefix}breadcrumb-divider-color: #{$breadcrumb-divider-color};\n  --#{$prefix}breadcrumb-item-padding-x: #{$breadcrumb-item-padding-x};\n  --#{$prefix}breadcrumb-item-active-color: #{$breadcrumb-active-color};\n  // scss-docs-end breadcrumb-css-vars\n\n  display: flex;\n  flex-wrap: wrap;\n  padding: var(--#{$prefix}breadcrumb-padding-y) var(--#{$prefix}breadcrumb-padding-x);\n  margin-bottom: var(--#{$prefix}breadcrumb-margin-bottom);\n  @include font-size(var(--#{$prefix}breadcrumb-font-size));\n  list-style: none;\n  background-color: var(--#{$prefix}breadcrumb-bg);\n  @include border-radius(var(--#{$prefix}breadcrumb-border-radius));\n}\n\n.breadcrumb-item {\n  // The separator between breadcrumbs (by default, a forward-slash: \"/\")\n  + .breadcrumb-item {\n    padding-left: var(--#{$prefix}breadcrumb-item-padding-x);\n\n    &::before {\n      float: left; // Suppress inline spacings and underlining of the separator\n      padding-right: var(--#{$prefix}breadcrumb-item-padding-x);\n      color: var(--#{$prefix}breadcrumb-divider-color);\n      content: var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider)) #{\"/* rtl:\"} var(--#{$prefix}breadcrumb-divider, escape-svg($breadcrumb-divider-flipped)) #{\"*/\"};\n    }\n  }\n\n  &.active {\n    color: var(--#{$prefix}breadcrumb-item-active-color);\n  }\n}\n",".pagination {\n  // scss-docs-start pagination-css-vars\n  --#{$prefix}pagination-padding-x: #{$pagination-padding-x};\n  --#{$prefix}pagination-padding-y: #{$pagination-padding-y};\n  @include rfs($pagination-font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-color: #{$pagination-color};\n  --#{$prefix}pagination-bg: #{$pagination-bg};\n  --#{$prefix}pagination-border-width: #{$pagination-border-width};\n  --#{$prefix}pagination-border-color: #{$pagination-border-color};\n  --#{$prefix}pagination-border-radius: #{$pagination-border-radius};\n  --#{$prefix}pagination-hover-color: #{$pagination-hover-color};\n  --#{$prefix}pagination-hover-bg: #{$pagination-hover-bg};\n  --#{$prefix}pagination-hover-border-color: #{$pagination-hover-border-color};\n  --#{$prefix}pagination-focus-color: #{$pagination-focus-color};\n  --#{$prefix}pagination-focus-bg: #{$pagination-focus-bg};\n  --#{$prefix}pagination-focus-box-shadow: #{$pagination-focus-box-shadow};\n  --#{$prefix}pagination-active-color: #{$pagination-active-color};\n  --#{$prefix}pagination-active-bg: #{$pagination-active-bg};\n  --#{$prefix}pagination-active-border-color: #{$pagination-active-border-color};\n  --#{$prefix}pagination-disabled-color: #{$pagination-disabled-color};\n  --#{$prefix}pagination-disabled-bg: #{$pagination-disabled-bg};\n  --#{$prefix}pagination-disabled-border-color: #{$pagination-disabled-border-color};\n  // scss-docs-end pagination-css-vars\n\n  display: flex;\n  @include list-unstyled();\n}\n\n.page-link {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}pagination-padding-y) var(--#{$prefix}pagination-padding-x);\n  @include font-size(var(--#{$prefix}pagination-font-size));\n  color: var(--#{$prefix}pagination-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}pagination-bg);\n  border: var(--#{$prefix}pagination-border-width) solid var(--#{$prefix}pagination-border-color);\n  @include transition($pagination-transition);\n\n  &:hover {\n    z-index: 2;\n    color: var(--#{$prefix}pagination-hover-color);\n    text-decoration: if($link-hover-decoration == underline, none, null);\n    background-color: var(--#{$prefix}pagination-hover-bg);\n    border-color: var(--#{$prefix}pagination-hover-border-color);\n  }\n\n  &:focus {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-focus-color);\n    background-color: var(--#{$prefix}pagination-focus-bg);\n    outline: $pagination-focus-outline;\n    box-shadow: var(--#{$prefix}pagination-focus-box-shadow);\n  }\n\n  &.active,\n  .active > & {\n    z-index: 3;\n    color: var(--#{$prefix}pagination-active-color);\n    @include gradient-bg(var(--#{$prefix}pagination-active-bg));\n    border-color: var(--#{$prefix}pagination-active-border-color);\n  }\n\n  &.disabled,\n  .disabled > & {\n    color: var(--#{$prefix}pagination-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}pagination-disabled-bg);\n    border-color: var(--#{$prefix}pagination-disabled-border-color);\n  }\n}\n\n.page-item {\n  &:not(:first-child) .page-link {\n    margin-left: $pagination-margin-start;\n  }\n\n  @if $pagination-margin-start == calc(#{$pagination-border-width} * -1) {\n    &:first-child {\n      .page-link {\n        @include border-start-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n\n    &:last-child {\n      .page-link {\n        @include border-end-radius(var(--#{$prefix}pagination-border-radius));\n      }\n    }\n  } @else {\n    // Add border-radius to all pageLinks in case they have left margin\n    .page-link {\n      @include border-radius(var(--#{$prefix}pagination-border-radius));\n    }\n  }\n}\n\n\n//\n// Sizing\n//\n\n.pagination-lg {\n  @include pagination-size($pagination-padding-y-lg, $pagination-padding-x-lg, $font-size-lg, $pagination-border-radius-lg);\n}\n\n.pagination-sm {\n  @include pagination-size($pagination-padding-y-sm, $pagination-padding-x-sm, $font-size-sm, $pagination-border-radius-sm);\n}\n","// Pagination\n\n// scss-docs-start pagination-mixin\n@mixin pagination-size($padding-y, $padding-x, $font-size, $border-radius) {\n  --#{$prefix}pagination-padding-x: #{$padding-x};\n  --#{$prefix}pagination-padding-y: #{$padding-y};\n  @include rfs($font-size, --#{$prefix}pagination-font-size);\n  --#{$prefix}pagination-border-radius: #{$border-radius};\n}\n// scss-docs-end pagination-mixin\n","// Base class\n//\n// Requires one of the contextual, color modifier classes for `color` and\n// `background-color`.\n\n.badge {\n  // scss-docs-start badge-css-vars\n  --#{$prefix}badge-padding-x: #{$badge-padding-x};\n  --#{$prefix}badge-padding-y: #{$badge-padding-y};\n  @include rfs($badge-font-size, --#{$prefix}badge-font-size);\n  --#{$prefix}badge-font-weight: #{$badge-font-weight};\n  --#{$prefix}badge-color: #{$badge-color};\n  --#{$prefix}badge-border-radius: #{$badge-border-radius};\n  // scss-docs-end badge-css-vars\n\n  display: inline-block;\n  padding: var(--#{$prefix}badge-padding-y) var(--#{$prefix}badge-padding-x);\n  @include font-size(var(--#{$prefix}badge-font-size));\n  font-weight: var(--#{$prefix}badge-font-weight);\n  line-height: 1;\n  color: var(--#{$prefix}badge-color);\n  text-align: center;\n  white-space: nowrap;\n  vertical-align: baseline;\n  @include border-radius(var(--#{$prefix}badge-border-radius));\n  @include gradient-bg();\n\n  // Empty badges collapse automatically\n  &:empty {\n    display: none;\n  }\n}\n\n// Quick fix for badges in buttons\n.btn .badge {\n  position: relative;\n  top: -1px;\n}\n","//\n// Base styles\n//\n\n.alert {\n  // scss-docs-start alert-css-vars\n  --#{$prefix}alert-bg: transparent;\n  --#{$prefix}alert-padding-x: #{$alert-padding-x};\n  --#{$prefix}alert-padding-y: #{$alert-padding-y};\n  --#{$prefix}alert-margin-bottom: #{$alert-margin-bottom};\n  --#{$prefix}alert-color: inherit;\n  --#{$prefix}alert-border-color: transparent;\n  --#{$prefix}alert-border: #{$alert-border-width} solid var(--#{$prefix}alert-border-color);\n  --#{$prefix}alert-border-radius: #{$alert-border-radius};\n  --#{$prefix}alert-link-color: inherit;\n  // scss-docs-end alert-css-vars\n\n  position: relative;\n  padding: var(--#{$prefix}alert-padding-y) var(--#{$prefix}alert-padding-x);\n  margin-bottom: var(--#{$prefix}alert-margin-bottom);\n  color: var(--#{$prefix}alert-color);\n  background-color: var(--#{$prefix}alert-bg);\n  border: var(--#{$prefix}alert-border);\n  @include border-radius(var(--#{$prefix}alert-border-radius));\n}\n\n// Headings for larger alerts\n.alert-heading {\n  // Specified to prevent conflicts of changing $headings-color\n  color: inherit;\n}\n\n// Provide class for links that match alerts\n.alert-link {\n  font-weight: $alert-link-font-weight;\n  color: var(--#{$prefix}alert-link-color);\n}\n\n\n// Dismissible alerts\n//\n// Expand the right padding and account for the close button's positioning.\n\n.alert-dismissible {\n  padding-right: $alert-dismissible-padding-r;\n\n  // Adjust close link position\n  .btn-close {\n    position: absolute;\n    top: 0;\n    right: 0;\n    z-index: $stretched-link-z-index + 1;\n    padding: $alert-padding-y * 1.25 $alert-padding-x;\n  }\n}\n\n\n// scss-docs-start alert-modifiers\n// Generate contextual modifier classes for colorizing the alert\n@each $state in map-keys($theme-colors) {\n  .alert-#{$state} {\n    --#{$prefix}alert-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}alert-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}alert-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}alert-link-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end alert-modifiers\n","// Disable animation if transitions are disabled\n\n// scss-docs-start progress-keyframes\n@if $enable-transitions {\n  @keyframes progress-bar-stripes {\n    0% { background-position-x: $progress-height; }\n  }\n}\n// scss-docs-end progress-keyframes\n\n.progress,\n.progress-stacked {\n  // scss-docs-start progress-css-vars\n  --#{$prefix}progress-height: #{$progress-height};\n  @include rfs($progress-font-size, --#{$prefix}progress-font-size);\n  --#{$prefix}progress-bg: #{$progress-bg};\n  --#{$prefix}progress-border-radius: #{$progress-border-radius};\n  --#{$prefix}progress-box-shadow: #{$progress-box-shadow};\n  --#{$prefix}progress-bar-color: #{$progress-bar-color};\n  --#{$prefix}progress-bar-bg: #{$progress-bar-bg};\n  --#{$prefix}progress-bar-transition: #{$progress-bar-transition};\n  // scss-docs-end progress-css-vars\n\n  display: flex;\n  height: var(--#{$prefix}progress-height);\n  overflow: hidden; // force rounded corners by cropping it\n  @include font-size(var(--#{$prefix}progress-font-size));\n  background-color: var(--#{$prefix}progress-bg);\n  @include border-radius(var(--#{$prefix}progress-border-radius));\n  @include box-shadow(var(--#{$prefix}progress-box-shadow));\n}\n\n.progress-bar {\n  display: flex;\n  flex-direction: column;\n  justify-content: center;\n  overflow: hidden;\n  color: var(--#{$prefix}progress-bar-color);\n  text-align: center;\n  white-space: nowrap;\n  background-color: var(--#{$prefix}progress-bar-bg);\n  @include transition(var(--#{$prefix}progress-bar-transition));\n}\n\n.progress-bar-striped {\n  @include gradient-striped();\n  background-size: var(--#{$prefix}progress-height) var(--#{$prefix}progress-height);\n}\n\n.progress-stacked > .progress {\n  overflow: visible;\n}\n\n.progress-stacked > .progress > .progress-bar {\n  width: 100%;\n}\n\n@if $enable-transitions {\n  .progress-bar-animated {\n    animation: $progress-bar-animation-timing progress-bar-stripes;\n\n    @if $enable-reduced-motion {\n      @media (prefers-reduced-motion: reduce) {\n        animation: none;\n      }\n    }\n  }\n}\n","// Base class\n//\n// Easily usable on <ul>, <ol>, or <div>.\n\n.list-group {\n  // scss-docs-start list-group-css-vars\n  --#{$prefix}list-group-color: #{$list-group-color};\n  --#{$prefix}list-group-bg: #{$list-group-bg};\n  --#{$prefix}list-group-border-color: #{$list-group-border-color};\n  --#{$prefix}list-group-border-width: #{$list-group-border-width};\n  --#{$prefix}list-group-border-radius: #{$list-group-border-radius};\n  --#{$prefix}list-group-item-padding-x: #{$list-group-item-padding-x};\n  --#{$prefix}list-group-item-padding-y: #{$list-group-item-padding-y};\n  --#{$prefix}list-group-action-color: #{$list-group-action-color};\n  --#{$prefix}list-group-action-hover-color: #{$list-group-action-hover-color};\n  --#{$prefix}list-group-action-hover-bg: #{$list-group-hover-bg};\n  --#{$prefix}list-group-action-active-color: #{$list-group-action-active-color};\n  --#{$prefix}list-group-action-active-bg: #{$list-group-action-active-bg};\n  --#{$prefix}list-group-disabled-color: #{$list-group-disabled-color};\n  --#{$prefix}list-group-disabled-bg: #{$list-group-disabled-bg};\n  --#{$prefix}list-group-active-color: #{$list-group-active-color};\n  --#{$prefix}list-group-active-bg: #{$list-group-active-bg};\n  --#{$prefix}list-group-active-border-color: #{$list-group-active-border-color};\n  // scss-docs-end list-group-css-vars\n\n  display: flex;\n  flex-direction: column;\n\n  // No need to set list-style: none; since .list-group-item is block level\n  padding-left: 0; // reset padding because ul and ol\n  margin-bottom: 0;\n  @include border-radius(var(--#{$prefix}list-group-border-radius));\n}\n\n.list-group-numbered {\n  list-style-type: none;\n  counter-reset: section;\n\n  > .list-group-item::before {\n    // Increments only this instance of the section counter\n    content: counters(section, \".\") \". \";\n    counter-increment: section;\n  }\n}\n\n// Interactive list items\n//\n// Use anchor or button elements instead of `li`s or `div`s to create interactive\n// list items. Includes an extra `.active` modifier class for selected items.\n\n.list-group-item-action {\n  width: 100%; // For `<button>`s (anchors become 100% by default though)\n  color: var(--#{$prefix}list-group-action-color);\n  text-align: inherit; // For `<button>`s (anchors inherit)\n\n  // Hover state\n  &:hover,\n  &:focus {\n    z-index: 1; // Place hover/focus items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-action-hover-color);\n    text-decoration: none;\n    background-color: var(--#{$prefix}list-group-action-hover-bg);\n  }\n\n  &:active {\n    color: var(--#{$prefix}list-group-action-active-color);\n    background-color: var(--#{$prefix}list-group-action-active-bg);\n  }\n}\n\n// Individual list items\n//\n// Use on `li`s or `div`s within the `.list-group` parent.\n\n.list-group-item {\n  position: relative;\n  display: block;\n  padding: var(--#{$prefix}list-group-item-padding-y) var(--#{$prefix}list-group-item-padding-x);\n  color: var(--#{$prefix}list-group-color);\n  text-decoration: if($link-decoration == none, null, none);\n  background-color: var(--#{$prefix}list-group-bg);\n  border: var(--#{$prefix}list-group-border-width) solid var(--#{$prefix}list-group-border-color);\n\n  &:first-child {\n    @include border-top-radius(inherit);\n  }\n\n  &:last-child {\n    @include border-bottom-radius(inherit);\n  }\n\n  &.disabled,\n  &:disabled {\n    color: var(--#{$prefix}list-group-disabled-color);\n    pointer-events: none;\n    background-color: var(--#{$prefix}list-group-disabled-bg);\n  }\n\n  // Include both here for `<a>`s and `<button>`s\n  &.active {\n    z-index: 2; // Place active items above their siblings for proper border styling\n    color: var(--#{$prefix}list-group-active-color);\n    background-color: var(--#{$prefix}list-group-active-bg);\n    border-color: var(--#{$prefix}list-group-active-border-color);\n  }\n\n  // stylelint-disable-next-line scss/selector-no-redundant-nesting-selector\n  & + .list-group-item {\n    border-top-width: 0;\n\n    &.active {\n      margin-top: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n      border-top-width: var(--#{$prefix}list-group-border-width);\n    }\n  }\n}\n\n// Horizontal\n//\n// Change the layout of list group items from vertical (default) to horizontal.\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .list-group-horizontal#{$infix} {\n      flex-direction: row;\n\n      > .list-group-item {\n        &:first-child:not(:last-child) {\n          @include border-bottom-start-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-top-end-radius(0);\n        }\n\n        &:last-child:not(:first-child) {\n          @include border-top-end-radius(var(--#{$prefix}list-group-border-radius));\n          @include border-bottom-start-radius(0);\n        }\n\n        &.active {\n          margin-top: 0;\n        }\n\n        + .list-group-item {\n          border-top-width: var(--#{$prefix}list-group-border-width);\n          border-left-width: 0;\n\n          &.active {\n            margin-left: calc(-1 * var(--#{$prefix}list-group-border-width)); // stylelint-disable-line function-disallowed-list\n            border-left-width: var(--#{$prefix}list-group-border-width);\n          }\n        }\n      }\n    }\n  }\n}\n\n\n// Flush list items\n//\n// Remove borders and border-radius to keep list group items edge-to-edge. Most\n// useful within other components (e.g., cards).\n\n.list-group-flush {\n  @include border-radius(0);\n\n  > .list-group-item {\n    border-width: 0 0 var(--#{$prefix}list-group-border-width);\n\n    &:last-child {\n      border-bottom-width: 0;\n    }\n  }\n}\n\n\n// scss-docs-start list-group-modifiers\n// List group contextual variants\n//\n// Add modifier classes to change text and background color on individual items.\n// Organizationally, this must come after the `:hover` states.\n\n@each $state in map-keys($theme-colors) {\n  .list-group-item-#{$state} {\n    --#{$prefix}list-group-color: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-bg: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-border-color: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-hover-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-hover-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-action-active-color: var(--#{$prefix}emphasis-color);\n    --#{$prefix}list-group-action-active-bg: var(--#{$prefix}#{$state}-border-subtle);\n    --#{$prefix}list-group-active-color: var(--#{$prefix}#{$state}-bg-subtle);\n    --#{$prefix}list-group-active-bg: var(--#{$prefix}#{$state}-text-emphasis);\n    --#{$prefix}list-group-active-border-color: var(--#{$prefix}#{$state}-text-emphasis);\n  }\n}\n// scss-docs-end list-group-modifiers\n","// Transparent background and border properties included for button version.\n// iOS requires the button element instead of an anchor tag.\n// If you want the anchor version, it requires `href=\"#\"`.\n// See https://developer.mozilla.org/en-US/docs/Web/Events/click#Safari_Mobile\n\n.btn-close {\n  // scss-docs-start close-css-vars\n  --#{$prefix}btn-close-color: #{$btn-close-color};\n  --#{$prefix}btn-close-bg: #{ escape-svg($btn-close-bg) };\n  --#{$prefix}btn-close-opacity: #{$btn-close-opacity};\n  --#{$prefix}btn-close-hover-opacity: #{$btn-close-hover-opacity};\n  --#{$prefix}btn-close-focus-shadow: #{$btn-close-focus-shadow};\n  --#{$prefix}btn-close-focus-opacity: #{$btn-close-focus-opacity};\n  --#{$prefix}btn-close-disabled-opacity: #{$btn-close-disabled-opacity};\n  --#{$prefix}btn-close-white-filter: #{$btn-close-white-filter};\n  // scss-docs-end close-css-vars\n\n  box-sizing: content-box;\n  width: $btn-close-width;\n  height: $btn-close-height;\n  padding: $btn-close-padding-y $btn-close-padding-x;\n  color: var(--#{$prefix}btn-close-color);\n  background: transparent var(--#{$prefix}btn-close-bg) center / $btn-close-width auto no-repeat; // include transparent for button elements\n  border: 0; // for button elements\n  @include border-radius();\n  opacity: var(--#{$prefix}btn-close-opacity);\n\n  // Override <a>'s hover style\n  &:hover {\n    color: var(--#{$prefix}btn-close-color);\n    text-decoration: none;\n    opacity: var(--#{$prefix}btn-close-hover-opacity);\n  }\n\n  &:focus {\n    outline: 0;\n    box-shadow: var(--#{$prefix}btn-close-focus-shadow);\n    opacity: var(--#{$prefix}btn-close-focus-opacity);\n  }\n\n  &:disabled,\n  &.disabled {\n    pointer-events: none;\n    user-select: none;\n    opacity: var(--#{$prefix}btn-close-disabled-opacity);\n  }\n}\n\n@mixin btn-close-white() {\n  filter: var(--#{$prefix}btn-close-white-filter);\n}\n\n.btn-close-white {\n  @include btn-close-white();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    .btn-close {\n      @include btn-close-white();\n    }\n  }\n}\n",".toast {\n  // scss-docs-start toast-css-vars\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n  --#{$prefix}toast-padding-x: #{$toast-padding-x};\n  --#{$prefix}toast-padding-y: #{$toast-padding-y};\n  --#{$prefix}toast-spacing: #{$toast-spacing};\n  --#{$prefix}toast-max-width: #{$toast-max-width};\n  @include rfs($toast-font-size, --#{$prefix}toast-font-size);\n  --#{$prefix}toast-color: #{$toast-color};\n  --#{$prefix}toast-bg: #{$toast-background-color};\n  --#{$prefix}toast-border-width: #{$toast-border-width};\n  --#{$prefix}toast-border-color: #{$toast-border-color};\n  --#{$prefix}toast-border-radius: #{$toast-border-radius};\n  --#{$prefix}toast-box-shadow: #{$toast-box-shadow};\n  --#{$prefix}toast-header-color: #{$toast-header-color};\n  --#{$prefix}toast-header-bg: #{$toast-header-background-color};\n  --#{$prefix}toast-header-border-color: #{$toast-header-border-color};\n  // scss-docs-end toast-css-vars\n\n  width: var(--#{$prefix}toast-max-width);\n  max-width: 100%;\n  @include font-size(var(--#{$prefix}toast-font-size));\n  color: var(--#{$prefix}toast-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}toast-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-border-color);\n  box-shadow: var(--#{$prefix}toast-box-shadow);\n  @include border-radius(var(--#{$prefix}toast-border-radius));\n\n  &.showing {\n    opacity: 0;\n  }\n\n  &:not(.show) {\n    display: none;\n  }\n}\n\n.toast-container {\n  --#{$prefix}toast-zindex: #{$zindex-toast};\n\n  position: absolute;\n  z-index: var(--#{$prefix}toast-zindex);\n  width: max-content;\n  max-width: 100%;\n  pointer-events: none;\n\n  > :not(:last-child) {\n    margin-bottom: var(--#{$prefix}toast-spacing);\n  }\n}\n\n.toast-header {\n  display: flex;\n  align-items: center;\n  padding: var(--#{$prefix}toast-padding-y) var(--#{$prefix}toast-padding-x);\n  color: var(--#{$prefix}toast-header-color);\n  background-color: var(--#{$prefix}toast-header-bg);\n  background-clip: padding-box;\n  border-bottom: var(--#{$prefix}toast-border-width) solid var(--#{$prefix}toast-header-border-color);\n  @include border-top-radius(calc(var(--#{$prefix}toast-border-radius) - var(--#{$prefix}toast-border-width)));\n\n  .btn-close {\n    margin-right: calc(-.5 * var(--#{$prefix}toast-padding-x)); // stylelint-disable-line function-disallowed-list\n    margin-left: var(--#{$prefix}toast-padding-x);\n  }\n}\n\n.toast-body {\n  padding: var(--#{$prefix}toast-padding-x);\n  word-wrap: break-word;\n}\n","// stylelint-disable function-disallowed-list\n\n// .modal-open      - body class for killing the scroll\n// .modal           - container to scroll within\n// .modal-dialog    - positioning shell for the actual modal\n// .modal-content   - actual modal w/ bg and corners and stuff\n\n\n// Container that the modal scrolls within\n.modal {\n  // scss-docs-start modal-css-vars\n  --#{$prefix}modal-zindex: #{$zindex-modal};\n  --#{$prefix}modal-width: #{$modal-md};\n  --#{$prefix}modal-padding: #{$modal-inner-padding};\n  --#{$prefix}modal-margin: #{$modal-dialog-margin};\n  --#{$prefix}modal-color: #{$modal-content-color};\n  --#{$prefix}modal-bg: #{$modal-content-bg};\n  --#{$prefix}modal-border-color: #{$modal-content-border-color};\n  --#{$prefix}modal-border-width: #{$modal-content-border-width};\n  --#{$prefix}modal-border-radius: #{$modal-content-border-radius};\n  --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-xs};\n  --#{$prefix}modal-inner-border-radius: #{$modal-content-inner-border-radius};\n  --#{$prefix}modal-header-padding-x: #{$modal-header-padding-x};\n  --#{$prefix}modal-header-padding-y: #{$modal-header-padding-y};\n  --#{$prefix}modal-header-padding: #{$modal-header-padding}; // Todo in v6: Split this padding into x and y\n  --#{$prefix}modal-header-border-color: #{$modal-header-border-color};\n  --#{$prefix}modal-header-border-width: #{$modal-header-border-width};\n  --#{$prefix}modal-title-line-height: #{$modal-title-line-height};\n  --#{$prefix}modal-footer-gap: #{$modal-footer-margin-between};\n  --#{$prefix}modal-footer-bg: #{$modal-footer-bg};\n  --#{$prefix}modal-footer-border-color: #{$modal-footer-border-color};\n  --#{$prefix}modal-footer-border-width: #{$modal-footer-border-width};\n  // scss-docs-end modal-css-vars\n\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: var(--#{$prefix}modal-zindex);\n  display: none;\n  width: 100%;\n  height: 100%;\n  overflow-x: hidden;\n  overflow-y: auto;\n  // Prevent Chrome on Windows from adding a focus outline. For details, see\n  // https://github.com/twbs/bootstrap/pull/10951.\n  outline: 0;\n  // We deliberately don't use `-webkit-overflow-scrolling: touch;` due to a\n  // gnarly iOS Safari bug: https://bugs.webkit.org/show_bug.cgi?id=158342\n  // See also https://github.com/twbs/bootstrap/issues/17695\n}\n\n// Shell div to position the modal with bottom padding\n.modal-dialog {\n  position: relative;\n  width: auto;\n  margin: var(--#{$prefix}modal-margin);\n  // allow clicks to pass through for custom click handling to close modal\n  pointer-events: none;\n\n  // When fading in the modal, animate it to slide down\n  .modal.fade & {\n    @include transition($modal-transition);\n    transform: $modal-fade-transform;\n  }\n  .modal.show & {\n    transform: $modal-show-transform;\n  }\n\n  // When trying to close, animate focus to scale\n  .modal.modal-static & {\n    transform: $modal-scale-transform;\n  }\n}\n\n.modal-dialog-scrollable {\n  height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n\n  .modal-content {\n    max-height: 100%;\n    overflow: hidden;\n  }\n\n  .modal-body {\n    overflow-y: auto;\n  }\n}\n\n.modal-dialog-centered {\n  display: flex;\n  align-items: center;\n  min-height: calc(100% - var(--#{$prefix}modal-margin) * 2);\n}\n\n// Actual modal\n.modal-content {\n  position: relative;\n  display: flex;\n  flex-direction: column;\n  width: 100%; // Ensure `.modal-content` extends the full width of the parent `.modal-dialog`\n  // counteract the pointer-events: none; in the .modal-dialog\n  color: var(--#{$prefix}modal-color);\n  pointer-events: auto;\n  background-color: var(--#{$prefix}modal-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}modal-border-width) solid var(--#{$prefix}modal-border-color);\n  @include border-radius(var(--#{$prefix}modal-border-radius));\n  @include box-shadow(var(--#{$prefix}modal-box-shadow));\n  // Remove focus outline from opened modal\n  outline: 0;\n}\n\n// Modal background\n.modal-backdrop {\n  // scss-docs-start modal-backdrop-css-vars\n  --#{$prefix}backdrop-zindex: #{$zindex-modal-backdrop};\n  --#{$prefix}backdrop-bg: #{$modal-backdrop-bg};\n  --#{$prefix}backdrop-opacity: #{$modal-backdrop-opacity};\n  // scss-docs-end modal-backdrop-css-vars\n\n  @include overlay-backdrop(var(--#{$prefix}backdrop-zindex), var(--#{$prefix}backdrop-bg), var(--#{$prefix}backdrop-opacity));\n}\n\n// Modal header\n// Top section of the modal w/ title and dismiss\n.modal-header {\n  display: flex;\n  flex-shrink: 0;\n  align-items: center;\n  justify-content: space-between; // Put modal header elements (title and dismiss) on opposite ends\n  padding: var(--#{$prefix}modal-header-padding);\n  border-bottom: var(--#{$prefix}modal-header-border-width) solid var(--#{$prefix}modal-header-border-color);\n  @include border-top-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}modal-header-padding-y) * .5) calc(var(--#{$prefix}modal-header-padding-x) * .5);\n    margin: calc(-.5 * var(--#{$prefix}modal-header-padding-y)) calc(-.5 * var(--#{$prefix}modal-header-padding-x)) calc(-.5 * var(--#{$prefix}modal-header-padding-y)) auto;\n  }\n}\n\n// Title text within header\n.modal-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}modal-title-line-height);\n}\n\n// Modal body\n// Where all modal content resides (sibling of .modal-header and .modal-footer)\n.modal-body {\n  position: relative;\n  // Enable `flex-grow: 1` so that the body take up as much space as possible\n  // when there should be a fixed height on `.modal-dialog`.\n  flex: 1 1 auto;\n  padding: var(--#{$prefix}modal-padding);\n}\n\n// Footer (for actions)\n.modal-footer {\n  display: flex;\n  flex-shrink: 0;\n  flex-wrap: wrap;\n  align-items: center; // vertically center\n  justify-content: flex-end; // Right align buttons with flex property because text-align doesn't work on flex items\n  padding: calc(var(--#{$prefix}modal-padding) - var(--#{$prefix}modal-footer-gap) * .5);\n  background-color: var(--#{$prefix}modal-footer-bg);\n  border-top: var(--#{$prefix}modal-footer-border-width) solid var(--#{$prefix}modal-footer-border-color);\n  @include border-bottom-radius(var(--#{$prefix}modal-inner-border-radius));\n\n  // Place margin between footer elements\n  // This solution is far from ideal because of the universal selector usage,\n  // but is needed to fix https://github.com/twbs/bootstrap/issues/24800\n  > * {\n    margin: calc(var(--#{$prefix}modal-footer-gap) * .5); // Todo in v6: replace with gap on parent class\n  }\n}\n\n// Scale up the modal\n@include media-breakpoint-up(sm) {\n  .modal {\n    --#{$prefix}modal-margin: #{$modal-dialog-margin-y-sm-up};\n    --#{$prefix}modal-box-shadow: #{$modal-content-box-shadow-sm-up};\n  }\n\n  // Automatically set modal's width for larger viewports\n  .modal-dialog {\n    max-width: var(--#{$prefix}modal-width);\n    margin-right: auto;\n    margin-left: auto;\n  }\n\n  .modal-sm {\n    --#{$prefix}modal-width: #{$modal-sm};\n  }\n}\n\n@include media-breakpoint-up(lg) {\n  .modal-lg,\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-lg};\n  }\n}\n\n@include media-breakpoint-up(xl) {\n  .modal-xl {\n    --#{$prefix}modal-width: #{$modal-xl};\n  }\n}\n\n// scss-docs-start modal-fullscreen-loop\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n  $postfix: if($infix != \"\", $infix + \"-down\", \"\");\n\n  @include media-breakpoint-down($breakpoint) {\n    .modal-fullscreen#{$postfix} {\n      width: 100vw;\n      max-width: none;\n      height: 100%;\n      margin: 0;\n\n      .modal-content {\n        height: 100%;\n        border: 0;\n        @include border-radius(0);\n      }\n\n      .modal-header,\n      .modal-footer {\n        @include border-radius(0);\n      }\n\n      .modal-body {\n        overflow-y: auto;\n      }\n    }\n  }\n}\n// scss-docs-end modal-fullscreen-loop\n","// Shared between modals and offcanvases\n@mixin overlay-backdrop($zindex, $backdrop-bg, $backdrop-opacity) {\n  position: fixed;\n  top: 0;\n  left: 0;\n  z-index: $zindex;\n  width: 100vw;\n  height: 100vh;\n  background-color: $backdrop-bg;\n\n  // Fade for backdrop\n  &.fade { opacity: 0; }\n  &.show { opacity: $backdrop-opacity; }\n}\n","// Base class\n.tooltip {\n  // scss-docs-start tooltip-css-vars\n  --#{$prefix}tooltip-zindex: #{$zindex-tooltip};\n  --#{$prefix}tooltip-max-width: #{$tooltip-max-width};\n  --#{$prefix}tooltip-padding-x: #{$tooltip-padding-x};\n  --#{$prefix}tooltip-padding-y: #{$tooltip-padding-y};\n  --#{$prefix}tooltip-margin: #{$tooltip-margin};\n  @include rfs($tooltip-font-size, --#{$prefix}tooltip-font-size);\n  --#{$prefix}tooltip-color: #{$tooltip-color};\n  --#{$prefix}tooltip-bg: #{$tooltip-bg};\n  --#{$prefix}tooltip-border-radius: #{$tooltip-border-radius};\n  --#{$prefix}tooltip-opacity: #{$tooltip-opacity};\n  --#{$prefix}tooltip-arrow-width: #{$tooltip-arrow-width};\n  --#{$prefix}tooltip-arrow-height: #{$tooltip-arrow-height};\n  // scss-docs-end tooltip-css-vars\n\n  z-index: var(--#{$prefix}tooltip-zindex);\n  display: block;\n  margin: var(--#{$prefix}tooltip-margin);\n  @include deprecate(\"`$tooltip-margin`\", \"v5\", \"v5.x\", true);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}tooltip-font-size));\n  // Allow breaking very long words so they don't overflow the tooltip's bounds\n  word-wrap: break-word;\n  opacity: 0;\n\n  &.show { opacity: var(--#{$prefix}tooltip-opacity); }\n\n  .tooltip-arrow {\n    display: block;\n    width: var(--#{$prefix}tooltip-arrow-width);\n    height: var(--#{$prefix}tooltip-arrow-height);\n\n    &::before {\n      position: absolute;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n    }\n  }\n}\n\n.bs-tooltip-top .tooltip-arrow {\n  bottom: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    top: -1px;\n    border-width: var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-top-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-end .tooltip-arrow {\n  left: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    right: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height) calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    border-right-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-bottom .tooltip-arrow {\n  top: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n\n  &::before {\n    bottom: -1px;\n    border-width: 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-bottom-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-tooltip-start .tooltip-arrow {\n  right: calc(-1 * var(--#{$prefix}tooltip-arrow-height)); // stylelint-disable-line function-disallowed-list\n  width: var(--#{$prefix}tooltip-arrow-height);\n  height: var(--#{$prefix}tooltip-arrow-width);\n\n  &::before {\n    left: -1px;\n    border-width: calc(var(--#{$prefix}tooltip-arrow-width) * .5) 0 calc(var(--#{$prefix}tooltip-arrow-width) * .5) var(--#{$prefix}tooltip-arrow-height); // stylelint-disable-line function-disallowed-list\n    border-left-color: var(--#{$prefix}tooltip-bg);\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-tooltip-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-tooltip-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-tooltip-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-tooltip-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-tooltip-start;\n  }\n}\n\n// Wrapper for the tooltip content\n.tooltip-inner {\n  max-width: var(--#{$prefix}tooltip-max-width);\n  padding: var(--#{$prefix}tooltip-padding-y) var(--#{$prefix}tooltip-padding-x);\n  color: var(--#{$prefix}tooltip-color);\n  text-align: center;\n  background-color: var(--#{$prefix}tooltip-bg);\n  @include border-radius(var(--#{$prefix}tooltip-border-radius));\n}\n","@mixin reset-text {\n  font-family: $font-family-base;\n  // We deliberately do NOT reset font-size or overflow-wrap / word-wrap.\n  font-style: normal;\n  font-weight: $font-weight-normal;\n  line-height: $line-height-base;\n  text-align: left; // Fallback for where `start` is not supported\n  text-align: start;\n  text-decoration: none;\n  text-shadow: none;\n  text-transform: none;\n  letter-spacing: normal;\n  word-break: normal;\n  white-space: normal;\n  word-spacing: normal;\n  line-break: auto;\n}\n",".popover {\n  // scss-docs-start popover-css-vars\n  --#{$prefix}popover-zindex: #{$zindex-popover};\n  --#{$prefix}popover-max-width: #{$popover-max-width};\n  @include rfs($popover-font-size, --#{$prefix}popover-font-size);\n  --#{$prefix}popover-bg: #{$popover-bg};\n  --#{$prefix}popover-border-width: #{$popover-border-width};\n  --#{$prefix}popover-border-color: #{$popover-border-color};\n  --#{$prefix}popover-border-radius: #{$popover-border-radius};\n  --#{$prefix}popover-inner-border-radius: #{$popover-inner-border-radius};\n  --#{$prefix}popover-box-shadow: #{$popover-box-shadow};\n  --#{$prefix}popover-header-padding-x: #{$popover-header-padding-x};\n  --#{$prefix}popover-header-padding-y: #{$popover-header-padding-y};\n  @include rfs($popover-header-font-size, --#{$prefix}popover-header-font-size);\n  --#{$prefix}popover-header-color: #{$popover-header-color};\n  --#{$prefix}popover-header-bg: #{$popover-header-bg};\n  --#{$prefix}popover-body-padding-x: #{$popover-body-padding-x};\n  --#{$prefix}popover-body-padding-y: #{$popover-body-padding-y};\n  --#{$prefix}popover-body-color: #{$popover-body-color};\n  --#{$prefix}popover-arrow-width: #{$popover-arrow-width};\n  --#{$prefix}popover-arrow-height: #{$popover-arrow-height};\n  --#{$prefix}popover-arrow-border: var(--#{$prefix}popover-border-color);\n  // scss-docs-end popover-css-vars\n\n  z-index: var(--#{$prefix}popover-zindex);\n  display: block;\n  max-width: var(--#{$prefix}popover-max-width);\n  // Our parent element can be arbitrary since tooltips are by default inserted as a sibling of their target element.\n  // So reset our font and text properties to avoid inheriting weird values.\n  @include reset-text();\n  @include font-size(var(--#{$prefix}popover-font-size));\n  // Allow breaking very long words so they don't overflow the popover's bounds\n  word-wrap: break-word;\n  background-color: var(--#{$prefix}popover-bg);\n  background-clip: padding-box;\n  border: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-radius(var(--#{$prefix}popover-border-radius));\n  @include box-shadow(var(--#{$prefix}popover-box-shadow));\n\n  .popover-arrow {\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    height: var(--#{$prefix}popover-arrow-height);\n\n    &::before,\n    &::after {\n      position: absolute;\n      display: block;\n      content: \"\";\n      border-color: transparent;\n      border-style: solid;\n      border-width: 0;\n    }\n  }\n}\n\n.bs-popover-top {\n  > .popover-arrow {\n    bottom: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      bottom: 0;\n      border-top-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      bottom: var(--#{$prefix}popover-border-width);\n      border-top-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-end {\n  > .popover-arrow {\n    left: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height) calc(var(--#{$prefix}popover-arrow-width) * .5) 0; // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      left: 0;\n      border-right-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      left: var(--#{$prefix}popover-border-width);\n      border-right-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-bottom {\n  > .popover-arrow {\n    top: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n\n    &::before,\n    &::after {\n      border-width: 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      top: 0;\n      border-bottom-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      top: var(--#{$prefix}popover-border-width);\n      border-bottom-color: var(--#{$prefix}popover-bg);\n    }\n  }\n\n  // This will remove the popover-header's border just below the arrow\n  .popover-header::before {\n    position: absolute;\n    top: 0;\n    left: 50%;\n    display: block;\n    width: var(--#{$prefix}popover-arrow-width);\n    margin-left: calc(-.5 * var(--#{$prefix}popover-arrow-width)); // stylelint-disable-line function-disallowed-list\n    content: \"\";\n    border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-header-bg);\n  }\n}\n\n/* rtl:begin:ignore */\n.bs-popover-start {\n  > .popover-arrow {\n    right: calc(-1 * (var(--#{$prefix}popover-arrow-height)) - var(--#{$prefix}popover-border-width)); // stylelint-disable-line function-disallowed-list\n    width: var(--#{$prefix}popover-arrow-height);\n    height: var(--#{$prefix}popover-arrow-width);\n\n    &::before,\n    &::after {\n      border-width: calc(var(--#{$prefix}popover-arrow-width) * .5) 0 calc(var(--#{$prefix}popover-arrow-width) * .5) var(--#{$prefix}popover-arrow-height); // stylelint-disable-line function-disallowed-list\n    }\n\n    &::before {\n      right: 0;\n      border-left-color: var(--#{$prefix}popover-arrow-border);\n    }\n\n    &::after {\n      right: var(--#{$prefix}popover-border-width);\n      border-left-color: var(--#{$prefix}popover-bg);\n    }\n  }\n}\n\n/* rtl:end:ignore */\n\n.bs-popover-auto {\n  &[data-popper-placement^=\"top\"] {\n    @extend .bs-popover-top;\n  }\n  &[data-popper-placement^=\"right\"] {\n    @extend .bs-popover-end;\n  }\n  &[data-popper-placement^=\"bottom\"] {\n    @extend .bs-popover-bottom;\n  }\n  &[data-popper-placement^=\"left\"] {\n    @extend .bs-popover-start;\n  }\n}\n\n// Offset the popover to account for the popover arrow\n.popover-header {\n  padding: var(--#{$prefix}popover-header-padding-y) var(--#{$prefix}popover-header-padding-x);\n  margin-bottom: 0; // Reset the default from Reboot\n  @include font-size(var(--#{$prefix}popover-header-font-size));\n  color: var(--#{$prefix}popover-header-color);\n  background-color: var(--#{$prefix}popover-header-bg);\n  border-bottom: var(--#{$prefix}popover-border-width) solid var(--#{$prefix}popover-border-color);\n  @include border-top-radius(var(--#{$prefix}popover-inner-border-radius));\n\n  &:empty {\n    display: none;\n  }\n}\n\n.popover-body {\n  padding: var(--#{$prefix}popover-body-padding-y) var(--#{$prefix}popover-body-padding-x);\n  color: var(--#{$prefix}popover-body-color);\n}\n","// Notes on the classes:\n//\n// 1. .carousel.pointer-event should ideally be pan-y (to allow for users to scroll vertically)\n//    even when their scroll action started on a carousel, but for compatibility (with Firefox)\n//    we're preventing all actions instead\n// 2. The .carousel-item-start and .carousel-item-end is used to indicate where\n//    the active slide is heading.\n// 3. .active.carousel-item is the current slide.\n// 4. .active.carousel-item-start and .active.carousel-item-end is the current\n//    slide in its in-transition state. Only one of these occurs at a time.\n// 5. .carousel-item-next.carousel-item-start and .carousel-item-prev.carousel-item-end\n//    is the upcoming slide in transition.\n\n.carousel {\n  position: relative;\n}\n\n.carousel.pointer-event {\n  touch-action: pan-y;\n}\n\n.carousel-inner {\n  position: relative;\n  width: 100%;\n  overflow: hidden;\n  @include clearfix();\n}\n\n.carousel-item {\n  position: relative;\n  display: none;\n  float: left;\n  width: 100%;\n  margin-right: -100%;\n  backface-visibility: hidden;\n  @include transition($carousel-transition);\n}\n\n.carousel-item.active,\n.carousel-item-next,\n.carousel-item-prev {\n  display: block;\n}\n\n.carousel-item-next:not(.carousel-item-start),\n.active.carousel-item-end {\n  transform: translateX(100%);\n}\n\n.carousel-item-prev:not(.carousel-item-end),\n.active.carousel-item-start {\n  transform: translateX(-100%);\n}\n\n\n//\n// Alternate transitions\n//\n\n.carousel-fade {\n  .carousel-item {\n    opacity: 0;\n    transition-property: opacity;\n    transform: none;\n  }\n\n  .carousel-item.active,\n  .carousel-item-next.carousel-item-start,\n  .carousel-item-prev.carousel-item-end {\n    z-index: 1;\n    opacity: 1;\n  }\n\n  .active.carousel-item-start,\n  .active.carousel-item-end {\n    z-index: 0;\n    opacity: 0;\n    @include transition(opacity 0s $carousel-transition-duration);\n  }\n}\n\n\n//\n// Left/right controls for nav\n//\n\n.carousel-control-prev,\n.carousel-control-next {\n  position: absolute;\n  top: 0;\n  bottom: 0;\n  z-index: 1;\n  // Use flex for alignment (1-3)\n  display: flex; // 1. allow flex styles\n  align-items: center; // 2. vertically center contents\n  justify-content: center; // 3. horizontally center contents\n  width: $carousel-control-width;\n  padding: 0;\n  color: $carousel-control-color;\n  text-align: center;\n  background: none;\n  border: 0;\n  opacity: $carousel-control-opacity;\n  @include transition($carousel-control-transition);\n\n  // Hover/focus state\n  &:hover,\n  &:focus {\n    color: $carousel-control-color;\n    text-decoration: none;\n    outline: 0;\n    opacity: $carousel-control-hover-opacity;\n  }\n}\n.carousel-control-prev {\n  left: 0;\n  background-image: if($enable-gradients, linear-gradient(90deg, rgba($black, .25), rgba($black, .001)), null);\n}\n.carousel-control-next {\n  right: 0;\n  background-image: if($enable-gradients, linear-gradient(270deg, rgba($black, .25), rgba($black, .001)), null);\n}\n\n// Icons for within\n.carousel-control-prev-icon,\n.carousel-control-next-icon {\n  display: inline-block;\n  width: $carousel-control-icon-width;\n  height: $carousel-control-icon-width;\n  background-repeat: no-repeat;\n  background-position: 50%;\n  background-size: 100% 100%;\n}\n\n/* rtl:options: {\n  \"autoRename\": true,\n  \"stringMap\":[ {\n    \"name\"    : \"prev-next\",\n    \"search\"  : \"prev\",\n    \"replace\" : \"next\"\n  } ]\n} */\n.carousel-control-prev-icon {\n  background-image: escape-svg($carousel-control-prev-icon-bg);\n}\n.carousel-control-next-icon {\n  background-image: escape-svg($carousel-control-next-icon-bg);\n}\n\n// Optional indicator pips/controls\n//\n// Add a container (such as a list) with the following class and add an item (ideally a focusable control,\n// like a button) with data-bs-target for each slide your carousel holds.\n\n.carousel-indicators {\n  position: absolute;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: 2;\n  display: flex;\n  justify-content: center;\n  padding: 0;\n  // Use the .carousel-control's width as margin so we don't overlay those\n  margin-right: $carousel-control-width;\n  margin-bottom: 1rem;\n  margin-left: $carousel-control-width;\n\n  [data-bs-target] {\n    box-sizing: content-box;\n    flex: 0 1 auto;\n    width: $carousel-indicator-width;\n    height: $carousel-indicator-height;\n    padding: 0;\n    margin-right: $carousel-indicator-spacer;\n    margin-left: $carousel-indicator-spacer;\n    text-indent: -999px;\n    cursor: pointer;\n    background-color: $carousel-indicator-active-bg;\n    background-clip: padding-box;\n    border: 0;\n    // Use transparent borders to increase the hit area by 10px on top and bottom.\n    border-top: $carousel-indicator-hit-area-height solid transparent;\n    border-bottom: $carousel-indicator-hit-area-height solid transparent;\n    opacity: $carousel-indicator-opacity;\n    @include transition($carousel-indicator-transition);\n  }\n\n  .active {\n    opacity: $carousel-indicator-active-opacity;\n  }\n}\n\n\n// Optional captions\n//\n//\n\n.carousel-caption {\n  position: absolute;\n  right: (100% - $carousel-caption-width) * .5;\n  bottom: $carousel-caption-spacer;\n  left: (100% - $carousel-caption-width) * .5;\n  padding-top: $carousel-caption-padding-y;\n  padding-bottom: $carousel-caption-padding-y;\n  color: $carousel-caption-color;\n  text-align: center;\n}\n\n// Dark mode carousel\n\n@mixin carousel-dark() {\n  .carousel-control-prev-icon,\n  .carousel-control-next-icon {\n    filter: $carousel-dark-control-icon-filter;\n  }\n\n  .carousel-indicators [data-bs-target] {\n    background-color: $carousel-dark-indicator-active-bg;\n  }\n\n  .carousel-caption {\n    color: $carousel-dark-caption-color;\n  }\n}\n\n.carousel-dark {\n  @include carousel-dark();\n}\n\n@if $enable-dark-mode {\n  @include color-mode(dark) {\n    @if $color-mode-type == \"media-query\" {\n      .carousel {\n        @include carousel-dark();\n      }\n    } @else {\n      .carousel,\n      &.carousel {\n        @include carousel-dark();\n      }\n    }\n  }\n}\n","// scss-docs-start clearfix\n@mixin clearfix() {\n  &::after {\n    display: block;\n    clear: both;\n    content: \"\";\n  }\n}\n// scss-docs-end clearfix\n","//\n// Rotating border\n//\n\n.spinner-grow,\n.spinner-border {\n  display: inline-block;\n  width: var(--#{$prefix}spinner-width);\n  height: var(--#{$prefix}spinner-height);\n  vertical-align: var(--#{$prefix}spinner-vertical-align);\n  // stylelint-disable-next-line property-disallowed-list\n  border-radius: 50%;\n  animation: var(--#{$prefix}spinner-animation-speed) linear infinite var(--#{$prefix}spinner-animation-name);\n}\n\n// scss-docs-start spinner-border-keyframes\n@keyframes spinner-border {\n  to { transform: rotate(360deg) #{\"/* rtl:ignore */\"}; }\n}\n// scss-docs-end spinner-border-keyframes\n\n.spinner-border {\n  // scss-docs-start spinner-border-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-border;\n  // scss-docs-end spinner-border-css-vars\n\n  border: var(--#{$prefix}spinner-border-width) solid currentcolor;\n  border-right-color: transparent;\n}\n\n.spinner-border-sm {\n  // scss-docs-start spinner-border-sm-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n  --#{$prefix}spinner-border-width: #{$spinner-border-width-sm};\n  // scss-docs-end spinner-border-sm-css-vars\n}\n\n//\n// Growing circle\n//\n\n// scss-docs-start spinner-grow-keyframes\n@keyframes spinner-grow {\n  0% {\n    transform: scale(0);\n  }\n  50% {\n    opacity: 1;\n    transform: none;\n  }\n}\n// scss-docs-end spinner-grow-keyframes\n\n.spinner-grow {\n  // scss-docs-start spinner-grow-css-vars\n  --#{$prefix}spinner-width: #{$spinner-width};\n  --#{$prefix}spinner-height: #{$spinner-height};\n  --#{$prefix}spinner-vertical-align: #{$spinner-vertical-align};\n  --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed};\n  --#{$prefix}spinner-animation-name: spinner-grow;\n  // scss-docs-end spinner-grow-css-vars\n\n  background-color: currentcolor;\n  opacity: 0;\n}\n\n.spinner-grow-sm {\n  --#{$prefix}spinner-width: #{$spinner-width-sm};\n  --#{$prefix}spinner-height: #{$spinner-height-sm};\n}\n\n@if $enable-reduced-motion {\n  @media (prefers-reduced-motion: reduce) {\n    .spinner-border,\n    .spinner-grow {\n      --#{$prefix}spinner-animation-speed: #{$spinner-animation-speed * 2};\n    }\n  }\n}\n","// stylelint-disable function-disallowed-list\n\n%offcanvas-css-vars {\n  // scss-docs-start offcanvas-css-vars\n  --#{$prefix}offcanvas-zindex: #{$zindex-offcanvas};\n  --#{$prefix}offcanvas-width: #{$offcanvas-horizontal-width};\n  --#{$prefix}offcanvas-height: #{$offcanvas-vertical-height};\n  --#{$prefix}offcanvas-padding-x: #{$offcanvas-padding-x};\n  --#{$prefix}offcanvas-padding-y: #{$offcanvas-padding-y};\n  --#{$prefix}offcanvas-color: #{$offcanvas-color};\n  --#{$prefix}offcanvas-bg: #{$offcanvas-bg-color};\n  --#{$prefix}offcanvas-border-width: #{$offcanvas-border-width};\n  --#{$prefix}offcanvas-border-color: #{$offcanvas-border-color};\n  --#{$prefix}offcanvas-box-shadow: #{$offcanvas-box-shadow};\n  --#{$prefix}offcanvas-transition: #{transform $offcanvas-transition-duration ease-in-out};\n  --#{$prefix}offcanvas-title-line-height: #{$offcanvas-title-line-height};\n  // scss-docs-end offcanvas-css-vars\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @extend %offcanvas-css-vars;\n  }\n}\n\n@each $breakpoint in map-keys($grid-breakpoints) {\n  $next: breakpoint-next($breakpoint, $grid-breakpoints);\n  $infix: breakpoint-infix($next, $grid-breakpoints);\n\n  .offcanvas#{$infix} {\n    @include media-breakpoint-down($next) {\n      position: fixed;\n      bottom: 0;\n      z-index: var(--#{$prefix}offcanvas-zindex);\n      display: flex;\n      flex-direction: column;\n      max-width: 100%;\n      color: var(--#{$prefix}offcanvas-color);\n      visibility: hidden;\n      background-color: var(--#{$prefix}offcanvas-bg);\n      background-clip: padding-box;\n      outline: 0;\n      @include box-shadow(var(--#{$prefix}offcanvas-box-shadow));\n      @include transition(var(--#{$prefix}offcanvas-transition));\n\n      &.offcanvas-start {\n        top: 0;\n        left: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-right: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(-100%);\n      }\n\n      &.offcanvas-end {\n        top: 0;\n        right: 0;\n        width: var(--#{$prefix}offcanvas-width);\n        border-left: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateX(100%);\n      }\n\n      &.offcanvas-top {\n        top: 0;\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-bottom: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(-100%);\n      }\n\n      &.offcanvas-bottom {\n        right: 0;\n        left: 0;\n        height: var(--#{$prefix}offcanvas-height);\n        max-height: 100%;\n        border-top: var(--#{$prefix}offcanvas-border-width) solid var(--#{$prefix}offcanvas-border-color);\n        transform: translateY(100%);\n      }\n\n      &.showing,\n      &.show:not(.hiding) {\n        transform: none;\n      }\n\n      &.showing,\n      &.hiding,\n      &.show {\n        visibility: visible;\n      }\n    }\n\n    @if not ($infix == \"\") {\n      @include media-breakpoint-up($next) {\n        --#{$prefix}offcanvas-height: auto;\n        --#{$prefix}offcanvas-border-width: 0;\n        background-color: transparent !important; // stylelint-disable-line declaration-no-important\n\n        .offcanvas-header {\n          display: none;\n        }\n\n        .offcanvas-body {\n          display: flex;\n          flex-grow: 0;\n          padding: 0;\n          overflow-y: visible;\n          // Reset `background-color` in case `.bg-*` classes are used in offcanvas\n          background-color: transparent !important; // stylelint-disable-line declaration-no-important\n        }\n      }\n    }\n  }\n}\n\n.offcanvas-backdrop {\n  @include overlay-backdrop($zindex-offcanvas-backdrop, $offcanvas-backdrop-bg, $offcanvas-backdrop-opacity);\n}\n\n.offcanvas-header {\n  display: flex;\n  align-items: center;\n  justify-content: space-between;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n\n  .btn-close {\n    padding: calc(var(--#{$prefix}offcanvas-padding-y) * .5) calc(var(--#{$prefix}offcanvas-padding-x) * .5);\n    margin-top: calc(-.5 * var(--#{$prefix}offcanvas-padding-y));\n    margin-right: calc(-.5 * var(--#{$prefix}offcanvas-padding-x));\n    margin-bottom: calc(-.5 * var(--#{$prefix}offcanvas-padding-y));\n  }\n}\n\n.offcanvas-title {\n  margin-bottom: 0;\n  line-height: var(--#{$prefix}offcanvas-title-line-height);\n}\n\n.offcanvas-body {\n  flex-grow: 1;\n  padding: var(--#{$prefix}offcanvas-padding-y) var(--#{$prefix}offcanvas-padding-x);\n  overflow-y: auto;\n}\n",".placeholder {\n  display: inline-block;\n  min-height: 1em;\n  vertical-align: middle;\n  cursor: wait;\n  background-color: currentcolor;\n  opacity: $placeholder-opacity-max;\n\n  &.btn::before {\n    display: inline-block;\n    content: \"\";\n  }\n}\n\n// Sizing\n.placeholder-xs {\n  min-height: .6em;\n}\n\n.placeholder-sm {\n  min-height: .8em;\n}\n\n.placeholder-lg {\n  min-height: 1.2em;\n}\n\n// Animation\n.placeholder-glow {\n  .placeholder {\n    animation: placeholder-glow 2s ease-in-out infinite;\n  }\n}\n\n@keyframes placeholder-glow {\n  50% {\n    opacity: $placeholder-opacity-min;\n  }\n}\n\n.placeholder-wave {\n  mask-image: linear-gradient(130deg, $black 55%, rgba(0, 0, 0, (1 - $placeholder-opacity-min)) 75%, $black 95%);\n  mask-size: 200% 100%;\n  animation: placeholder-wave 2s linear infinite;\n}\n\n@keyframes placeholder-wave {\n  100% {\n    mask-position: -200% 0%;\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .text-bg-#{$color} {\n    color: color-contrast($value) if($enable-important-utilities, !important, null);\n    background-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}bg-opacity, 1)) if($enable-important-utilities, !important, null);\n  }\n}\n","// All-caps `RGBA()` function used because of this Sass bug: https://github.com/sass/node-sass/issues/2251\n@each $color, $value in $theme-colors {\n  .link-#{$color} {\n    color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n    text-decoration-color: RGBA(var(--#{$prefix}#{$color}-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n    @if $link-shade-percentage != 0 {\n      &:hover,\n      &:focus {\n        $hover-color: if(color-contrast($value) == $color-contrast-light, shade-color($value, $link-shade-percentage), tint-color($value, $link-shade-percentage));\n        color: RGBA(#{to-rgb($hover-color)}, var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n        text-decoration-color: RGBA(to-rgb($hover-color), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n      }\n    }\n  }\n}\n\n// One-off special link helper as a bridge until v6\n.link-body-emphasis {\n  color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, 1)) if($enable-important-utilities, !important, null);\n  text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, 1)) if($enable-important-utilities, !important, null);\n\n  @if $link-shade-percentage != 0 {\n    &:hover,\n    &:focus {\n      color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-opacity, .75)) if($enable-important-utilities, !important, null);\n      text-decoration-color: RGBA(var(--#{$prefix}emphasis-color-rgb), var(--#{$prefix}link-underline-opacity, .75)) if($enable-important-utilities, !important, null);\n    }\n  }\n}\n",".focus-ring:focus {\n  outline: 0;\n  // By default, there is no `--bs-focus-ring-x`, `--bs-focus-ring-y`, or `--bs-focus-ring-blur`, but we provide CSS variables with fallbacks to initial `0` values\n  box-shadow: var(--#{$prefix}focus-ring-x, 0) var(--#{$prefix}focus-ring-y, 0) var(--#{$prefix}focus-ring-blur, 0) var(--#{$prefix}focus-ring-width) var(--#{$prefix}focus-ring-color);\n}\n",".icon-link {\n  display: inline-flex;\n  gap: $icon-link-gap;\n  align-items: center;\n  text-decoration-color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, .5));\n  text-underline-offset: $icon-link-underline-offset;\n  backface-visibility: hidden;\n\n  > .bi {\n    flex-shrink: 0;\n    width: $icon-link-icon-size;\n    height: $icon-link-icon-size;\n    fill: currentcolor;\n    @include transition($icon-link-icon-transition);\n  }\n}\n\n.icon-link-hover {\n  &:hover,\n  &:focus-visible {\n    > .bi {\n      transform: var(--#{$prefix}icon-link-transform, $icon-link-icon-transform);\n    }\n  }\n}\n","// Credit: Nicolas Gallagher and SUIT CSS.\n\n.ratio {\n  position: relative;\n  width: 100%;\n\n  &::before {\n    display: block;\n    padding-top: var(--#{$prefix}aspect-ratio);\n    content: \"\";\n  }\n\n  > * {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n  }\n}\n\n@each $key, $ratio in $aspect-ratios {\n  .ratio-#{$key} {\n    --#{$prefix}aspect-ratio: #{$ratio};\n  }\n}\n","// Shorthand\n\n.fixed-top {\n  position: fixed;\n  top: 0;\n  right: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n.fixed-bottom {\n  position: fixed;\n  right: 0;\n  bottom: 0;\n  left: 0;\n  z-index: $zindex-fixed;\n}\n\n// Responsive sticky top and bottom\n@each $breakpoint in map-keys($grid-breakpoints) {\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    .sticky#{$infix}-top {\n      position: sticky;\n      top: 0;\n      z-index: $zindex-sticky;\n    }\n\n    .sticky#{$infix}-bottom {\n      position: sticky;\n      bottom: 0;\n      z-index: $zindex-sticky;\n    }\n  }\n}\n","// scss-docs-start stacks\n.hstack {\n  display: flex;\n  flex-direction: row;\n  align-items: center;\n  align-self: stretch;\n}\n\n.vstack {\n  display: flex;\n  flex: 1 1 auto;\n  flex-direction: column;\n  align-self: stretch;\n}\n// scss-docs-end stacks\n","//\n// Visually hidden\n//\n\n.visually-hidden,\n.visually-hidden-focusable:not(:focus):not(:focus-within) {\n  @include visually-hidden();\n}\n","// stylelint-disable declaration-no-important\n\n// Hide content visually while keeping it accessible to assistive technologies\n//\n// See: https://www.a11yproject.com/posts/2013-01-11-how-to-hide-content/\n// See: https://kittygiraudel.com/2016/10/13/css-hide-and-seek/\n\n@mixin visually-hidden() {\n  width: 1px !important;\n  height: 1px !important;\n  padding: 0 !important;\n  margin: -1px !important; // Fix for https://github.com/twbs/bootstrap/issues/25686\n  overflow: hidden !important;\n  clip: rect(0, 0, 0, 0) !important;\n  white-space: nowrap !important;\n  border: 0 !important;\n\n  // Fix for positioned table caption that could become anonymous cells\n  &:not(caption) {\n    position: absolute !important;\n  }\n}\n\n// Use to only display content when it's focused, or one of its child elements is focused\n// (i.e. when focus is within the element/container that the class was applied to)\n//\n// Useful for \"Skip to main content\" links; see https://www.w3.org/TR/2013/NOTE-WCAG20-TECHS-20130905/G1\n\n@mixin visually-hidden-focusable() {\n  &:not(:focus):not(:focus-within) {\n    @include visually-hidden();\n  }\n}\n","//\n// Stretched link\n//\n\n.stretched-link {\n  &::#{$stretched-link-pseudo-element} {\n    position: absolute;\n    top: 0;\n    right: 0;\n    bottom: 0;\n    left: 0;\n    z-index: $stretched-link-z-index;\n    content: \"\";\n  }\n}\n","//\n// Text truncation\n//\n\n.text-truncate {\n  @include text-truncate();\n}\n","// Text truncate\n// Requires inline-block or block for proper styling\n\n@mixin text-truncate() {\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n",".vr {\n  display: inline-block;\n  align-self: stretch;\n  width: $vr-border-width;\n  min-height: 1em;\n  background-color: currentcolor;\n  opacity: $hr-opacity;\n}\n","// Utility generator\n// Used to generate utilities & print utilities\n@mixin generate-utility($utility, $infix: \"\", $is-rfs-media-query: false) {\n  $values: map-get($utility, values);\n\n  // If the values are a list or string, convert it into a map\n  @if type-of($values) == \"string\" or type-of(nth($values, 1)) != \"list\" {\n    $values: zip($values, $values);\n  }\n\n  @each $key, $value in $values {\n    $properties: map-get($utility, property);\n\n    // Multiple properties are possible, for example with vertical or horizontal margins or paddings\n    @if type-of($properties) == \"string\" {\n      $properties: append((), $properties);\n    }\n\n    // Use custom class if present\n    $property-class: if(map-has-key($utility, class), map-get($utility, class), nth($properties, 1));\n    $property-class: if($property-class == null, \"\", $property-class);\n\n    // Use custom CSS variable name if present, otherwise default to `class`\n    $css-variable-name: if(map-has-key($utility, css-variable-name), map-get($utility, css-variable-name), map-get($utility, class));\n\n    // State params to generate pseudo-classes\n    $state: if(map-has-key($utility, state), map-get($utility, state), ());\n\n    $infix: if($property-class == \"\" and str-slice($infix, 1, 1) == \"-\", str-slice($infix, 2), $infix);\n\n    // Don't prefix if value key is null (e.g. with shadow class)\n    $property-class-modifier: if($key, if($property-class == \"\" and $infix == \"\", \"\", \"-\") + $key, \"\");\n\n    @if map-get($utility, rfs) {\n      // Inside the media query\n      @if $is-rfs-media-query {\n        $val: rfs-value($value);\n\n        // Do not render anything if fluid and non fluid values are the same\n        $value: if($val == rfs-fluid-value($value), null, $val);\n      }\n      @else {\n        $value: rfs-fluid-value($value);\n      }\n    }\n\n    $is-css-var: map-get($utility, css-var);\n    $is-local-vars: map-get($utility, local-vars);\n    $is-rtl: map-get($utility, rtl);\n\n    @if $value != null {\n      @if $is-rtl == false {\n        /* rtl:begin:remove */\n      }\n\n      @if $is-css-var {\n        .#{$property-class + $infix + $property-class-modifier} {\n          --#{$prefix}#{$css-variable-name}: #{$value};\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            --#{$prefix}#{$css-variable-name}: #{$value};\n          }\n        }\n      } @else {\n        .#{$property-class + $infix + $property-class-modifier} {\n          @each $property in $properties {\n            @if $is-local-vars {\n              @each $local-var, $variable in $is-local-vars {\n                --#{$prefix}#{$local-var}: #{$variable};\n              }\n            }\n            #{$property}: $value if($enable-important-utilities, !important, null);\n          }\n        }\n\n        @each $pseudo in $state {\n          .#{$property-class + $infix + $property-class-modifier}-#{$pseudo}:#{$pseudo} {\n            @each $property in $properties {\n              @if $is-local-vars {\n                @each $local-var, $variable in $is-local-vars {\n                  --#{$prefix}#{$local-var}: #{$variable};\n                }\n              }\n              #{$property}: $value if($enable-important-utilities, !important, null);\n            }\n          }\n        }\n      }\n\n      @if $is-rtl == false {\n        /* rtl:end:remove */\n      }\n    }\n  }\n}\n","// Loop over each breakpoint\n@each $breakpoint in map-keys($grid-breakpoints) {\n\n  // Generate media query if needed\n  @include media-breakpoint-up($breakpoint) {\n    $infix: breakpoint-infix($breakpoint, $grid-breakpoints);\n\n    // Loop over each utility property\n    @each $key, $utility in $utilities {\n      // The utility can be disabled with `false`, thus check if the utility is a map first\n      // Only proceed if responsive media queries are enabled or if it's the base media query\n      @if type-of($utility) == \"map\" and (map-get($utility, responsive) or $infix == \"\") {\n        @include 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\ No newline at end of file
diff --git a/stable/_static/styles/pydata-sphinx-theme.css b/stable/_static/styles/pydata-sphinx-theme.css
index fe09ca68..db7635b1 100644
--- a/stable/_static/styles/pydata-sphinx-theme.css
+++ b/stable/_static/styles/pydata-sphinx-theme.css
@@ -1,6 +1,2 @@
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\ No newline at end of file
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var(--sd-color-white);--pst-sd-dropdown-bg-color: var(--sd-color-white-bg)}details.sd-dropdown summary.sd-card-header.sd-bg-text-white{color:var(--sd-color-white-bg-text) !important}details.sd-dropdown summary.sd-card-header:before{content:\"\";width:100%;height:100%;position:absolute;left:0;top:0;background-color:var(--pst-sd-dropdown-bg-color);z-index:-1;pointer-events:none}details.sd-dropdown summary.sd-card-header+div.sd-summary-content{border-left:.2rem solid var(--pst-sd-dropdown-color) !important;border-bottom-left-radius:calc(.25rem - 1px);background-color:var(--pst-color-on-background)}details.sd-dropdown summary.sd-card-header span.sd-summary-icon{display:inline-flex;align-items:center;color:var(--pst-sd-dropdown-color) !important}details.sd-dropdown summary.sd-card-header span.sd-summary-icon svg{opacity:1}details.sd-dropdown summary.sd-card-header .sd-summary-up,details.sd-dropdown summary.sd-card-header .sd-summary-down{top:.7rem}.bd-content .admonition button.toggle-button{color:inherit}.bd-content details.toggle-details summary{border-left:3px solid var(--pst-color-primary)}html div.rendered_html html .jp-RenderedHTMLCommon table{table-layout:auto}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}html[data-theme=dark] .bd-content div.cell_output img,html[data-theme=dark] .bd-content div.cell_output .text_html{color:var(--pst-color-on-background);background-color:var(--pst-color-text-base);border-radius:.25rem;padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:0}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{margin:0;list-style:none}.bd-search-container ul.search li{background-image:none;padding:1rem 0;margin:1rem 0;border-top:1px solid var(--pst-color-text-muted)}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0 0}.bd-search-container ul.search li div.context a:before,.bd-search-container ul.search li p.context a:before{content:\"#\";padding-right:.2em;color:var(--pst-color-text-muted)}","html {\n  /*****************************************************************************\n  * Overall Layout Variables\n  */\n\n  // Header height will impact the top offset for many sections\n  // Article header is 66% of Header\n  --pst-header-height: 4rem;\n  --pst-header-article-height: calc(var(--pst-header-height) * 2 / 3);\n  --pst-sidebar-secondary: 17rem;\n}\n\n/*******************************************************************************\n* Breakpoints that trigger UI changes\n*\n* Note that media-breakpoint-down begins at the next highest level!\n* So we should choose a media-breakpoint-down one *lower* than when we want to start\n* example: media-breakpoint-up(md) and media-breakpoint-down(sm) trigger at the same time\n* ref: https://github.com/twbs/bootstrap/issues/31214\n*/\n$breakpoint-sidebar-primary: lg; // When we collapse the primary sidebar\n$breakpoint-sidebar-secondary: xl; // When we collapse the secondary sidebar\n$breakpoint-page-width: 88rem; // taken from sphinx-basic-ng, which we are ultimately going to inherit\n\n/*******************************************************************************\n* Define the animation behaviour\n*/\n$animation-time: 200ms;\n\n/*******************************************************************************\n* UI shaping and padding\n*/\n$admonition-border-radius: 0.25rem;\n","html {\n  /*****************************************************************************\n  * Font features used in this theme\n  */\n\n  // base font size - applied at body/html level\n  --pst-font-size-base: 1rem;\n\n  // heading font sizes based on bootstrap sizing\n  --pst-font-size-h1: 2.5rem;\n  --pst-font-size-h2: 2rem;\n  --pst-font-size-h3: 1.75rem;\n  --pst-font-size-h4: 1.5rem;\n  --pst-font-size-h5: 1.25rem;\n  --pst-font-size-h6: 1.1rem;\n\n  // smaller than heading font sizes\n  --pst-font-size-milli: 0.9rem;\n\n  // Sidebar styles\n  --pst-sidebar-font-size: 0.9rem;\n  --pst-sidebar-font-size-mobile: 1.1rem;\n  --pst-sidebar-header-font-size: 1.2rem;\n  --pst-sidebar-header-font-weight: 600;\n\n  // Admonition styles\n  --pst-admonition-font-weight-heading: 600;\n\n  // Font weights\n  --pst-font-weight-caption: 300;\n  --pst-font-weight-heading: 400;\n\n  // Font family\n  // These are adapted from https://systemfontstack.com/ */\n  --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI,\n    \"Helvetica Neue\", Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji,\n    Segoe UI Symbol;\n  --pst-font-family-monospace-system: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n\n  --pst-font-family-base: var(--pst-font-family-base-system);\n  --pst-font-family-heading: var(--pst-font-family-base-system);\n  --pst-font-family-monospace: var(--pst-font-family-monospace-system);\n}\n","html {\n  /*****************************************************************************\n  * Icon\n  */\n\n  // Font size across all icons\n  --pst-font-size-icon: 1.5rem;\n\n  // Font Awesome default icons\n  --pst-icon-check-circle: \"\\f058\"; // fa-solid fa-circle-check\n  --pst-icon-info-circle: \"\\f05a\"; // fa-solid fa-circle-info\n  --pst-icon-exclamation-triangle: \"\\f071\"; // fa-solid fa-triangle-exclamation\n  --pst-icon-exclamation-circle: \"\\f06a\"; // fa-solid fa-circle-exclamation\n  --pst-icon-times-circle: \"\\f057\"; // fa-solid fa-circle-xmark\n  --pst-icon-lightbulb: \"\\f0eb\"; // fa-solid fa-lightbulb\n  --pst-icon-download: \"\\f019\"; // fa-solid fa-download\n  --pst-icon-angle-left: \"\\f104\"; // fa-solid fa-angle-left\n  --pst-icon-angle-right: \"\\f105\"; // fa-solid fa-angle-right\n  --pst-icon-external-link: \"\\f35d\"; // fa-solid fa-up-right-from-square\n  --pst-icon-search-minus: \"\\f010\"; // fa-solid fa-magnifying-glass-minus\n  --pst-icon-github: \"\\f09b\"; // fa-brands fa-github\n  --pst-icon-gitlab: \"\\f296\"; // fa-brands fa-gitlab\n  --pst-icon-share: \"\\f064\"; // fa-solid fa-share\n  --pst-icon-bell: \"\\f0f3\"; // fa-solid fa-bell\n  --pst-icon-pencil: \"\\f303\"; // fa-solid fa-pencil\n\n  // Bootstrap icons\n  --pst-breadcrumb-divider: \"\\f105\";\n}\n","html {\n  /*****************************************************************************\n  * Admonitions\n  **/\n\n  --pst-icon-admonition-default: var(--pst-icon-bell);\n  --pst-icon-admonition-note: var(--pst-icon-info-circle);\n  --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);\n  --pst-icon-admonition-error: var(--pst-icon-times-circle);\n  --pst-icon-admonition-hint: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-tip: var(--pst-icon-lightbulb);\n  --pst-icon-admonition-important: var(--pst-icon-exclamation-circle);\n  --pst-icon-admonition-seealso: var(--pst-icon-share);\n  --pst-icon-admonition-todo: var(--pst-icon-pencil);\n}\n","html {\n  /*****************************************************************************\n  * versionmodified\n  **/\n\n  --pst-icon-versionmodified-default: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-added: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-changed: var(--pst-icon-exclamation-circle);\n  --pst-icon-versionmodified-deprecated: var(--pst-icon-exclamation-circle);\n}\n","html {\n  font-size: var(--pst-font-size-base);\n  scroll-padding-top: calc(var(--pst-header-height) + 1rem);\n}\n\nbody {\n  background-color: var(--pst-color-background);\n  font-family: var(--pst-font-family-base);\n  font-weight: 400;\n  line-height: 1.65;\n  color: var(--pst-color-text-base);\n  min-height: 100vh;\n  display: flex;\n  flex-direction: column;\n\n  // hack to avoid the black background on some browser including Safari\n  &::-webkit-scrollbar-track {\n    background: var(--pst-color-background);\n  }\n}\n\np {\n  margin-bottom: 1.15rem;\n  font-size: 1em;\n  color: var(--pst-color-text-base);\n\n  /* section header in docstring pages */\n  &.rubric {\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n\n  &.centered {\n    text-align: center;\n  }\n}\n\na {\n  @include link-style-default;\n\n  // The # anchor that appears on hover over headings\n  &.headerlink {\n    color: var(--pst-color-secondary);\n    opacity: 0.7;\n    font-size: 0.8em;\n    padding: 0 4px 0 4px;\n    margin-left: 0.2em;\n    text-decoration: none;\n    transition: all 0.2s ease-out;\n    user-select: none;\n\n    &:hover {\n      opacity: 1;\n    }\n  }\n\n  // set up a icon next to the shorten links from github and gitlab\n  &.github,\n  &.gitlab {\n    &::before {\n      color: var(--pst-color-text-muted);\n      font: var(--fa-font-brands);\n      margin-right: 0.25rem;\n    }\n  }\n\n  &.github::before {\n    content: var(--pst-icon-github);\n  }\n\n  &.gitlab::before {\n    content: var(--pst-icon-gitlab);\n  }\n}\n\n.heading-style {\n  margin: 2.75rem 0 1.05rem;\n  font-family: var(--pst-font-family-heading);\n  font-weight: var(--pst-font-weight-heading);\n  line-height: 1.15;\n}\n\nh1 {\n  @extend .heading-style;\n  margin-top: 0;\n  font-size: var(--pst-font-size-h1);\n  color: var(--pst-heading-color);\n}\n\nh2 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h2);\n  color: var(--pst-heading-color);\n}\n\nh3 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h3);\n  color: var(--pst-heading-color);\n}\n\nh4 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h4);\n  color: var(--pst-heading-color);\n}\n\nh5 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h5);\n  color: var(--pst-color-text-base);\n}\n\nh6 {\n  @extend .heading-style;\n  font-size: var(--pst-font-size-h6);\n  color: var(--pst-color-text-base);\n}\n\nsmall,\n.text_small {\n  font-size: var(--pst-font-size-milli);\n}\n\nhr {\n  border: 0;\n  border-top: 1px solid var(--pst-color-border);\n}\n\npre,\ncode,\nkbd,\nsamp {\n  font-family: var(--pst-font-family-monospace);\n}\n\nkbd {\n  // use theme negative\n  background-color: var(--pst-color-on-background);\n  color: var(--pst-color-text-muted);\n\n  // Compound keyboard elements will have nested kbd in them, so this prevents double lines\n  &:not(.compound) {\n    border: 1px solid var(--pst-color-border);\n    margin: 0 0.1rem;\n    padding: 0.1rem 0.4rem;\n    box-shadow: 1px 1px 1px var(--pst-color-shadow);\n  }\n}\n\ncode {\n  color: var(--pst-color-inline-code);\n}\n\npre {\n  margin: 1.5em 0 1.5em 0;\n  padding: 1rem;\n  background-color: var(--pst-color-surface);\n  color: var(--pst-color-text-base);\n  line-height: 1.2em;\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  .linenos {\n    // minimum opacity to make the line numbers WCAG AA conformant\n    opacity: 0.8;\n    padding-right: 10px;\n  }\n}\n\n// the back to top btn\n#pst-back-to-top {\n  z-index: $zindex-tooltip;\n  position: fixed;\n  display: none;\n  top: 80vh;\n  left: 50vw;\n  transform: translate(-50%);\n  color: var(--pst-color-secondary-text);\n  background-color: var(--pst-color-secondary);\n  border: none;\n}\n","/**\n * Consistent styling for links\n **/\n\n// Define some useful variables for links styling consistency\n//\n//  Thickness of the underline for links\n// the default will be either:\n//  - 1px\n// - 0.0625rem if it's thicker than 1px because the user has changed the text\n//   size in their browser\n$link-underline-thickness: unquote(\"max(1px, .0625rem)\") !default;\n// Offset of link underlines from text baseline\n// The default is 3px expressed as ems, as calculated against the default body\n// font size (on desktop).\n$link-underline-offset: 0.1578em !default;\n// Thickness of link underlines in hover state\n// The default for each link will be the thickest of the following:\n//  - 3px\n//  - 0.1875rem, if it's thicker than 3px because the user has changed the text\n//    size in their browser\n//  - 0.12em (relative to the link's text size)\n$link-hover-decoration-thickness: unquote(\"max(3px, .1875rem, .12em)\") !default;\n\n//  Ensures links have an underline decoration by default - needed to meet\n// WCAG SC 1.4.1\n@mixin link-decoration {\n  text-decoration: underline;\n\n  @if $link-underline-thickness {\n    text-decoration-thickness: $link-underline-thickness;\n  }\n\n  @if $link-underline-offset {\n    text-underline-offset: $link-underline-offset;\n  }\n}\n\n// Ensures links have an underline decoration on hover - distinct from the\n// default behaviour\n@mixin link-decoration-hover {\n  @if $link-hover-decoration-thickness {\n    text-decoration-thickness: $link-hover-decoration-thickness;\n    // Disable ink skipping on underlines on hover. Browsers haven't\n    // standardised on this part of the spec yet, so set both properties\n    text-decoration-skip-ink: none; // Chromium, Firefox\n    text-decoration-skip: none; // Safari\n  }\n}\n\n// Simple hover style - can be used alone or in conjunction with other mixins\n// Add the text underline and change in thickness on hover\n@mixin link-style-hover {\n  &:hover {\n    @include link-decoration;\n    @include link-decoration-hover;\n    color: var(--pst-color-link-hover);\n  }\n}\n\n// Default link styles\n//\n// Defines: default unvisited, visited, hover, and active.\n// TODO: @trallard to improve focus styles in subsequent PR\n@mixin link-style-default {\n  // So that really long links don't spill out of their container\n  word-wrap: break-word;\n\n  color: var(--pst-color-link);\n  @include link-decoration;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to add active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link);\n  }\n\n  // Visited should still be hoverable\n  &:visited {\n    color: var(--pst-color-link);\n    &:hover {\n      color: var(--pst-color-link-hover);\n    }\n  }\n  @include focus-indicator;\n}\n\n// Text link styles\n//\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation bar links.\n@mixin link-style-text {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @include link-decoration;\n    @include link-decoration-hover;\n  }\n  @include focus-indicator;\n}\n\n// Sidebar and TOC links\n//\n// Makes links use the muted text colour and removes the underline.\n// Use this mixin for navigation the primary sidebar and table of contents.\n// Active and hover should work together rather than one overriding the other.\n@mixin link-sidebar {\n  color: var(--pst-color-text-muted);\n  text-decoration: none;\n\n  &:hover {\n    text-decoration: underline;\n    background-color: transparent;\n    color: var(--pst-color-link-hover);\n    @include link-decoration-hover;\n  }\n\n  // TODO: @trallard to update active styles in subsequent PR\n  &:active {\n    color: var(--pst-color-link-hover);\n  }\n}\n\n// Sidebar current page link styles\n//\n// Adds a vertical line on the left hand side of the link to indicate that\n// it's the current page. Note this is distinct from an active state.\n// Used on the primary sidebar and the TOC.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-sidebar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  @if $link-hover-decoration-thickness {\n    box-shadow: inset\n      $link-hover-decoration-thickness\n      0px\n      0px\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar current page link styles\n//\n// Adds a bottom underline, this leaves enough space for the hover state without\n// cluttering the navbar.\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin link-navbar-current {\n  font-weight: 600;\n  color: var(--pst-color-primary);\n  @if $link-hover-decoration-thickness {\n    border-bottom: $link-hover-decoration-thickness\n      solid\n      var(--pst-color-primary);\n  }\n}\n\n// Navigation bar icon links hover styles\n//\n// Adds a bottom box-shadow - since there is no text we cannot use text-decoration\n// We want the side box shadow to have the same thickness as the hover underline\n@mixin icon-navbar-hover {\n  &:hover {\n    color: var(--pst-color-link-hover);\n    @if $link-hover-decoration-thickness {\n      box-shadow: 0px\n        $link-hover-decoration-thickness\n        0px\n        var(--pst-color-link-hover);\n    }\n  }\n}\n\n// Focus indicator\n@mixin focus-indicator {\n  &:focus-visible {\n    outline: 2px solid var(--pst-color-accent);\n  }\n}\n","// Variables\n//\n// Variables should follow the `$component-state-property-size` formula for\n// consistent naming. Ex: $nav-link-disabled-color and $modal-content-box-shadow-xs.\n\n// Color system\n\n// scss-docs-start gray-color-variables\n$white:    #fff !default;\n$gray-100: #f8f9fa !default;\n$gray-200: #e9ecef !default;\n$gray-300: #dee2e6 !default;\n$gray-400: #ced4da !default;\n$gray-500: #adb5bd !default;\n$gray-600: #6c757d !default;\n$gray-700: #495057 !default;\n$gray-800: #343a40 !default;\n$gray-900: #212529 !default;\n$black:    #000 !default;\n// scss-docs-end gray-color-variables\n\n// fusv-disable\n// scss-docs-start gray-colors-map\n$grays: (\n  \"100\": $gray-100,\n  \"200\": $gray-200,\n  \"300\": $gray-300,\n  \"400\": $gray-400,\n  \"500\": $gray-500,\n  \"600\": $gray-600,\n  \"700\": $gray-700,\n  \"800\": $gray-800,\n  \"900\": $gray-900\n) !default;\n// scss-docs-end gray-colors-map\n// fusv-enable\n\n// scss-docs-start color-variables\n$blue:    #0d6efd !default;\n$indigo:  #6610f2 !default;\n$purple:  #6f42c1 !default;\n$pink:    #d63384 !default;\n$red:     #dc3545 !default;\n$orange:  #fd7e14 !default;\n$yellow:  #ffc107 !default;\n$green:   #198754 !default;\n$teal:    #20c997 !default;\n$cyan:    #0dcaf0 !default;\n// scss-docs-end color-variables\n\n// scss-docs-start colors-map\n$colors: (\n  \"blue\":       $blue,\n  \"indigo\":     $indigo,\n  \"purple\":     $purple,\n  \"pink\":       $pink,\n  \"red\":        $red,\n  \"orange\":     $orange,\n  \"yellow\":     $yellow,\n  \"green\":      $green,\n  \"teal\":       $teal,\n  \"cyan\":       $cyan,\n  \"black\":      $black,\n  \"white\":      $white,\n  \"gray\":       $gray-600,\n  \"gray-dark\":  $gray-800\n) !default;\n// scss-docs-end colors-map\n\n// The contrast ratio to reach against white, to determine if color changes from \"light\" to \"dark\". Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n$min-contrast-ratio:   4.5 !default;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$color-contrast-dark:      $black !default;\n$color-contrast-light:     $white !default;\n\n// fusv-disable\n$blue-100: tint-color($blue, 80%) !default;\n$blue-200: tint-color($blue, 60%) !default;\n$blue-300: tint-color($blue, 40%) !default;\n$blue-400: tint-color($blue, 20%) !default;\n$blue-500: $blue !default;\n$blue-600: shade-color($blue, 20%) !default;\n$blue-700: shade-color($blue, 40%) !default;\n$blue-800: shade-color($blue, 60%) !default;\n$blue-900: shade-color($blue, 80%) !default;\n\n$indigo-100: tint-color($indigo, 80%) !default;\n$indigo-200: tint-color($indigo, 60%) !default;\n$indigo-300: tint-color($indigo, 40%) !default;\n$indigo-400: tint-color($indigo, 20%) !default;\n$indigo-500: $indigo !default;\n$indigo-600: shade-color($indigo, 20%) !default;\n$indigo-700: shade-color($indigo, 40%) !default;\n$indigo-800: shade-color($indigo, 60%) !default;\n$indigo-900: shade-color($indigo, 80%) !default;\n\n$purple-100: tint-color($purple, 80%) !default;\n$purple-200: tint-color($purple, 60%) !default;\n$purple-300: tint-color($purple, 40%) !default;\n$purple-400: tint-color($purple, 20%) !default;\n$purple-500: $purple !default;\n$purple-600: shade-color($purple, 20%) !default;\n$purple-700: shade-color($purple, 40%) !default;\n$purple-800: shade-color($purple, 60%) !default;\n$purple-900: shade-color($purple, 80%) !default;\n\n$pink-100: tint-color($pink, 80%) !default;\n$pink-200: tint-color($pink, 60%) !default;\n$pink-300: tint-color($pink, 40%) !default;\n$pink-400: tint-color($pink, 20%) !default;\n$pink-500: $pink !default;\n$pink-600: shade-color($pink, 20%) !default;\n$pink-700: shade-color($pink, 40%) !default;\n$pink-800: shade-color($pink, 60%) !default;\n$pink-900: shade-color($pink, 80%) !default;\n\n$red-100: tint-color($red, 80%) !default;\n$red-200: tint-color($red, 60%) !default;\n$red-300: tint-color($red, 40%) !default;\n$red-400: tint-color($red, 20%) !default;\n$red-500: $red !default;\n$red-600: shade-color($red, 20%) !default;\n$red-700: shade-color($red, 40%) !default;\n$red-800: shade-color($red, 60%) !default;\n$red-900: shade-color($red, 80%) !default;\n\n$orange-100: tint-color($orange, 80%) !default;\n$orange-200: tint-color($orange, 60%) !default;\n$orange-300: tint-color($orange, 40%) !default;\n$orange-400: tint-color($orange, 20%) !default;\n$orange-500: $orange !default;\n$orange-600: shade-color($orange, 20%) !default;\n$orange-700: shade-color($orange, 40%) !default;\n$orange-800: shade-color($orange, 60%) !default;\n$orange-900: shade-color($orange, 80%) !default;\n\n$yellow-100: tint-color($yellow, 80%) !default;\n$yellow-200: tint-color($yellow, 60%) !default;\n$yellow-300: tint-color($yellow, 40%) !default;\n$yellow-400: tint-color($yellow, 20%) !default;\n$yellow-500: $yellow !default;\n$yellow-600: shade-color($yellow, 20%) !default;\n$yellow-700: shade-color($yellow, 40%) !default;\n$yellow-800: shade-color($yellow, 60%) !default;\n$yellow-900: shade-color($yellow, 80%) !default;\n\n$green-100: tint-color($green, 80%) !default;\n$green-200: tint-color($green, 60%) !default;\n$green-300: tint-color($green, 40%) !default;\n$green-400: tint-color($green, 20%) !default;\n$green-500: $green !default;\n$green-600: shade-color($green, 20%) !default;\n$green-700: shade-color($green, 40%) !default;\n$green-800: shade-color($green, 60%) !default;\n$green-900: shade-color($green, 80%) !default;\n\n$teal-100: tint-color($teal, 80%) !default;\n$teal-200: tint-color($teal, 60%) !default;\n$teal-300: tint-color($teal, 40%) !default;\n$teal-400: tint-color($teal, 20%) !default;\n$teal-500: $teal !default;\n$teal-600: shade-color($teal, 20%) !default;\n$teal-700: shade-color($teal, 40%) !default;\n$teal-800: shade-color($teal, 60%) !default;\n$teal-900: shade-color($teal, 80%) !default;\n\n$cyan-100: tint-color($cyan, 80%) !default;\n$cyan-200: tint-color($cyan, 60%) !default;\n$cyan-300: tint-color($cyan, 40%) !default;\n$cyan-400: tint-color($cyan, 20%) !default;\n$cyan-500: $cyan !default;\n$cyan-600: shade-color($cyan, 20%) !default;\n$cyan-700: shade-color($cyan, 40%) !default;\n$cyan-800: shade-color($cyan, 60%) !default;\n$cyan-900: shade-color($cyan, 80%) !default;\n\n$blues: (\n  \"blue-100\": $blue-100,\n  \"blue-200\": $blue-200,\n  \"blue-300\": $blue-300,\n  \"blue-400\": $blue-400,\n  \"blue-500\": $blue-500,\n  \"blue-600\": $blue-600,\n  \"blue-700\": $blue-700,\n  \"blue-800\": $blue-800,\n  \"blue-900\": $blue-900\n) !default;\n\n$indigos: (\n  \"indigo-100\": $indigo-100,\n  \"indigo-200\": $indigo-200,\n  \"indigo-300\": $indigo-300,\n  \"indigo-400\": $indigo-400,\n  \"indigo-500\": $indigo-500,\n  \"indigo-600\": $indigo-600,\n  \"indigo-700\": $indigo-700,\n  \"indigo-800\": $indigo-800,\n  \"indigo-900\": $indigo-900\n) !default;\n\n$purples: (\n  \"purple-100\": $purple-100,\n  \"purple-200\": $purple-200,\n  \"purple-300\": $purple-300,\n  \"purple-400\": $purple-400,\n  \"purple-500\": $purple-500,\n  \"purple-600\": $purple-600,\n  \"purple-700\": $purple-700,\n  \"purple-800\": $purple-800,\n  \"purple-900\": $purple-900\n) !default;\n\n$pinks: (\n  \"pink-100\": $pink-100,\n  \"pink-200\": $pink-200,\n  \"pink-300\": $pink-300,\n  \"pink-400\": $pink-400,\n  \"pink-500\": $pink-500,\n  \"pink-600\": $pink-600,\n  \"pink-700\": $pink-700,\n  \"pink-800\": $pink-800,\n  \"pink-900\": $pink-900\n) !default;\n\n$reds: (\n  \"red-100\": $red-100,\n  \"red-200\": $red-200,\n  \"red-300\": $red-300,\n  \"red-400\": $red-400,\n  \"red-500\": $red-500,\n  \"red-600\": $red-600,\n  \"red-700\": $red-700,\n  \"red-800\": $red-800,\n  \"red-900\": $red-900\n) !default;\n\n$oranges: (\n  \"orange-100\": $orange-100,\n  \"orange-200\": $orange-200,\n  \"orange-300\": $orange-300,\n  \"orange-400\": $orange-400,\n  \"orange-500\": $orange-500,\n  \"orange-600\": $orange-600,\n  \"orange-700\": $orange-700,\n  \"orange-800\": $orange-800,\n  \"orange-900\": $orange-900\n) !default;\n\n$yellows: (\n  \"yellow-100\": $yellow-100,\n  \"yellow-200\": $yellow-200,\n  \"yellow-300\": $yellow-300,\n  \"yellow-400\": $yellow-400,\n  \"yellow-500\": $yellow-500,\n  \"yellow-600\": $yellow-600,\n  \"yellow-700\": $yellow-700,\n  \"yellow-800\": $yellow-800,\n  \"yellow-900\": $yellow-900\n) !default;\n\n$greens: (\n  \"green-100\": $green-100,\n  \"green-200\": $green-200,\n  \"green-300\": $green-300,\n  \"green-400\": $green-400,\n  \"green-500\": $green-500,\n  \"green-600\": $green-600,\n  \"green-700\": $green-700,\n  \"green-800\": $green-800,\n  \"green-900\": $green-900\n) !default;\n\n$teals: (\n  \"teal-100\": $teal-100,\n  \"teal-200\": $teal-200,\n  \"teal-300\": $teal-300,\n  \"teal-400\": $teal-400,\n  \"teal-500\": $teal-500,\n  \"teal-600\": $teal-600,\n  \"teal-700\": $teal-700,\n  \"teal-800\": $teal-800,\n  \"teal-900\": $teal-900\n) !default;\n\n$cyans: (\n  \"cyan-100\": $cyan-100,\n  \"cyan-200\": $cyan-200,\n  \"cyan-300\": $cyan-300,\n  \"cyan-400\": $cyan-400,\n  \"cyan-500\": $cyan-500,\n  \"cyan-600\": $cyan-600,\n  \"cyan-700\": $cyan-700,\n  \"cyan-800\": $cyan-800,\n  \"cyan-900\": $cyan-900\n) !default;\n// fusv-enable\n\n// scss-docs-start theme-color-variables\n$primary:       $blue !default;\n$secondary:     $gray-600 !default;\n$success:       $green !default;\n$info:          $cyan !default;\n$warning:       $yellow !default;\n$danger:        $red !default;\n$light:         $gray-100 !default;\n$dark:          $gray-900 !default;\n// scss-docs-end theme-color-variables\n\n// scss-docs-start theme-colors-map\n$theme-colors: (\n  \"primary\":    $primary,\n  \"secondary\":  $secondary,\n  \"success\":    $success,\n  \"info\":       $info,\n  \"warning\":    $warning,\n  \"danger\":     $danger,\n  \"light\":      $light,\n  \"dark\":       $dark\n) !default;\n// scss-docs-end theme-colors-map\n\n// scss-docs-start theme-text-variables\n$primary-text-emphasis:   shade-color($primary, 60%) !default;\n$secondary-text-emphasis: shade-color($secondary, 60%) !default;\n$success-text-emphasis:   shade-color($success, 60%) !default;\n$info-text-emphasis:      shade-color($info, 60%) !default;\n$warning-text-emphasis:   shade-color($warning, 60%) !default;\n$danger-text-emphasis:    shade-color($danger, 60%) !default;\n$light-text-emphasis:     $gray-700 !default;\n$dark-text-emphasis:      $gray-700 !default;\n// scss-docs-end theme-text-variables\n\n// scss-docs-start theme-bg-subtle-variables\n$primary-bg-subtle:       tint-color($primary, 80%) !default;\n$secondary-bg-subtle:     tint-color($secondary, 80%) !default;\n$success-bg-subtle:       tint-color($success, 80%) !default;\n$info-bg-subtle:          tint-color($info, 80%) !default;\n$warning-bg-subtle:       tint-color($warning, 80%) !default;\n$danger-bg-subtle:        tint-color($danger, 80%) !default;\n$light-bg-subtle:         mix($gray-100, $white) !default;\n$dark-bg-subtle:          $gray-400 !default;\n// scss-docs-end theme-bg-subtle-variables\n\n// scss-docs-start theme-border-subtle-variables\n$primary-border-subtle:   tint-color($primary, 60%) !default;\n$secondary-border-subtle: tint-color($secondary, 60%) !default;\n$success-border-subtle:   tint-color($success, 60%) !default;\n$info-border-subtle:      tint-color($info, 60%) !default;\n$warning-border-subtle:   tint-color($warning, 60%) !default;\n$danger-border-subtle:    tint-color($danger, 60%) !default;\n$light-border-subtle:     $gray-200 !default;\n$dark-border-subtle:      $gray-500 !default;\n// scss-docs-end theme-border-subtle-variables\n\n// Characters which are escaped by the escape-svg function\n$escaped-characters: (\n  (\"<\", \"%3c\"),\n  (\">\", \"%3e\"),\n  (\"#\", \"%23\"),\n  (\"(\", \"%28\"),\n  (\")\", \"%29\"),\n) !default;\n\n// Options\n//\n// Quickly modify global styling by enabling or disabling optional features.\n\n$enable-caret:                true !default;\n$enable-rounded:              true !default;\n$enable-shadows:              false !default;\n$enable-gradients:            false !default;\n$enable-transitions:          true !default;\n$enable-reduced-motion:       true !default;\n$enable-smooth-scroll:        true !default;\n$enable-grid-classes:         true !default;\n$enable-container-classes:    true !default;\n$enable-cssgrid:              false !default;\n$enable-button-pointers:      true !default;\n$enable-rfs:                  true !default;\n$enable-validation-icons:     true !default;\n$enable-negative-margins:     false !default;\n$enable-deprecation-messages: true !default;\n$enable-important-utilities:  true !default;\n\n$enable-dark-mode:            true !default;\n$color-mode-type:             data !default; // `data` or `media-query`\n\n// Prefix for :root CSS variables\n\n$variable-prefix:             bs- !default; // Deprecated in v5.2.0 for the shorter `$prefix`\n$prefix:                      $variable-prefix !default;\n\n// Gradient\n//\n// The gradient which is added to components if `$enable-gradients` is `true`\n// This gradient is also added to elements with `.bg-gradient`\n// scss-docs-start variable-gradient\n$gradient: linear-gradient(180deg, rgba($white, .15), rgba($white, 0)) !default;\n// scss-docs-end variable-gradient\n\n// Spacing\n//\n// Control the default styling of most Bootstrap elements by modifying these\n// variables. Mostly focused on spacing.\n// You can add more entries to the $spacers map, should you need more variation.\n\n// scss-docs-start spacer-variables-maps\n$spacer: 1rem !default;\n$spacers: (\n  0: 0,\n  1: $spacer * .25,\n  2: $spacer * .5,\n  3: $spacer,\n  4: $spacer * 1.5,\n  5: $spacer * 3,\n) !default;\n// scss-docs-end spacer-variables-maps\n\n// Position\n//\n// Define the edge positioning anchors of the position utilities.\n\n// scss-docs-start position-map\n$position-values: (\n  0: 0,\n  50: 50%,\n  100: 100%\n) !default;\n// scss-docs-end position-map\n\n// Body\n//\n// Settings for the `<body>` element.\n\n$body-text-align:           null !default;\n$body-color:                $gray-900 !default;\n$body-bg:                   $white !default;\n\n$body-secondary-color:      rgba($body-color, .75) !default;\n$body-secondary-bg:         $gray-200 !default;\n\n$body-tertiary-color:       rgba($body-color, .5) !default;\n$body-tertiary-bg:          $gray-100 !default;\n\n$body-emphasis-color:       $black !default;\n\n// Links\n//\n// Style anchor elements.\n\n$link-color:                              $primary !default;\n$link-decoration:                         underline !default;\n$link-shade-percentage:                   20% !default;\n$link-hover-color:                        shift-color($link-color, $link-shade-percentage) !default;\n$link-hover-decoration:                   null !default;\n\n$stretched-link-pseudo-element:           after !default;\n$stretched-link-z-index:                  1 !default;\n\n// Icon links\n// scss-docs-start icon-link-variables\n$icon-link-gap:               .375rem !default;\n$icon-link-underline-offset:  .25em !default;\n$icon-link-icon-size:         1em !default;\n$icon-link-icon-transition:   .2s ease-in-out transform !default;\n$icon-link-icon-transform:    translate3d(.25em, 0, 0) !default;\n// scss-docs-end icon-link-variables\n\n// Paragraphs\n//\n// Style p element.\n\n$paragraph-margin-bottom:   1rem !default;\n\n\n// Grid breakpoints\n//\n// Define the minimum dimensions at which your layout will change,\n// adapting to different screen sizes, for use in media queries.\n\n// scss-docs-start grid-breakpoints\n$grid-breakpoints: (\n  xs: 0,\n  sm: 576px,\n  md: 768px,\n  lg: 992px,\n  xl: 1200px,\n  xxl: 1400px\n) !default;\n// scss-docs-end grid-breakpoints\n\n@include _assert-ascending($grid-breakpoints, \"$grid-breakpoints\");\n@include _assert-starts-at-zero($grid-breakpoints, \"$grid-breakpoints\");\n\n\n// Grid containers\n//\n// Define the maximum width of `.container` for different screen sizes.\n\n// scss-docs-start container-max-widths\n$container-max-widths: (\n  sm: 540px,\n  md: 720px,\n  lg: 960px,\n  xl: 1140px,\n  xxl: 1320px\n) !default;\n// scss-docs-end container-max-widths\n\n@include _assert-ascending($container-max-widths, \"$container-max-widths\");\n\n\n// Grid columns\n//\n// Set the number of columns and specify the width of the gutters.\n\n$grid-columns:                12 !default;\n$grid-gutter-width:           1.5rem !default;\n$grid-row-columns:            6 !default;\n\n// Container padding\n\n$container-padding-x: $grid-gutter-width !default;\n\n\n// Components\n//\n// Define common padding and border radius sizes and more.\n\n// scss-docs-start border-variables\n$border-width:                1px !default;\n$border-widths: (\n  1: 1px,\n  2: 2px,\n  3: 3px,\n  4: 4px,\n  5: 5px\n) !default;\n$border-style:                solid !default;\n$border-color:                $gray-300 !default;\n$border-color-translucent:    rgba($black, .175) !default;\n// scss-docs-end border-variables\n\n// scss-docs-start border-radius-variables\n$border-radius:               .375rem !default;\n$border-radius-sm:            .25rem !default;\n$border-radius-lg:            .5rem !default;\n$border-radius-xl:            1rem !default;\n$border-radius-xxl:           2rem !default;\n$border-radius-pill:          50rem !default;\n// scss-docs-end border-radius-variables\n// fusv-disable\n$border-radius-2xl:           $border-radius-xxl !default; // Deprecated in v5.3.0\n// fusv-enable\n\n// scss-docs-start box-shadow-variables\n$box-shadow:                  0 .5rem 1rem rgba($black, .15) !default;\n$box-shadow-sm:               0 .125rem .25rem rgba($black, .075) !default;\n$box-shadow-lg:               0 1rem 3rem rgba($black, .175) !default;\n$box-shadow-inset:            inset 0 1px 2px rgba($black, .075) !default;\n// scss-docs-end box-shadow-variables\n\n$component-active-color:      $white !default;\n$component-active-bg:         $primary !default;\n\n// scss-docs-start focus-ring-variables\n$focus-ring-width:      .25rem !default;\n$focus-ring-opacity:    .25 !default;\n$focus-ring-color:      rgba($primary, $focus-ring-opacity) !default;\n$focus-ring-blur:       0 !default;\n$focus-ring-box-shadow: 0 0 $focus-ring-blur $focus-ring-width $focus-ring-color !default;\n// scss-docs-end focus-ring-variables\n\n// scss-docs-start caret-variables\n$caret-width:                 .3em !default;\n$caret-vertical-align:        $caret-width * .85 !default;\n$caret-spacing:               $caret-width * .85 !default;\n// scss-docs-end caret-variables\n\n$transition-base:             all .2s ease-in-out !default;\n$transition-fade:             opacity .15s linear !default;\n// scss-docs-start collapse-transition\n$transition-collapse:         height .35s ease !default;\n$transition-collapse-width:   width .35s ease !default;\n// scss-docs-end collapse-transition\n\n// stylelint-disable function-disallowed-list\n// scss-docs-start aspect-ratios\n$aspect-ratios: (\n  \"1x1\": 100%,\n  \"4x3\": calc(3 / 4 * 100%),\n  \"16x9\": calc(9 / 16 * 100%),\n  \"21x9\": calc(9 / 21 * 100%)\n) !default;\n// scss-docs-end aspect-ratios\n// stylelint-enable function-disallowed-list\n\n// Typography\n//\n// Font, line-height, and color for body text, headings, and more.\n\n// scss-docs-start font-variables\n// stylelint-disable value-keyword-case\n$font-family-sans-serif:      system-ui, -apple-system, \"Segoe UI\", Roboto, \"Helvetica Neue\", \"Noto Sans\", \"Liberation Sans\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\", \"Noto Color Emoji\" !default;\n$font-family-monospace:       SFMono-Regular, Menlo, Monaco, Consolas, \"Liberation Mono\", \"Courier New\", monospace !default;\n// stylelint-enable value-keyword-case\n$font-family-base:            var(--#{$prefix}font-sans-serif) !default;\n$font-family-code:            var(--#{$prefix}font-monospace) !default;\n\n// $font-size-root affects the value of `rem`, which is used for as well font sizes, paddings, and margins\n// $font-size-base affects the font size of the body text\n$font-size-root:              null !default;\n$font-size-base:              1rem !default; // Assumes the browser default, typically `16px`\n$font-size-sm:                $font-size-base * .875 !default;\n$font-size-lg:                $font-size-base * 1.25 !default;\n\n$font-weight-lighter:         lighter !default;\n$font-weight-light:           300 !default;\n$font-weight-normal:          400 !default;\n$font-weight-medium:          500 !default;\n$font-weight-semibold:        600 !default;\n$font-weight-bold:            700 !default;\n$font-weight-bolder:          bolder !default;\n\n$font-weight-base:            $font-weight-normal !default;\n\n$line-height-base:            1.5 !default;\n$line-height-sm:              1.25 !default;\n$line-height-lg:              2 !default;\n\n$h1-font-size:                $font-size-base * 2.5 !default;\n$h2-font-size:                $font-size-base * 2 !default;\n$h3-font-size:                $font-size-base * 1.75 !default;\n$h4-font-size:                $font-size-base * 1.5 !default;\n$h5-font-size:                $font-size-base * 1.25 !default;\n$h6-font-size:                $font-size-base !default;\n// scss-docs-end font-variables\n\n// scss-docs-start font-sizes\n$font-sizes: (\n  1: $h1-font-size,\n  2: $h2-font-size,\n  3: $h3-font-size,\n  4: $h4-font-size,\n  5: $h5-font-size,\n  6: $h6-font-size\n) !default;\n// scss-docs-end font-sizes\n\n// scss-docs-start headings-variables\n$headings-margin-bottom:      $spacer * .5 !default;\n$headings-font-family:        null !default;\n$headings-font-style:         null !default;\n$headings-font-weight:        500 !default;\n$headings-line-height:        1.2 !default;\n$headings-color:              inherit !default;\n// scss-docs-end headings-variables\n\n// scss-docs-start display-headings\n$display-font-sizes: (\n  1: 5rem,\n  2: 4.5rem,\n  3: 4rem,\n  4: 3.5rem,\n  5: 3rem,\n  6: 2.5rem\n) !default;\n\n$display-font-family: null !default;\n$display-font-style:  null !default;\n$display-font-weight: 300 !default;\n$display-line-height: $headings-line-height !default;\n// scss-docs-end display-headings\n\n// scss-docs-start type-variables\n$lead-font-size:              $font-size-base * 1.25 !default;\n$lead-font-weight:            300 !default;\n\n$small-font-size:             .875em !default;\n\n$sub-sup-font-size:           .75em !default;\n\n// fusv-disable\n$text-muted:                  var(--#{$prefix}secondary-color) !default; // Deprecated in 5.3.0\n// fusv-enable\n\n$initialism-font-size:        $small-font-size !default;\n\n$blockquote-margin-y:         $spacer !default;\n$blockquote-font-size:        $font-size-base * 1.25 !default;\n$blockquote-footer-color:     $gray-600 !default;\n$blockquote-footer-font-size: $small-font-size !default;\n\n$hr-margin-y:                 $spacer !default;\n$hr-color:                    inherit !default;\n\n// fusv-disable\n$hr-bg-color:                 null !default; // Deprecated in v5.2.0\n$hr-height:                   null !default; // Deprecated in v5.2.0\n// fusv-enable\n\n$hr-border-color:             null !default; // Allows for inherited colors\n$hr-border-width:             var(--#{$prefix}border-width) !default;\n$hr-opacity:                  .25 !default;\n\n// scss-docs-start vr-variables\n$vr-border-width:             var(--#{$prefix}border-width) !default;\n// scss-docs-end vr-variables\n\n$legend-margin-bottom:        .5rem !default;\n$legend-font-size:            1.5rem !default;\n$legend-font-weight:          null !default;\n\n$dt-font-weight:              $font-weight-bold !default;\n\n$list-inline-padding:         .5rem !default;\n\n$mark-padding:                .1875em !default;\n$mark-color:                  $body-color !default;\n$mark-bg:                     $yellow-100 !default;\n// scss-docs-end type-variables\n\n\n// Tables\n//\n// Customizes the `.table` component with basic values, each used across all table variations.\n\n// scss-docs-start table-variables\n$table-cell-padding-y:        .5rem !default;\n$table-cell-padding-x:        .5rem !default;\n$table-cell-padding-y-sm:     .25rem !default;\n$table-cell-padding-x-sm:     .25rem !default;\n\n$table-cell-vertical-align:   top !default;\n\n$table-color:                 var(--#{$prefix}emphasis-color) !default;\n$table-bg:                    var(--#{$prefix}body-bg) !default;\n$table-accent-bg:             transparent !default;\n\n$table-th-font-weight:        null !default;\n\n$table-striped-color:         $table-color !default;\n$table-striped-bg-factor:     .05 !default;\n$table-striped-bg:            rgba(var(--#{$prefix}emphasis-color-rgb), $table-striped-bg-factor) !default;\n\n$table-active-color:          $table-color !default;\n$table-active-bg-factor:      .1 !default;\n$table-active-bg:             rgba(var(--#{$prefix}emphasis-color-rgb), $table-active-bg-factor) !default;\n\n$table-hover-color:           $table-color !default;\n$table-hover-bg-factor:       .075 !default;\n$table-hover-bg:              rgba(var(--#{$prefix}emphasis-color-rgb), $table-hover-bg-factor) !default;\n\n$table-border-factor:         .2 !default;\n$table-border-width:          var(--#{$prefix}border-width) !default;\n$table-border-color:          var(--#{$prefix}border-color) !default;\n\n$table-striped-order:         odd !default;\n$table-striped-columns-order: even !default;\n\n$table-group-separator-color: currentcolor !default;\n\n$table-caption-color:         var(--#{$prefix}secondary-color) !default;\n\n$table-bg-scale:              -80% !default;\n// scss-docs-end table-variables\n\n// scss-docs-start table-loop\n$table-variants: (\n  \"primary\":    shift-color($primary, $table-bg-scale),\n  \"secondary\":  shift-color($secondary, $table-bg-scale),\n  \"success\":    shift-color($success, $table-bg-scale),\n  \"info\":       shift-color($info, $table-bg-scale),\n  \"warning\":    shift-color($warning, $table-bg-scale),\n  \"danger\":     shift-color($danger, $table-bg-scale),\n  \"light\":      $light,\n  \"dark\":       $dark,\n) !default;\n// scss-docs-end table-loop\n\n\n// Buttons + Forms\n//\n// Shared variables that are reassigned to `$input-` and `$btn-` specific variables.\n\n// scss-docs-start input-btn-variables\n$input-btn-padding-y:         .375rem !default;\n$input-btn-padding-x:         .75rem !default;\n$input-btn-font-family:       null !default;\n$input-btn-font-size:         $font-size-base !default;\n$input-btn-line-height:       $line-height-base !default;\n\n$input-btn-focus-width:         $focus-ring-width !default;\n$input-btn-focus-color-opacity: $focus-ring-opacity !default;\n$input-btn-focus-color:         $focus-ring-color !default;\n$input-btn-focus-blur:          $focus-ring-blur !default;\n$input-btn-focus-box-shadow:    $focus-ring-box-shadow !default;\n\n$input-btn-padding-y-sm:      .25rem !default;\n$input-btn-padding-x-sm:      .5rem !default;\n$input-btn-font-size-sm:      $font-size-sm !default;\n\n$input-btn-padding-y-lg:      .5rem !default;\n$input-btn-padding-x-lg:      1rem !default;\n$input-btn-font-size-lg:      $font-size-lg !default;\n\n$input-btn-border-width:      var(--#{$prefix}border-width) !default;\n// scss-docs-end input-btn-variables\n\n\n// Buttons\n//\n// For each of Bootstrap's buttons, define text, background, and border color.\n\n// scss-docs-start btn-variables\n$btn-color:                   var(--#{$prefix}body-color) !default;\n$btn-padding-y:               $input-btn-padding-y !default;\n$btn-padding-x:               $input-btn-padding-x !default;\n$btn-font-family:             $input-btn-font-family !default;\n$btn-font-size:               $input-btn-font-size !default;\n$btn-line-height:             $input-btn-line-height !default;\n$btn-white-space:             null !default; // Set to `nowrap` to prevent text wrapping\n\n$btn-padding-y-sm:            $input-btn-padding-y-sm !default;\n$btn-padding-x-sm:            $input-btn-padding-x-sm !default;\n$btn-font-size-sm:            $input-btn-font-size-sm !default;\n\n$btn-padding-y-lg:            $input-btn-padding-y-lg !default;\n$btn-padding-x-lg:            $input-btn-padding-x-lg !default;\n$btn-font-size-lg:            $input-btn-font-size-lg !default;\n\n$btn-border-width:            $input-btn-border-width !default;\n\n$btn-font-weight:             $font-weight-normal !default;\n$btn-box-shadow:              inset 0 1px 0 rgba($white, .15), 0 1px 1px rgba($black, .075) !default;\n$btn-focus-width:             $input-btn-focus-width !default;\n$btn-focus-box-shadow:        $input-btn-focus-box-shadow !default;\n$btn-disabled-opacity:        .65 !default;\n$btn-active-box-shadow:       inset 0 3px 5px rgba($black, .125) !default;\n\n$btn-link-color:              var(--#{$prefix}link-color) !default;\n$btn-link-hover-color:        var(--#{$prefix}link-hover-color) !default;\n$btn-link-disabled-color:     $gray-600 !default;\n$btn-link-focus-shadow-rgb:   to-rgb(mix(color-contrast($link-color), $link-color, 15%)) !default;\n\n// Allows for customizing button radius independently from global border radius\n$btn-border-radius:           var(--#{$prefix}border-radius) !default;\n$btn-border-radius-sm:        var(--#{$prefix}border-radius-sm) !default;\n$btn-border-radius-lg:        var(--#{$prefix}border-radius-lg) !default;\n\n$btn-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$btn-hover-bg-shade-amount:       15% !default;\n$btn-hover-bg-tint-amount:        15% !default;\n$btn-hover-border-shade-amount:   20% !default;\n$btn-hover-border-tint-amount:    10% !default;\n$btn-active-bg-shade-amount:      20% !default;\n$btn-active-bg-tint-amount:       20% !default;\n$btn-active-border-shade-amount:  25% !default;\n$btn-active-border-tint-amount:   10% !default;\n// scss-docs-end btn-variables\n\n\n// Forms\n\n// scss-docs-start form-text-variables\n$form-text-margin-top:                  .25rem !default;\n$form-text-font-size:                   $small-font-size !default;\n$form-text-font-style:                  null !default;\n$form-text-font-weight:                 null !default;\n$form-text-color:                       var(--#{$prefix}secondary-color) !default;\n// scss-docs-end form-text-variables\n\n// scss-docs-start form-label-variables\n$form-label-margin-bottom:              .5rem !default;\n$form-label-font-size:                  null !default;\n$form-label-font-style:                 null !default;\n$form-label-font-weight:                null !default;\n$form-label-color:                      null !default;\n// scss-docs-end form-label-variables\n\n// scss-docs-start form-input-variables\n$input-padding-y:                       $input-btn-padding-y !default;\n$input-padding-x:                       $input-btn-padding-x !default;\n$input-font-family:                     $input-btn-font-family !default;\n$input-font-size:                       $input-btn-font-size !default;\n$input-font-weight:                     $font-weight-base !default;\n$input-line-height:                     $input-btn-line-height !default;\n\n$input-padding-y-sm:                    $input-btn-padding-y-sm !default;\n$input-padding-x-sm:                    $input-btn-padding-x-sm !default;\n$input-font-size-sm:                    $input-btn-font-size-sm !default;\n\n$input-padding-y-lg:                    $input-btn-padding-y-lg !default;\n$input-padding-x-lg:                    $input-btn-padding-x-lg !default;\n$input-font-size-lg:                    $input-btn-font-size-lg !default;\n\n$input-bg:                              var(--#{$prefix}body-bg) !default;\n$input-disabled-color:                  null !default;\n$input-disabled-bg:                     var(--#{$prefix}secondary-bg) !default;\n$input-disabled-border-color:           null !default;\n\n$input-color:                           var(--#{$prefix}body-color) !default;\n$input-border-color:                    var(--#{$prefix}border-color) !default;\n$input-border-width:                    $input-btn-border-width !default;\n$input-box-shadow:                      var(--#{$prefix}box-shadow-inset) !default;\n\n$input-border-radius:                   var(--#{$prefix}border-radius) !default;\n$input-border-radius-sm:                var(--#{$prefix}border-radius-sm) !default;\n$input-border-radius-lg:                var(--#{$prefix}border-radius-lg) !default;\n\n$input-focus-bg:                        $input-bg !default;\n$input-focus-border-color:              tint-color($component-active-bg, 50%) !default;\n$input-focus-color:                     $input-color !default;\n$input-focus-width:                     $input-btn-focus-width !default;\n$input-focus-box-shadow:                $input-btn-focus-box-shadow !default;\n\n$input-placeholder-color:               var(--#{$prefix}secondary-color) !default;\n$input-plaintext-color:                 var(--#{$prefix}body-color) !default;\n\n$input-height-border:                   calc(#{$input-border-width} * 2) !default; // stylelint-disable-line function-disallowed-list\n\n$input-height-inner:                    add($input-line-height * 1em, $input-padding-y * 2) !default;\n$input-height-inner-half:               add($input-line-height * .5em, $input-padding-y) !default;\n$input-height-inner-quarter:            add($input-line-height * .25em, $input-padding-y * .5) !default;\n\n$input-height:                          add($input-line-height * 1em, add($input-padding-y * 2, $input-height-border, false)) !default;\n$input-height-sm:                       add($input-line-height * 1em, add($input-padding-y-sm * 2, $input-height-border, false)) !default;\n$input-height-lg:                       add($input-line-height * 1em, add($input-padding-y-lg * 2, $input-height-border, false)) !default;\n\n$input-transition:                      border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$form-color-width:                      3rem !default;\n// scss-docs-end form-input-variables\n\n// scss-docs-start form-check-variables\n$form-check-input-width:                  1em !default;\n$form-check-min-height:                   $font-size-base * $line-height-base !default;\n$form-check-padding-start:                $form-check-input-width + .5em !default;\n$form-check-margin-bottom:                .125rem !default;\n$form-check-label-color:                  null !default;\n$form-check-label-cursor:                 null !default;\n$form-check-transition:                   null !default;\n\n$form-check-input-active-filter:          brightness(90%) !default;\n\n$form-check-input-bg:                     $input-bg !default;\n$form-check-input-border:                 var(--#{$prefix}border-width) solid var(--#{$prefix}border-color) !default;\n$form-check-input-border-radius:          .25em !default;\n$form-check-radio-border-radius:          50% !default;\n$form-check-input-focus-border:           $input-focus-border-color !default;\n$form-check-input-focus-box-shadow:       $focus-ring-box-shadow !default;\n\n$form-check-input-checked-color:          $component-active-color !default;\n$form-check-input-checked-bg-color:       $component-active-bg !default;\n$form-check-input-checked-border-color:   $form-check-input-checked-bg-color !default;\n$form-check-input-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-checked-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='m6 10 3 3 6-6'/></svg>\") !default;\n$form-check-radio-checked-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='2' fill='#{$form-check-input-checked-color}'/></svg>\") !default;\n\n$form-check-input-indeterminate-color:          $component-active-color !default;\n$form-check-input-indeterminate-bg-color:       $component-active-bg !default;\n$form-check-input-indeterminate-border-color:   $form-check-input-indeterminate-bg-color !default;\n$form-check-input-indeterminate-bg-image:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 20 20'><path fill='none' stroke='#{$form-check-input-indeterminate-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M6 10h8'/></svg>\") !default;\n\n$form-check-input-disabled-opacity:        .5 !default;\n$form-check-label-disabled-opacity:        $form-check-input-disabled-opacity !default;\n$form-check-btn-check-disabled-opacity:    $btn-disabled-opacity !default;\n\n$form-check-inline-margin-end:    1rem !default;\n// scss-docs-end form-check-variables\n\n// scss-docs-start form-switch-variables\n$form-switch-color:               rgba($black, .25) !default;\n$form-switch-width:               2em !default;\n$form-switch-padding-start:       $form-switch-width + .5em !default;\n$form-switch-bg-image:            url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-color}'/></svg>\") !default;\n$form-switch-border-radius:       $form-switch-width !default;\n$form-switch-transition:          background-position .15s ease-in-out !default;\n\n$form-switch-focus-color:         $input-focus-border-color !default;\n$form-switch-focus-bg-image:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-focus-color}'/></svg>\") !default;\n\n$form-switch-checked-color:       $component-active-color !default;\n$form-switch-checked-bg-image:    url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'><circle r='3' fill='#{$form-switch-checked-color}'/></svg>\") !default;\n$form-switch-checked-bg-position: right center !default;\n// scss-docs-end form-switch-variables\n\n// scss-docs-start input-group-variables\n$input-group-addon-padding-y:           $input-padding-y !default;\n$input-group-addon-padding-x:           $input-padding-x !default;\n$input-group-addon-font-weight:         $input-font-weight !default;\n$input-group-addon-color:               $input-color !default;\n$input-group-addon-bg:                  var(--#{$prefix}tertiary-bg) !default;\n$input-group-addon-border-color:        $input-border-color !default;\n// scss-docs-end input-group-variables\n\n// scss-docs-start form-select-variables\n$form-select-padding-y:             $input-padding-y !default;\n$form-select-padding-x:             $input-padding-x !default;\n$form-select-font-family:           $input-font-family !default;\n$form-select-font-size:             $input-font-size !default;\n$form-select-indicator-padding:     $form-select-padding-x * 3 !default; // Extra padding for background-image\n$form-select-font-weight:           $input-font-weight !default;\n$form-select-line-height:           $input-line-height !default;\n$form-select-color:                 $input-color !default;\n$form-select-bg:                    $input-bg !default;\n$form-select-disabled-color:        null !default;\n$form-select-disabled-bg:           $input-disabled-bg !default;\n$form-select-disabled-border-color: $input-disabled-border-color !default;\n$form-select-bg-position:           right $form-select-padding-x center !default;\n$form-select-bg-size:               16px 12px !default; // In pixels because image dimensions\n$form-select-indicator-color:       $gray-800 !default;\n$form-select-indicator:             url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16'><path fill='none' stroke='#{$form-select-indicator-color}' stroke-linecap='round' stroke-linejoin='round' stroke-width='2' d='m2 5 6 6 6-6'/></svg>\") !default;\n\n$form-select-feedback-icon-padding-end: $form-select-padding-x * 2.5 + $form-select-indicator-padding !default;\n$form-select-feedback-icon-position:    center right $form-select-indicator-padding !default;\n$form-select-feedback-icon-size:        $input-height-inner-half $input-height-inner-half !default;\n\n$form-select-border-width:        $input-border-width !default;\n$form-select-border-color:        $input-border-color !default;\n$form-select-border-radius:       $input-border-radius !default;\n$form-select-box-shadow:          var(--#{$prefix}box-shadow-inset) !default;\n\n$form-select-focus-border-color:  $input-focus-border-color !default;\n$form-select-focus-width:         $input-focus-width !default;\n$form-select-focus-box-shadow:    0 0 0 $form-select-focus-width $input-btn-focus-color !default;\n\n$form-select-padding-y-sm:        $input-padding-y-sm !default;\n$form-select-padding-x-sm:        $input-padding-x-sm !default;\n$form-select-font-size-sm:        $input-font-size-sm !default;\n$form-select-border-radius-sm:    $input-border-radius-sm !default;\n\n$form-select-padding-y-lg:        $input-padding-y-lg !default;\n$form-select-padding-x-lg:        $input-padding-x-lg !default;\n$form-select-font-size-lg:        $input-font-size-lg !default;\n$form-select-border-radius-lg:    $input-border-radius-lg !default;\n\n$form-select-transition:          $input-transition !default;\n// scss-docs-end form-select-variables\n\n// scss-docs-start form-range-variables\n$form-range-track-width:          100% !default;\n$form-range-track-height:         .5rem !default;\n$form-range-track-cursor:         pointer !default;\n$form-range-track-bg:             var(--#{$prefix}secondary-bg) !default;\n$form-range-track-border-radius:  1rem !default;\n$form-range-track-box-shadow:     var(--#{$prefix}box-shadow-inset) !default;\n\n$form-range-thumb-width:                   1rem !default;\n$form-range-thumb-height:                  $form-range-thumb-width !default;\n$form-range-thumb-bg:                      $component-active-bg !default;\n$form-range-thumb-border:                  0 !default;\n$form-range-thumb-border-radius:           1rem !default;\n$form-range-thumb-box-shadow:              0 .1rem .25rem rgba($black, .1) !default;\n$form-range-thumb-focus-box-shadow:        0 0 0 1px $body-bg, $input-focus-box-shadow !default;\n$form-range-thumb-focus-box-shadow-width:  $input-focus-width !default; // For focus box shadow issue in Edge\n$form-range-thumb-active-bg:               tint-color($component-active-bg, 70%) !default;\n$form-range-thumb-disabled-bg:             var(--#{$prefix}secondary-color) !default;\n$form-range-thumb-transition:              background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n// scss-docs-end form-range-variables\n\n// scss-docs-start form-file-variables\n$form-file-button-color:          $input-color !default;\n$form-file-button-bg:             var(--#{$prefix}tertiary-bg) !default;\n$form-file-button-hover-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end form-file-variables\n\n// scss-docs-start form-floating-variables\n$form-floating-height:                  add(3.5rem, $input-height-border) !default;\n$form-floating-line-height:             1.25 !default;\n$form-floating-padding-x:               $input-padding-x !default;\n$form-floating-padding-y:               1rem !default;\n$form-floating-input-padding-t:         1.625rem !default;\n$form-floating-input-padding-b:         .625rem !default;\n$form-floating-label-height:            1.5em !default;\n$form-floating-label-opacity:           .65 !default;\n$form-floating-label-transform:         scale(.85) translateY(-.5rem) translateX(.15rem) !default;\n$form-floating-label-disabled-color:    $gray-600 !default;\n$form-floating-transition:              opacity .1s ease-in-out, transform .1s ease-in-out !default;\n// scss-docs-end form-floating-variables\n\n// Form validation\n\n// scss-docs-start form-feedback-variables\n$form-feedback-margin-top:          $form-text-margin-top !default;\n$form-feedback-font-size:           $form-text-font-size !default;\n$form-feedback-font-style:          $form-text-font-style !default;\n$form-feedback-valid-color:         $success !default;\n$form-feedback-invalid-color:       $danger !default;\n\n$form-feedback-icon-valid-color:    $form-feedback-valid-color !default;\n$form-feedback-icon-valid:          url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'><path fill='#{$form-feedback-icon-valid-color}' d='M2.3 6.73.6 4.53c-.4-1.04.46-1.4 1.1-.8l1.1 1.4 3.4-3.8c.6-.63 1.6-.27 1.2.7l-4 4.6c-.43.5-.8.4-1.1.1z'/></svg>\") !default;\n$form-feedback-icon-invalid-color:  $form-feedback-invalid-color !default;\n$form-feedback-icon-invalid:        url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 12 12' width='12' height='12' fill='none' stroke='#{$form-feedback-icon-invalid-color}'><circle cx='6' cy='6' r='4.5'/><path stroke-linejoin='round' d='M5.8 3.6h.4L6 6.5z'/><circle cx='6' cy='8.2' r='.6' fill='#{$form-feedback-icon-invalid-color}' stroke='none'/></svg>\") !default;\n// scss-docs-end form-feedback-variables\n\n// scss-docs-start form-validation-colors\n$form-valid-color:                  $form-feedback-valid-color !default;\n$form-valid-border-color:           $form-feedback-valid-color !default;\n$form-invalid-color:                $form-feedback-invalid-color !default;\n$form-invalid-border-color:         $form-feedback-invalid-color !default;\n// scss-docs-end form-validation-colors\n\n// scss-docs-start form-validation-states\n$form-validation-states: (\n  \"valid\": (\n    \"color\": var(--#{$prefix}form-valid-color),\n    \"icon\": $form-feedback-icon-valid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}success),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}success-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-valid-border-color),\n  ),\n  \"invalid\": (\n    \"color\": var(--#{$prefix}form-invalid-color),\n    \"icon\": $form-feedback-icon-invalid,\n    \"tooltip-color\": #fff,\n    \"tooltip-bg-color\": var(--#{$prefix}danger),\n    \"focus-box-shadow\": 0 0 $input-btn-focus-blur $input-focus-width rgba(var(--#{$prefix}danger-rgb), $input-btn-focus-color-opacity),\n    \"border-color\": var(--#{$prefix}form-invalid-border-color),\n  )\n) !default;\n// scss-docs-end form-validation-states\n\n// Z-index master list\n//\n// Warning: Avoid customizing these values. They're used for a bird's eye view\n// of components dependent on the z-axis and are designed to all work together.\n\n// scss-docs-start zindex-stack\n$zindex-dropdown:                   1000 !default;\n$zindex-sticky:                     1020 !default;\n$zindex-fixed:                      1030 !default;\n$zindex-offcanvas-backdrop:         1040 !default;\n$zindex-offcanvas:                  1045 !default;\n$zindex-modal-backdrop:             1050 !default;\n$zindex-modal:                      1055 !default;\n$zindex-popover:                    1070 !default;\n$zindex-tooltip:                    1080 !default;\n$zindex-toast:                      1090 !default;\n// scss-docs-end zindex-stack\n\n// scss-docs-start zindex-levels-map\n$zindex-levels: (\n  n1: -1,\n  0: 0,\n  1: 1,\n  2: 2,\n  3: 3\n) !default;\n// scss-docs-end zindex-levels-map\n\n\n// Navs\n\n// scss-docs-start nav-variables\n$nav-link-padding-y:                .5rem !default;\n$nav-link-padding-x:                1rem !default;\n$nav-link-font-size:                null !default;\n$nav-link-font-weight:              null !default;\n$nav-link-color:                    var(--#{$prefix}link-color) !default;\n$nav-link-hover-color:              var(--#{$prefix}link-hover-color) !default;\n$nav-link-transition:               color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out !default;\n$nav-link-disabled-color:           var(--#{$prefix}secondary-color) !default;\n$nav-link-focus-box-shadow:         $focus-ring-box-shadow !default;\n\n$nav-tabs-border-color:             var(--#{$prefix}border-color) !default;\n$nav-tabs-border-width:             var(--#{$prefix}border-width) !default;\n$nav-tabs-border-radius:            var(--#{$prefix}border-radius) !default;\n$nav-tabs-link-hover-border-color:  var(--#{$prefix}secondary-bg) var(--#{$prefix}secondary-bg) $nav-tabs-border-color !default;\n$nav-tabs-link-active-color:        var(--#{$prefix}emphasis-color) !default;\n$nav-tabs-link-active-bg:           var(--#{$prefix}body-bg) !default;\n$nav-tabs-link-active-border-color: var(--#{$prefix}border-color) var(--#{$prefix}border-color) $nav-tabs-link-active-bg !default;\n\n$nav-pills-border-radius:           var(--#{$prefix}border-radius) !default;\n$nav-pills-link-active-color:       $component-active-color !default;\n$nav-pills-link-active-bg:          $component-active-bg !default;\n\n$nav-underline-gap:                 1rem !default;\n$nav-underline-border-width:        .125rem !default;\n$nav-underline-link-active-color:   var(--#{$prefix}emphasis-color) !default;\n// scss-docs-end nav-variables\n\n\n// Navbar\n\n// scss-docs-start navbar-variables\n$navbar-padding-y:                  $spacer * .5 !default;\n$navbar-padding-x:                  null !default;\n\n$navbar-nav-link-padding-x:         .5rem !default;\n\n$navbar-brand-font-size:            $font-size-lg !default;\n// Compute the navbar-brand padding-y so the navbar-brand will have the same height as navbar-text and nav-link\n$nav-link-height:                   $font-size-base * $line-height-base + $nav-link-padding-y * 2 !default;\n$navbar-brand-height:               $navbar-brand-font-size * $line-height-base !default;\n$navbar-brand-padding-y:            ($nav-link-height - $navbar-brand-height) * .5 !default;\n$navbar-brand-margin-end:           1rem !default;\n\n$navbar-toggler-padding-y:          .25rem !default;\n$navbar-toggler-padding-x:          .75rem !default;\n$navbar-toggler-font-size:          $font-size-lg !default;\n$navbar-toggler-border-radius:      $btn-border-radius !default;\n$navbar-toggler-focus-width:        $btn-focus-width !default;\n$navbar-toggler-transition:         box-shadow .15s ease-in-out !default;\n\n$navbar-light-color:                rgba(var(--#{$prefix}emphasis-color-rgb), .65) !default;\n$navbar-light-hover-color:          rgba(var(--#{$prefix}emphasis-color-rgb), .8) !default;\n$navbar-light-active-color:         rgba(var(--#{$prefix}emphasis-color-rgb), 1) !default;\n$navbar-light-disabled-color:       rgba(var(--#{$prefix}emphasis-color-rgb), .3) !default;\n$navbar-light-icon-color:           rgba($body-color, .75) !default;\n$navbar-light-toggler-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-light-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-light-toggler-border-color: rgba(var(--#{$prefix}emphasis-color-rgb), .15) !default;\n$navbar-light-brand-color:          $navbar-light-active-color !default;\n$navbar-light-brand-hover-color:    $navbar-light-active-color !default;\n// scss-docs-end navbar-variables\n\n// scss-docs-start navbar-dark-variables\n$navbar-dark-color:                 rgba($white, .55) !default;\n$navbar-dark-hover-color:           rgba($white, .75) !default;\n$navbar-dark-active-color:          $white !default;\n$navbar-dark-disabled-color:        rgba($white, .25) !default;\n$navbar-dark-icon-color:            $navbar-dark-color !default;\n$navbar-dark-toggler-icon-bg:       url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'><path stroke='#{$navbar-dark-icon-color}' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/></svg>\") !default;\n$navbar-dark-toggler-border-color:  rgba($white, .1) !default;\n$navbar-dark-brand-color:           $navbar-dark-active-color !default;\n$navbar-dark-brand-hover-color:     $navbar-dark-active-color !default;\n// scss-docs-end navbar-dark-variables\n\n\n// Dropdowns\n//\n// Dropdown menu container and contents.\n\n// scss-docs-start dropdown-variables\n$dropdown-min-width:                10rem !default;\n$dropdown-padding-x:                0 !default;\n$dropdown-padding-y:                .5rem !default;\n$dropdown-spacer:                   .125rem !default;\n$dropdown-font-size:                $font-size-base !default;\n$dropdown-color:                    var(--#{$prefix}body-color) !default;\n$dropdown-bg:                       var(--#{$prefix}body-bg) !default;\n$dropdown-border-color:             var(--#{$prefix}border-color-translucent) !default;\n$dropdown-border-radius:            var(--#{$prefix}border-radius) !default;\n$dropdown-border-width:             var(--#{$prefix}border-width) !default;\n$dropdown-inner-border-radius:      calc(#{$dropdown-border-radius} - #{$dropdown-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$dropdown-divider-bg:               $dropdown-border-color !default;\n$dropdown-divider-margin-y:         $spacer * .5 !default;\n$dropdown-box-shadow:               var(--#{$prefix}box-shadow) !default;\n\n$dropdown-link-color:               var(--#{$prefix}body-color) !default;\n$dropdown-link-hover-color:         $dropdown-link-color !default;\n$dropdown-link-hover-bg:            var(--#{$prefix}tertiary-bg) !default;\n\n$dropdown-link-active-color:        $component-active-color !default;\n$dropdown-link-active-bg:           $component-active-bg !default;\n\n$dropdown-link-disabled-color:      var(--#{$prefix}tertiary-color) !default;\n\n$dropdown-item-padding-y:           $spacer * .25 !default;\n$dropdown-item-padding-x:           $spacer !default;\n\n$dropdown-header-color:             $gray-600 !default;\n$dropdown-header-padding-x:         $dropdown-item-padding-x !default;\n$dropdown-header-padding-y:         $dropdown-padding-y !default;\n// fusv-disable\n$dropdown-header-padding:           $dropdown-header-padding-y $dropdown-header-padding-x !default; // Deprecated in v5.2.0\n// fusv-enable\n// scss-docs-end dropdown-variables\n\n// scss-docs-start dropdown-dark-variables\n$dropdown-dark-color:               $gray-300 !default;\n$dropdown-dark-bg:                  $gray-800 !default;\n$dropdown-dark-border-color:        $dropdown-border-color !default;\n$dropdown-dark-divider-bg:          $dropdown-divider-bg !default;\n$dropdown-dark-box-shadow:          null !default;\n$dropdown-dark-link-color:          $dropdown-dark-color !default;\n$dropdown-dark-link-hover-color:    $white !default;\n$dropdown-dark-link-hover-bg:       rgba($white, .15) !default;\n$dropdown-dark-link-active-color:   $dropdown-link-active-color !default;\n$dropdown-dark-link-active-bg:      $dropdown-link-active-bg !default;\n$dropdown-dark-link-disabled-color: $gray-500 !default;\n$dropdown-dark-header-color:        $gray-500 !default;\n// scss-docs-end dropdown-dark-variables\n\n\n// Pagination\n\n// scss-docs-start pagination-variables\n$pagination-padding-y:              .375rem !default;\n$pagination-padding-x:              .75rem !default;\n$pagination-padding-y-sm:           .25rem !default;\n$pagination-padding-x-sm:           .5rem !default;\n$pagination-padding-y-lg:           .75rem !default;\n$pagination-padding-x-lg:           1.5rem !default;\n\n$pagination-font-size:              $font-size-base !default;\n\n$pagination-color:                  var(--#{$prefix}link-color) !default;\n$pagination-bg:                     var(--#{$prefix}body-bg) !default;\n$pagination-border-radius:          var(--#{$prefix}border-radius) !default;\n$pagination-border-width:           var(--#{$prefix}border-width) !default;\n$pagination-margin-start:           calc(#{$pagination-border-width} * -1) !default; // stylelint-disable-line function-disallowed-list\n$pagination-border-color:           var(--#{$prefix}border-color) !default;\n\n$pagination-focus-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-focus-bg:               var(--#{$prefix}secondary-bg) !default;\n$pagination-focus-box-shadow:       $focus-ring-box-shadow !default;\n$pagination-focus-outline:          0 !default;\n\n$pagination-hover-color:            var(--#{$prefix}link-hover-color) !default;\n$pagination-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$pagination-hover-border-color:     var(--#{$prefix}border-color) !default; // Todo in v6: remove this?\n\n$pagination-active-color:           $component-active-color !default;\n$pagination-active-bg:              $component-active-bg !default;\n$pagination-active-border-color:    $component-active-bg !default;\n\n$pagination-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$pagination-disabled-bg:            var(--#{$prefix}secondary-bg) !default;\n$pagination-disabled-border-color:  var(--#{$prefix}border-color) !default;\n\n$pagination-transition:              color .15s ease-in-out, background-color .15s ease-in-out, border-color .15s ease-in-out, box-shadow .15s ease-in-out !default;\n\n$pagination-border-radius-sm:       var(--#{$prefix}border-radius-sm) !default;\n$pagination-border-radius-lg:       var(--#{$prefix}border-radius-lg) !default;\n// scss-docs-end pagination-variables\n\n\n// Placeholders\n\n// scss-docs-start placeholders\n$placeholder-opacity-max:           .5 !default;\n$placeholder-opacity-min:           .2 !default;\n// scss-docs-end placeholders\n\n// Cards\n\n// scss-docs-start card-variables\n$card-spacer-y:                     $spacer !default;\n$card-spacer-x:                     $spacer !default;\n$card-title-spacer-y:               $spacer * .5 !default;\n$card-title-color:                  null !default;\n$card-subtitle-color:               null !default;\n$card-border-width:                 var(--#{$prefix}border-width) !default;\n$card-border-color:                 var(--#{$prefix}border-color-translucent) !default;\n$card-border-radius:                var(--#{$prefix}border-radius) !default;\n$card-box-shadow:                   null !default;\n$card-inner-border-radius:          subtract($card-border-radius, $card-border-width) !default;\n$card-cap-padding-y:                $card-spacer-y * .5 !default;\n$card-cap-padding-x:                $card-spacer-x !default;\n$card-cap-bg:                       rgba(var(--#{$prefix}body-color-rgb), .03) !default;\n$card-cap-color:                    null !default;\n$card-height:                       null !default;\n$card-color:                        null !default;\n$card-bg:                           var(--#{$prefix}body-bg) !default;\n$card-img-overlay-padding:          $spacer !default;\n$card-group-margin:                 $grid-gutter-width * .5 !default;\n// scss-docs-end card-variables\n\n// Accordion\n\n// scss-docs-start accordion-variables\n$accordion-padding-y:                     1rem !default;\n$accordion-padding-x:                     1.25rem !default;\n$accordion-color:                         var(--#{$prefix}body-color) !default;\n$accordion-bg:                            var(--#{$prefix}body-bg) !default;\n$accordion-border-width:                  var(--#{$prefix}border-width) !default;\n$accordion-border-color:                  var(--#{$prefix}border-color) !default;\n$accordion-border-radius:                 var(--#{$prefix}border-radius) !default;\n$accordion-inner-border-radius:           subtract($accordion-border-radius, $accordion-border-width) !default;\n\n$accordion-body-padding-y:                $accordion-padding-y !default;\n$accordion-body-padding-x:                $accordion-padding-x !default;\n\n$accordion-button-padding-y:              $accordion-padding-y !default;\n$accordion-button-padding-x:              $accordion-padding-x !default;\n$accordion-button-color:                  var(--#{$prefix}body-color) !default;\n$accordion-button-bg:                     var(--#{$prefix}accordion-bg) !default;\n$accordion-transition:                    $btn-transition, border-radius .15s ease !default;\n$accordion-button-active-bg:              var(--#{$prefix}primary-bg-subtle) !default;\n$accordion-button-active-color:           var(--#{$prefix}primary-text-emphasis) !default;\n\n$accordion-button-focus-border-color:     $input-focus-border-color !default;\n$accordion-button-focus-box-shadow:       $btn-focus-box-shadow !default;\n\n$accordion-icon-width:                    1.25rem !default;\n$accordion-icon-color:                    $body-color !default;\n$accordion-icon-active-color:             $primary-text-emphasis !default;\n$accordion-icon-transition:               transform .2s ease-in-out !default;\n$accordion-icon-transform:                rotate(-180deg) !default;\n\n$accordion-button-icon:         url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n$accordion-button-active-icon:  url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$accordion-icon-active-color}'><path fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n// scss-docs-end accordion-variables\n\n// Tooltips\n\n// scss-docs-start tooltip-variables\n$tooltip-font-size:                 $font-size-sm !default;\n$tooltip-max-width:                 200px !default;\n$tooltip-color:                     var(--#{$prefix}body-bg) !default;\n$tooltip-bg:                        var(--#{$prefix}emphasis-color) !default;\n$tooltip-border-radius:             var(--#{$prefix}border-radius) !default;\n$tooltip-opacity:                   .9 !default;\n$tooltip-padding-y:                 $spacer * .25 !default;\n$tooltip-padding-x:                 $spacer * .5 !default;\n$tooltip-margin:                    null !default; // TODO: remove this in v6\n\n$tooltip-arrow-width:               .8rem !default;\n$tooltip-arrow-height:              .4rem !default;\n// fusv-disable\n$tooltip-arrow-color:               null !default; // Deprecated in Bootstrap 5.2.0 for CSS variables\n// fusv-enable\n// scss-docs-end tooltip-variables\n\n// Form tooltips must come after regular tooltips\n// scss-docs-start tooltip-feedback-variables\n$form-feedback-tooltip-padding-y:     $tooltip-padding-y !default;\n$form-feedback-tooltip-padding-x:     $tooltip-padding-x !default;\n$form-feedback-tooltip-font-size:     $tooltip-font-size !default;\n$form-feedback-tooltip-line-height:   null !default;\n$form-feedback-tooltip-opacity:       $tooltip-opacity !default;\n$form-feedback-tooltip-border-radius: $tooltip-border-radius !default;\n// scss-docs-end tooltip-feedback-variables\n\n\n// Popovers\n\n// scss-docs-start popover-variables\n$popover-font-size:                 $font-size-sm !default;\n$popover-bg:                        var(--#{$prefix}body-bg) !default;\n$popover-max-width:                 276px !default;\n$popover-border-width:              var(--#{$prefix}border-width) !default;\n$popover-border-color:              var(--#{$prefix}border-color-translucent) !default;\n$popover-border-radius:             var(--#{$prefix}border-radius-lg) !default;\n$popover-inner-border-radius:       calc(#{$popover-border-radius} - #{$popover-border-width}) !default; // stylelint-disable-line function-disallowed-list\n$popover-box-shadow:                var(--#{$prefix}box-shadow) !default;\n\n$popover-header-font-size:          $font-size-base !default;\n$popover-header-bg:                 var(--#{$prefix}secondary-bg) !default;\n$popover-header-color:              $headings-color !default;\n$popover-header-padding-y:          .5rem !default;\n$popover-header-padding-x:          $spacer !default;\n\n$popover-body-color:                var(--#{$prefix}body-color) !default;\n$popover-body-padding-y:            $spacer !default;\n$popover-body-padding-x:            $spacer !default;\n\n$popover-arrow-width:               1rem !default;\n$popover-arrow-height:              .5rem !default;\n// scss-docs-end popover-variables\n\n// fusv-disable\n// Deprecated in Bootstrap 5.2.0 for CSS variables\n$popover-arrow-color:               $popover-bg !default;\n$popover-arrow-outer-color:         var(--#{$prefix}border-color-translucent) !default;\n// fusv-enable\n\n\n// Toasts\n\n// scss-docs-start toast-variables\n$toast-max-width:                   350px !default;\n$toast-padding-x:                   .75rem !default;\n$toast-padding-y:                   .5rem !default;\n$toast-font-size:                   .875rem !default;\n$toast-color:                       null !default;\n$toast-background-color:            rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-border-width:                var(--#{$prefix}border-width) !default;\n$toast-border-color:                var(--#{$prefix}border-color-translucent) !default;\n$toast-border-radius:               var(--#{$prefix}border-radius) !default;\n$toast-box-shadow:                  var(--#{$prefix}box-shadow) !default;\n$toast-spacing:                     $container-padding-x !default;\n\n$toast-header-color:                var(--#{$prefix}secondary-color) !default;\n$toast-header-background-color:     rgba(var(--#{$prefix}body-bg-rgb), .85) !default;\n$toast-header-border-color:         $toast-border-color !default;\n// scss-docs-end toast-variables\n\n\n// Badges\n\n// scss-docs-start badge-variables\n$badge-font-size:                   .75em !default;\n$badge-font-weight:                 $font-weight-bold !default;\n$badge-color:                       $white !default;\n$badge-padding-y:                   .35em !default;\n$badge-padding-x:                   .65em !default;\n$badge-border-radius:               var(--#{$prefix}border-radius) !default;\n// scss-docs-end badge-variables\n\n\n// Modals\n\n// scss-docs-start modal-variables\n$modal-inner-padding:               $spacer !default;\n\n$modal-footer-margin-between:       .5rem !default;\n\n$modal-dialog-margin:               .5rem !default;\n$modal-dialog-margin-y-sm-up:       1.75rem !default;\n\n$modal-title-line-height:           $line-height-base !default;\n\n$modal-content-color:               null !default;\n$modal-content-bg:                  var(--#{$prefix}body-bg) !default;\n$modal-content-border-color:        var(--#{$prefix}border-color-translucent) !default;\n$modal-content-border-width:        var(--#{$prefix}border-width) !default;\n$modal-content-border-radius:       var(--#{$prefix}border-radius-lg) !default;\n$modal-content-inner-border-radius: subtract($modal-content-border-radius, $modal-content-border-width) !default;\n$modal-content-box-shadow-xs:       var(--#{$prefix}box-shadow-sm) !default;\n$modal-content-box-shadow-sm-up:    var(--#{$prefix}box-shadow) !default;\n\n$modal-backdrop-bg:                 $black !default;\n$modal-backdrop-opacity:            .5 !default;\n\n$modal-header-border-color:         var(--#{$prefix}border-color) !default;\n$modal-header-border-width:         $modal-content-border-width !default;\n$modal-header-padding-y:            $modal-inner-padding !default;\n$modal-header-padding-x:            $modal-inner-padding !default;\n$modal-header-padding:              $modal-header-padding-y $modal-header-padding-x !default; // Keep this for backwards compatibility\n\n$modal-footer-bg:                   null !default;\n$modal-footer-border-color:         $modal-header-border-color !default;\n$modal-footer-border-width:         $modal-header-border-width !default;\n\n$modal-sm:                          300px !default;\n$modal-md:                          500px !default;\n$modal-lg:                          800px !default;\n$modal-xl:                          1140px !default;\n\n$modal-fade-transform:              translate(0, -50px) !default;\n$modal-show-transform:              none !default;\n$modal-transition:                  transform .3s ease-out !default;\n$modal-scale-transform:             scale(1.02) !default;\n// scss-docs-end modal-variables\n\n\n// Alerts\n//\n// Define alert colors, border radius, and padding.\n\n// scss-docs-start alert-variables\n$alert-padding-y:               $spacer !default;\n$alert-padding-x:               $spacer !default;\n$alert-margin-bottom:           1rem !default;\n$alert-border-radius:           var(--#{$prefix}border-radius) !default;\n$alert-link-font-weight:        $font-weight-bold !default;\n$alert-border-width:            var(--#{$prefix}border-width) !default;\n$alert-dismissible-padding-r:   $alert-padding-x * 3 !default; // 3x covers width of x plus default padding on either side\n// scss-docs-end alert-variables\n\n// fusv-disable\n$alert-bg-scale:                -80% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-border-scale:            -70% !default; // Deprecated in v5.2.0, to be removed in v6\n$alert-color-scale:             40% !default; // Deprecated in v5.2.0, to be removed in v6\n// fusv-enable\n\n// Progress bars\n\n// scss-docs-start progress-variables\n$progress-height:                   1rem !default;\n$progress-font-size:                $font-size-base * .75 !default;\n$progress-bg:                       var(--#{$prefix}secondary-bg) !default;\n$progress-border-radius:            var(--#{$prefix}border-radius) !default;\n$progress-box-shadow:               var(--#{$prefix}box-shadow-inset) !default;\n$progress-bar-color:                $white !default;\n$progress-bar-bg:                   $primary !default;\n$progress-bar-animation-timing:     1s linear infinite !default;\n$progress-bar-transition:           width .6s ease !default;\n// scss-docs-end progress-variables\n\n\n// List group\n\n// scss-docs-start list-group-variables\n$list-group-color:                  var(--#{$prefix}body-color) !default;\n$list-group-bg:                     var(--#{$prefix}body-bg) !default;\n$list-group-border-color:           var(--#{$prefix}border-color) !default;\n$list-group-border-width:           var(--#{$prefix}border-width) !default;\n$list-group-border-radius:          var(--#{$prefix}border-radius) !default;\n\n$list-group-item-padding-y:         $spacer * .5 !default;\n$list-group-item-padding-x:         $spacer !default;\n// fusv-disable\n$list-group-item-bg-scale:          -80% !default; // Deprecated in v5.3.0\n$list-group-item-color-scale:       40% !default; // Deprecated in v5.3.0\n// fusv-enable\n\n$list-group-hover-bg:               var(--#{$prefix}tertiary-bg) !default;\n$list-group-active-color:           $component-active-color !default;\n$list-group-active-bg:              $component-active-bg !default;\n$list-group-active-border-color:    $list-group-active-bg !default;\n\n$list-group-disabled-color:         var(--#{$prefix}secondary-color) !default;\n$list-group-disabled-bg:            $list-group-bg !default;\n\n$list-group-action-color:           var(--#{$prefix}secondary-color) !default;\n$list-group-action-hover-color:     var(--#{$prefix}emphasis-color) !default;\n\n$list-group-action-active-color:    var(--#{$prefix}body-color) !default;\n$list-group-action-active-bg:       var(--#{$prefix}secondary-bg) !default;\n// scss-docs-end list-group-variables\n\n\n// Image thumbnails\n\n// scss-docs-start thumbnail-variables\n$thumbnail-padding:                 .25rem !default;\n$thumbnail-bg:                      var(--#{$prefix}body-bg) !default;\n$thumbnail-border-width:            var(--#{$prefix}border-width) !default;\n$thumbnail-border-color:            var(--#{$prefix}border-color) !default;\n$thumbnail-border-radius:           var(--#{$prefix}border-radius) !default;\n$thumbnail-box-shadow:              var(--#{$prefix}box-shadow-sm) !default;\n// scss-docs-end thumbnail-variables\n\n\n// Figures\n\n// scss-docs-start figure-variables\n$figure-caption-font-size:          $small-font-size !default;\n$figure-caption-color:              var(--#{$prefix}secondary-color) !default;\n// scss-docs-end figure-variables\n\n\n// Breadcrumbs\n\n// scss-docs-start breadcrumb-variables\n$breadcrumb-font-size:              null !default;\n$breadcrumb-padding-y:              0 !default;\n$breadcrumb-padding-x:              0 !default;\n$breadcrumb-item-padding-x:         .5rem !default;\n$breadcrumb-margin-bottom:          1rem !default;\n$breadcrumb-bg:                     null !default;\n$breadcrumb-divider-color:          var(--#{$prefix}secondary-color) !default;\n$breadcrumb-active-color:           var(--#{$prefix}secondary-color) !default;\n$breadcrumb-divider:                quote(\"/\") !default;\n$breadcrumb-divider-flipped:        $breadcrumb-divider !default;\n$breadcrumb-border-radius:          null !default;\n// scss-docs-end breadcrumb-variables\n\n// Carousel\n\n// scss-docs-start carousel-variables\n$carousel-control-color:             $white !default;\n$carousel-control-width:             15% !default;\n$carousel-control-opacity:           .5 !default;\n$carousel-control-hover-opacity:     .9 !default;\n$carousel-control-transition:        opacity .15s ease !default;\n\n$carousel-indicator-width:           30px !default;\n$carousel-indicator-height:          3px !default;\n$carousel-indicator-hit-area-height: 10px !default;\n$carousel-indicator-spacer:          3px !default;\n$carousel-indicator-opacity:         .5 !default;\n$carousel-indicator-active-bg:       $white !default;\n$carousel-indicator-active-opacity:  1 !default;\n$carousel-indicator-transition:      opacity .6s ease !default;\n\n$carousel-caption-width:             70% !default;\n$carousel-caption-color:             $white !default;\n$carousel-caption-padding-y:         1.25rem !default;\n$carousel-caption-spacer:            1.25rem !default;\n\n$carousel-control-icon-width:        2rem !default;\n\n$carousel-control-prev-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M11.354 1.646a.5.5 0 0 1 0 .708L5.707 8l5.647 5.646a.5.5 0 0 1-.708.708l-6-6a.5.5 0 0 1 0-.708l6-6a.5.5 0 0 1 .708 0z'/></svg>\") !default;\n$carousel-control-next-icon-bg:      url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$carousel-control-color}'><path d='M4.646 1.646a.5.5 0 0 1 .708 0l6 6a.5.5 0 0 1 0 .708l-6 6a.5.5 0 0 1-.708-.708L10.293 8 4.646 2.354a.5.5 0 0 1 0-.708z'/></svg>\") !default;\n\n$carousel-transition-duration:       .6s !default;\n$carousel-transition:                transform $carousel-transition-duration ease-in-out !default; // Define transform transition first if using multiple transitions (e.g., `transform 2s ease, opacity .5s ease-out`)\n// scss-docs-end carousel-variables\n\n// scss-docs-start carousel-dark-variables\n$carousel-dark-indicator-active-bg:  $black !default;\n$carousel-dark-caption-color:        $black !default;\n$carousel-dark-control-icon-filter:  invert(1) grayscale(100) !default;\n// scss-docs-end carousel-dark-variables\n\n\n// Spinners\n\n// scss-docs-start spinner-variables\n$spinner-width:           2rem !default;\n$spinner-height:          $spinner-width !default;\n$spinner-vertical-align:  -.125em !default;\n$spinner-border-width:    .25em !default;\n$spinner-animation-speed: .75s !default;\n\n$spinner-width-sm:        1rem !default;\n$spinner-height-sm:       $spinner-width-sm !default;\n$spinner-border-width-sm: .2em !default;\n// scss-docs-end spinner-variables\n\n\n// Close\n\n// scss-docs-start close-variables\n$btn-close-width:            1em !default;\n$btn-close-height:           $btn-close-width !default;\n$btn-close-padding-x:        .25em !default;\n$btn-close-padding-y:        $btn-close-padding-x !default;\n$btn-close-color:            $black !default;\n$btn-close-bg:               url(\"data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='#{$btn-close-color}'><path d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/></svg>\") !default;\n$btn-close-focus-shadow:     $focus-ring-box-shadow !default;\n$btn-close-opacity:          .5 !default;\n$btn-close-hover-opacity:    .75 !default;\n$btn-close-focus-opacity:    1 !default;\n$btn-close-disabled-opacity: .25 !default;\n$btn-close-white-filter:     invert(1) grayscale(100%) brightness(200%) !default;\n// scss-docs-end close-variables\n\n\n// Offcanvas\n\n// scss-docs-start offcanvas-variables\n$offcanvas-padding-y:               $modal-inner-padding !default;\n$offcanvas-padding-x:               $modal-inner-padding !default;\n$offcanvas-horizontal-width:        400px !default;\n$offcanvas-vertical-height:         30vh !default;\n$offcanvas-transition-duration:     .3s !default;\n$offcanvas-border-color:            $modal-content-border-color !default;\n$offcanvas-border-width:            $modal-content-border-width !default;\n$offcanvas-title-line-height:       $modal-title-line-height !default;\n$offcanvas-bg-color:                var(--#{$prefix}body-bg) !default;\n$offcanvas-color:                   var(--#{$prefix}body-color) !default;\n$offcanvas-box-shadow:              $modal-content-box-shadow-xs !default;\n$offcanvas-backdrop-bg:             $modal-backdrop-bg !default;\n$offcanvas-backdrop-opacity:        $modal-backdrop-opacity !default;\n// scss-docs-end offcanvas-variables\n\n// Code\n\n$code-font-size:                    $small-font-size !default;\n$code-color:                        $pink !default;\n\n$kbd-padding-y:                     .1875rem !default;\n$kbd-padding-x:                     .375rem !default;\n$kbd-font-size:                     $code-font-size !default;\n$kbd-color:                         var(--#{$prefix}body-bg) !default;\n$kbd-bg:                            var(--#{$prefix}body-color) !default;\n$nested-kbd-font-weight:            null !default; // Deprecated in v5.2.0, removing in v6\n\n$pre-color:                         null !default;\n","/***\n * Rules for the UX/UI of skip navigation link btn. \n *It's only visible to people\n * navigating with keyboard for accessibility purposes\n * ref: https://www.youtube.com/watch?v=VUR0I5mqq7I\n ***/\n\n.skip-link {\n  position: fixed;\n  top: 0;\n  left: 0;\n  right: 0;\n  text-align: center;\n  background-color: var(--pst-color-warning);\n  // Ensure we are using a WCAG conformant colour\n  color: var(--pst-color-warning-text) !important;\n  padding: 0.5rem;\n  z-index: $zindex-modal;\n  border-bottom: 1px solid var(--pst-color-border);\n\n  // This shows / hides the button\n  transform: translateY(-100%);\n  transition: transform 150ms ease-in-out;\n  &:focus {\n    transform: translateY(0%);\n    // ensure this is visible\n    outline: 3px solid $foundation-black;\n  }\n}\n",".bd-container {\n  flex-grow: 1;\n  display: flex;\n  justify-content: center;\n\n  .bd-container__inner {\n    display: flex;\n  }\n}\n\n.bd-page-width {\n  width: 100%;\n  @include media-breakpoint-up(lg) {\n    max-width: $breakpoint-page-width;\n  }\n}\n","// Breakpoint viewport sizes and media queries.\n//\n// Breakpoints are defined as a map of (name: minimum width), order from small to large:\n//\n//    (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px)\n//\n// The map defined in the `$grid-breakpoints` global variable is used as the `$breakpoints` argument by default.\n\n// Name of the next breakpoint, or null for the last breakpoint.\n//\n//    >> breakpoint-next(sm)\n//    md\n//    >> breakpoint-next(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    md\n//    >> breakpoint-next(sm, $breakpoint-names: (xs sm md lg xl xxl))\n//    md\n@function breakpoint-next($name, $breakpoints: $grid-breakpoints, $breakpoint-names: map-keys($breakpoints)) {\n  $n: index($breakpoint-names, $name);\n  @if not $n {\n    @error \"breakpoint `#{$name}` not found in `#{$breakpoints}`\";\n  }\n  @return if($n < length($breakpoint-names), nth($breakpoint-names, $n + 1), null);\n}\n\n// Minimum breakpoint width. Null for the smallest (first) breakpoint.\n//\n//    >> breakpoint-min(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    576px\n@function breakpoint-min($name, $breakpoints: $grid-breakpoints) {\n  $min: map-get($breakpoints, $name);\n  @return if($min != 0, $min, null);\n}\n\n// Maximum breakpoint width.\n// The maximum value is reduced by 0.02px to work around the limitations of\n// `min-` and `max-` prefixes and viewports with fractional widths.\n// See https://www.w3.org/TR/mediaqueries-4/#mq-min-max\n// Uses 0.02px rather than 0.01px to work around a current rounding bug in Safari.\n// See https://bugs.webkit.org/show_bug.cgi?id=178261\n//\n//    >> breakpoint-max(md, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    767.98px\n@function breakpoint-max($name, $breakpoints: $grid-breakpoints) {\n  $max: map-get($breakpoints, $name);\n  @return if($max and $max > 0, $max - .02, null);\n}\n\n// Returns a blank string if smallest breakpoint, otherwise returns the name with a dash in front.\n// Useful for making responsive utilities.\n//\n//    >> breakpoint-infix(xs, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"\"  (Returns a blank string)\n//    >> breakpoint-infix(sm, (xs: 0, sm: 576px, md: 768px, lg: 992px, xl: 1200px, xxl: 1400px))\n//    \"-sm\"\n@function breakpoint-infix($name, $breakpoints: $grid-breakpoints) {\n  @return if(breakpoint-min($name, $breakpoints) == null, \"\", \"-#{$name}\");\n}\n\n// Media of at least the minimum breakpoint width. No query for the smallest breakpoint.\n// Makes the @content apply to the given breakpoint and wider.\n@mixin media-breakpoint-up($name, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($name, $breakpoints);\n  @if $min {\n    @media (min-width: $min) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media of at most the maximum breakpoint width. No query for the largest breakpoint.\n// Makes the @content apply to the given breakpoint and narrower.\n@mixin media-breakpoint-down($name, $breakpoints: $grid-breakpoints) {\n  $max: breakpoint-max($name, $breakpoints);\n  @if $max {\n    @media (max-width: $max) {\n      @content;\n    }\n  } @else {\n    @content;\n  }\n}\n\n// Media that spans multiple breakpoint widths.\n// Makes the @content apply between the min and max breakpoints\n@mixin media-breakpoint-between($lower, $upper, $breakpoints: $grid-breakpoints) {\n  $min: breakpoint-min($lower, $breakpoints);\n  $max: breakpoint-max($upper, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($lower, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($upper, $breakpoints) {\n      @content;\n    }\n  }\n}\n\n// Media between the breakpoint's minimum and maximum widths.\n// No minimum for the smallest breakpoint, and no maximum for the largest one.\n// Makes the @content apply only to the given breakpoint, not viewports any wider or narrower.\n@mixin media-breakpoint-only($name, $breakpoints: $grid-breakpoints) {\n  $min:  breakpoint-min($name, $breakpoints);\n  $next: breakpoint-next($name, $breakpoints);\n  $max:  breakpoint-max($next, $breakpoints);\n\n  @if $min != null and $max != null {\n    @media (min-width: $min) and (max-width: $max) {\n      @content;\n    }\n  } @else if $max == null {\n    @include media-breakpoint-up($name, $breakpoints) {\n      @content;\n    }\n  } @else if $min == null {\n    @include media-breakpoint-down($next, $breakpoints) {\n      @content;\n    }\n  }\n}\n",".bd-header-version-warning,\n.bd-header-announcement {\n  min-height: 3rem;\n  width: 100%;\n  display: flex;\n  position: relative;\n  align-items: center;\n  justify-content: center;\n  text-align: center;\n  padding: 0.5rem 12.5%; // Horizontal padding so the width is 75%\n  // One breakpoint less than $breakpoint-sidebar-primary. See variables/_layout.scss for more info.\n  @include media-breakpoint-down(lg) {\n    // Announcements can take a bit more width on mobile\n    padding: 0.5rem 2%;\n  }\n\n  p {\n    font-weight: bold;\n    margin: 0;\n  }\n\n  &:after {\n    position: absolute;\n    width: 100%;\n    height: 100%;\n    left: 0;\n    top: 0;\n    content: \"\";\n    z-index: -1; // So it doesn't hover over the content\n  }\n\n  &:empty {\n    display: none;\n  }\n\n  // Ensure there is enough contrast against the background\n  a {\n    color: var(--pst-color-inline-code-links);\n  }\n}\n\n// Bg color is now defined in the theme color palette - using our secondary color\n.bd-header-announcement {\n  &:after {\n    background-color: var(--pst-color-secondary-bg);\n  }\n}\n\n.bd-header-version-warning {\n  &:after {\n    background-color: var(--pst-color-danger-bg);\n  }\n}\n","/**\n * Main content area\n */\n.bd-main {\n  flex-grow: 1;\n  flex-direction: column;\n  display: flex;\n  min-width: 0;\n\n  .bd-content {\n    display: flex;\n    justify-content: center;\n    height: 100%;\n    .bd-article-container {\n      justify-content: start;\n      display: flex;\n      flex-direction: column;\n      // Max-width is slightly more than the W3 since our docs often have images.\n      // We shoot for about 100 characters per line instead of 80.\n      // ref: https://www.w3.org/WAI/tutorials/page-structure/styling/#line-length\n      width: 100%;\n      max-width: 60em;\n      overflow-x: auto; // Prevent wide content from pushing off the secondary sidebar\n      padding: 1rem;\n      .bd-article {\n        // Give a bit more verticle spacing on wide screens\n        @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n          padding-top: 1.5rem;\n          padding-left: 2rem;\n        }\n      }\n    }\n  }\n}\n",".bd-footer {\n  width: 100%;\n  border-top: 1px solid var(--pst-color-border);\n\n  .bd-footer__inner {\n    display: flex;\n    flex-grow: 1;\n    padding: 1rem;\n    margin: auto;\n  }\n\n  .footer-items__start,\n  .footer-items__center,\n  .footer-items__end {\n    display: flex;\n    flex-direction: column;\n    gap: 0.5rem;\n    justify-content: center;\n    flex-grow: 1;\n  }\n\n  .footer-items__center {\n    text-align: center;\n  }\n\n  .footer-items__end {\n    text-align: end;\n  }\n\n  // So that paragraphs don't take up extra room\n  .footer-item p {\n    margin-bottom: 0;\n  }\n}\n",".bd-footer-article {\n  margin-top: auto;\n\n  .footer-article-items {\n    display: flex;\n    flex-direction: column;\n  }\n}\n",".bd-footer-content {\n  .footer-content-items {\n    display: flex;\n    flex-direction: column;\n    margin-top: auto;\n  }\n}\n","/**\n * Header at the top of the page\n * It includes the announcement bar and the navigation bar.\n */\n\n// Styling for the Icon links can be found in components/_icon-links.scss\n\n// If we want the shadow to only point downward in the future, set\n// box-shadow to: 0 0.125rem 0.25rem -0.125rem rgba(0, 0, 0, 0.11);\n.bd-header {\n  position: sticky;\n  top: 0;\n  z-index: $zindex-fixed;\n\n  // Overrides bootstrap\n  background: var(--pst-color-on-background) !important;\n  box-shadow: 0 0.125rem 0.25rem 0 var(--pst-color-shadow);\n\n  width: 100%;\n  padding: 0;\n  max-width: 100vw;\n  justify-content: center;\n  .bd-header__inner {\n    display: flex;\n    align-items: center;\n    height: fit-content;\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n\n  // These items will define the height of the header\n  .navbar-item {\n    height: var(--pst-header-height);\n    max-height: var(--pst-header-height);\n    display: flex;\n    align-items: center;\n  }\n\n  // Hide the header items on mobile\n  .navbar-header-items {\n    display: none;\n    flex-shrink: 1;\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: flex;\n      flex-grow: 1;\n      padding: 0 0 0 0.5rem;\n    }\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center,\n  .navbar-header-items__start {\n    display: flex;\n    align-items: center;\n    flex-flow: wrap;\n    // In case we wrap our items to multiple rows on small screens\n    row-gap: 0;\n  }\n\n  .navbar-header-items__end,\n  .navbar-header-items__center {\n    column-gap: 1rem;\n  }\n\n  // A little smaller because this is displayed by default on mobile\n  .navbar-header-items__start {\n    flex-shrink: 0;\n    margin-right: auto;\n    gap: 0.5rem;\n  }\n\n  .navbar-header-items__end {\n    // End navbar items should snap to the right\n    justify-content: end;\n  }\n\n  // Contains the navigation links within the navbar\n  .navbar-nav {\n    display: flex;\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      // Center align on wide screens so the dropdown button is centered properly\n      align-items: center;\n    }\n\n    li a.nav-link {\n      @include link-style-text;\n    }\n\n    // Current page is always underlined in the navbar\n    > .current > .nav-link {\n      @include link-navbar-current;\n    }\n\n    // Dropdowns for the extra links\n    .dropdown {\n      button {\n        display: unset;\n        color: var(--pst-color-text-muted);\n        border: none;\n        @include link-style-hover;\n        @include focus-indicator;\n      }\n\n      .dropdown-menu {\n        z-index: $zindex-popover;\n        border: 1px solid var(--pst-color-border);\n        box-shadow: 0 0 0.3rem 0.1rem var(--pst-color-shadow);\n        background-color: var(--pst-color-on-background);\n        padding: 0.5rem 0;\n        margin: 0.5rem 0;\n        min-width: 20rem;\n\n        .dropdown-item {\n          // Give the items in the dropdown some breathing room but let the hit\n          // and hover area of the items extend to the edges of the menu\n          padding: 0.25rem 1.5rem;\n\n          // Override Bootstrap\n          &:focus:not(:hover):not(:active) {\n            background-color: inherit;\n          }\n        }\n\n        // Hide the menu unless show has been clicked\n        &:not(.show) {\n          display: none;\n        }\n      }\n    }\n  }\n}\n\n// inline the element in the navbar as long as they fit and use display block when collapsing\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  .navbar-center-items .navbar-item {\n    display: inline-block;\n  }\n}\n\n.nav-link {\n  @include link-style-hover;\n\n  // Override Bootstrap\n  transition: none;\n\n  &.nav-external:after {\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-external-link);\n    font-size: 0.75em;\n    margin-left: 0.3em;\n  }\n}\n\n.bd-navbar-elements li.nav-item i {\n  font-size: 0.7rem;\n  padding-left: 2px;\n  vertical-align: middle;\n}\n\n/**\n * Showing and hiding the sidebar toggle buttons and header items\n */\n\n// Hide the header items on mobile\n.bd-header {\n  // Toggle labels\n  label {\n    &.sidebar-toggle {\n      display: flex;\n      cursor: pointer;\n      font-size: var(--pst-font-size-icon);\n      align-items: center;\n      color: var(--pst-color-muted);\n      margin-bottom: 0;\n      padding-bottom: 0.25rem;\n    }\n\n    &.primary-toggle {\n      margin-right: 1rem;\n      @include media-breakpoint-up($breakpoint-sidebar-primary) {\n        display: none;\n      }\n    }\n\n    &.secondary-toggle {\n      margin-left: 1rem;\n      @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n        display: none;\n      }\n    }\n    @include icon-navbar-hover;\n    @include focus-indicator;\n  }\n\n  // Hide the navbar header items on mobile because they're in the sidebar\n  .navbar-header-items {\n    display: none;\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: inherit;\n    }\n  }\n}\n\n// THe elements next to the hamburger menu only show on narrow screens\n.navbar-persistent--mobile {\n  margin-left: auto;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: none;\n  }\n}\n\n// The navbar-persistent content should only show on wide screens\n.navbar-persistent--container {\n  display: none;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: flex;\n  }\n}\n",".header-article__inner {\n  display: flex;\n  padding: 0 0.5rem;\n\n  // The items define the height so that it disappears if there are no items\n  .header-article-item {\n    min-height: var(--pst-header-article-height);\n    height: var(--pst-header-article-height);\n  }\n\n  .header-article-items__start,\n  .header-article-items__end {\n    display: flex;\n    align-items: start;\n    gap: 0.5rem;\n  }\n\n  .header-article-items__end {\n    margin-left: auto;\n  }\n}\n","/**\n * The primary sidebar on the left.\n * e.g., between-pages navigation.\n */\n\n.bd-sidebar-primary {\n  display: flex;\n  flex-direction: column;\n  gap: 1rem;\n  max-height: calc(100vh - var(--pst-header-height));\n  position: sticky;\n  top: var(--pst-header-height);\n  @include make-col(3);\n\n  // Borders padding and whitespace\n  padding: 2rem 1rem 1rem 1rem;\n  border-right: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n\n  font-size: var(--pst-sidebar-font-size-mobile);\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // override bootstrap when navlink are displayed in the sidebar\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n  }\n\n  &.no-sidebar {\n    border-right: 0;\n  }\n\n  &.hide-on-wide {\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  // Headers shouldn't be colored in the sidebars and some extensions add headers\n  h1,\n  h2,\n  h3,\n  h4 {\n    color: var(--pst-color-text-base);\n  }\n\n  .sidebar-primary-items__start,\n  .sidebar-primary-items__end {\n    .sidebar-primary-item {\n      padding: 0.5rem 0;\n    }\n  }\n\n  // Hide the sidebar header items on widescreen since they are visible in the header\n  .sidebar-header-items {\n    display: flex;\n    flex-direction: column;\n\n    .sidebar-header-items__title {\n      font-weight: var(--pst-sidebar-header-font-weight);\n      font-size: var(--pst-sidebar-header-font-size);\n      color: var(--pst-color-text-base);\n      margin-bottom: 0.5rem;\n    }\n\n    // The dropdown toggle for extra links just shows them all instead.\n    .nav-item.dropdown {\n      // On mobile, the dropdown behaves like any other link, no hiding\n      button {\n        display: none;\n      }\n\n      .dropdown-menu {\n        display: flex;\n        flex-direction: column;\n        padding: 0;\n        margin: 0;\n        border: none;\n        background-color: inherit;\n        font-size: inherit;\n      }\n    }\n\n    // Center header items get displayed vertically, end items are displayed horizontally\n    .sidebar-header-items__center {\n      display: flex;\n      flex-direction: column;\n    }\n\n    // Positioning end items\n    .sidebar-header-items__end {\n      display: flex;\n      align-items: center;\n      gap: 0.5rem;\n    }\n\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      display: none;\n    }\n  }\n\n  .sidebar-primary-items__start {\n    // Add a border on mobile to separate it from the header sidebar area\n    border-top: 1px solid var(--pst-color-border);\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      border-top: none;\n    }\n  }\n\n  .sidebar-primary-items__end {\n    margin-top: auto;\n    margin-bottom: 1em;\n  }\n  .list-caption {\n    list-style: none;\n    padding-left: 0px;\n  }\n  li {\n    position: relative;\n    // If it has children, add a bit more padding to wrap the content to avoid\n    // overlapping with the <label>\n    &.has-children {\n      > .reference {\n        padding-right: 30px;\n      }\n    }\n  }\n  // Navigation item chevrons\n  label.toctree-toggle {\n    position: absolute;\n    top: 0;\n    right: 0;\n    height: 30px;\n    width: 30px;\n\n    cursor: pointer;\n\n    display: flex;\n    justify-content: center;\n    align-items: center;\n\n    &:hover {\n      background: var(--pst-color-surface);\n    }\n\n    i {\n      display: inline-block;\n      font-size: 0.75rem;\n      text-align: center;\n      &:hover {\n        color: var(--pst-color-primary);\n      }\n    }\n  }\n  .label-parts {\n    width: 100%;\n    height: 100%;\n    &:hover {\n      background: none;\n    }\n    i {\n      width: 30px;\n      position: absolute;\n      top: 0.3em; // aligning chevron with text\n      right: 0em; // aligning chevron to the right\n    }\n  }\n}\n\n/* Between-page links and captions */\nnav.bd-links {\n  margin-right: -1rem;\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    display: block;\n  }\n\n  ul {\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  li > a {\n    display: block;\n    padding: 0.25rem 0.65rem;\n    @include link-sidebar;\n    box-shadow: none;\n\n    &.reference.external {\n      &:after {\n        font: var(--fa-font-solid);\n        content: var(--pst-icon-external-link);\n        font-size: 0.75em;\n        margin-left: 0.3em;\n      }\n    }\n  }\n\n  .current {\n    > a {\n      @include link-sidebar-current;\n      background-color: transparent;\n    }\n  }\n\n  // Title\n  p.bd-links__title {\n    font-size: var(--pst-sidebar-header-font-size);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n\n  // Toctree captions\n  p.caption {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    position: relative;\n    margin-top: 1.25rem;\n    margin-bottom: 0.5rem;\n    color: var(--pst-color-text-base);\n    &:first-child {\n      margin-top: 0;\n    }\n\n    font-size: var(--pst-sidebar-font-size-mobile);\n    @include media-breakpoint-up($breakpoint-sidebar-primary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n}\n","// Grid system\n//\n// Generate semantic grid columns with these mixins.\n\n@mixin make-row($gutter: $grid-gutter-width) {\n  --#{$prefix}gutter-x: #{$gutter};\n  --#{$prefix}gutter-y: 0;\n  display: flex;\n  flex-wrap: wrap;\n  // TODO: Revisit calc order after https://github.com/react-bootstrap/react-bootstrap/issues/6039 is fixed\n  margin-top: calc(-1 * var(--#{$prefix}gutter-y)); // stylelint-disable-line function-disallowed-list\n  margin-right: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n  margin-left: calc(-.5 * var(--#{$prefix}gutter-x)); // stylelint-disable-line function-disallowed-list\n}\n\n@mixin make-col-ready() {\n  // Add box sizing if only the grid is loaded\n  box-sizing: if(variable-exists(include-column-box-sizing) and $include-column-box-sizing, border-box, null);\n  // Prevent columns from becoming too narrow when at smaller grid tiers by\n  // always setting `width: 100%;`. This works because we set the width\n  // later on to override this initial width.\n  flex-shrink: 0;\n  width: 100%;\n  max-width: 100%; // Prevent `.col-auto`, `.col` (& responsive variants) from breaking out the grid\n  padding-right: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  padding-left: calc(var(--#{$prefix}gutter-x) * .5); // stylelint-disable-line function-disallowed-list\n  margin-top: var(--#{$prefix}gutter-y);\n}\n\n@mixin make-col($size: false, $columns: $grid-columns) {\n  @if $size {\n    flex: 0 0 auto;\n    width: percentage(divide($size, $columns));\n\n  } @else {\n    flex: 1 1 0;\n    max-width: 100%;\n  }\n}\n\n@mixin make-col-auto() {\n  flex: 0 0 auto;\n  width: auto;\n}\n\n@mixin make-col-offset($size, $columns: $grid-columns) {\n  $num: divide($size, $columns);\n  margin-left: if($num == 0, 0, percentage($num));\n}\n\n// Row columns\n//\n// Specify on a parent element(e.g., .row) to force immediate children into NN\n// number of columns. Supports wrapping to new lines, but does not do a Masonry\n// style grid.\n@mixin row-cols($count) {\n  > * {\n    flex: 0 0 auto;\n    width: percentage(divide(1, $count));\n  }\n}\n\n// Framework grid generation\n//\n// Used only by Bootstrap to generate the correct number of grid classes given\n// any value of `$grid-columns`.\n\n@mixin make-grid-columns($columns: $grid-columns, $gutter: $grid-gutter-width, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      // Provide basic `.col-{bp}` classes for equal-width flexbox columns\n      .col#{$infix} {\n        flex: 1 0 0%; // Flexbugs #4: https://github.com/philipwalton/flexbugs#flexbug-4\n      }\n\n      .row-cols#{$infix}-auto > * {\n        @include make-col-auto();\n      }\n\n      @if $grid-row-columns > 0 {\n        @for $i from 1 through $grid-row-columns {\n          .row-cols#{$infix}-#{$i} {\n            @include row-cols($i);\n          }\n        }\n      }\n\n      .col#{$infix}-auto {\n        @include make-col-auto();\n      }\n\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .col#{$infix}-#{$i} {\n            @include make-col($i, $columns);\n          }\n        }\n\n        // `$columns - 1` because offsetting by the width of an entire row isn't possible\n        @for $i from 0 through ($columns - 1) {\n          @if not ($infix == \"\" and $i == 0) { // Avoid emitting useless .offset-0\n            .offset#{$infix}-#{$i} {\n              @include make-col-offset($i, $columns);\n            }\n          }\n        }\n      }\n\n      // Gutters\n      //\n      // Make use of `.g-*`, `.gx-*` or `.gy-*` utilities to change spacing between the columns.\n      @each $key, $value in $gutters {\n        .g#{$infix}-#{$key},\n        .gx#{$infix}-#{$key} {\n          --#{$prefix}gutter-x: #{$value};\n        }\n\n        .g#{$infix}-#{$key},\n        .gy#{$infix}-#{$key} {\n          --#{$prefix}gutter-y: #{$value};\n        }\n      }\n    }\n  }\n}\n\n@mixin make-cssgrid($columns: $grid-columns, $breakpoints: $grid-breakpoints) {\n  @each $breakpoint in map-keys($breakpoints) {\n    $infix: breakpoint-infix($breakpoint, $breakpoints);\n\n    @include media-breakpoint-up($breakpoint, $breakpoints) {\n      @if $columns > 0 {\n        @for $i from 1 through $columns {\n          .g-col#{$infix}-#{$i} {\n            grid-column: auto / span $i;\n          }\n        }\n\n        // Start with `1` because `0` is and invalid value.\n        // Ends with `$columns - 1` because offsetting by the width of an entire row isn't possible.\n        @for $i from 1 through ($columns - 1) {\n          .g-start#{$infix}-#{$i} {\n            grid-column-start: $i;\n          }\n        }\n      }\n    }\n  }\n}\n","/**\n * Secondary sidebar on the right.\n * e.g., in-page table of contents.\n */\n.bd-sidebar-secondary {\n  display: flex;\n  order: 2;\n  flex-shrink: 0;\n  flex-direction: column;\n  position: sticky;\n  top: var(--pst-header-height);\n  max-height: calc(100vh - var(--pst-header-height));\n  padding: 2rem 1rem 1rem 1rem;\n  width: var(--pst-sidebar-secondary);\n  font-size: var(--pst-sidebar-font-size-mobile);\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    font-size: var(--pst-sidebar-font-size);\n  }\n\n  // Color and border\n  background-color: var(--pst-color-background);\n  overflow-y: auto;\n}\n\n.sidebar-secondary-item {\n  padding: 0.5rem 0.5rem;\n  @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n    border-left: 1px solid var(--pst-color-border);\n    padding-left: 1rem;\n  }\n\n  i {\n    padding-right: 0.5rem;\n  }\n}\n","/*******************************************************************************\n* Rules for the UX/UI of sidebar sliding drawers on mobile\n* Note that this sheet controls styles across many parts of the theme\n* It is aggregated into this one sheet instead of being split across\n* components in order to keep it easier to debug in one place.\n* It is broken up into major sections below.\n*/\n\n/*******************************************************************************\n* Buttons and overlays\n*/\ninput.sidebar-toggle {\n  display: none;\n}\n\n// Background overlays\nlabel.overlay {\n  background-color: black;\n  opacity: 0.5;\n  height: 0;\n  width: 0;\n  position: fixed;\n  top: 0;\n  left: 0;\n  transition: opacity $animation-time ease-out;\n  z-index: $zindex-modal-backdrop;\n}\n\ninput {\n  // Show the correct overlay when its input is checked\n  &#__primary:checked + label.overlay.overlay-primary,\n  &#__secondary:checked + label.overlay.overlay-secondary {\n    height: 100vh;\n    width: 100vw;\n  }\n\n  // Primary sidebar slides in from the left\n  &#__primary:checked ~ .bd-container .bd-sidebar-primary {\n    visibility: visible;\n    margin-left: 0;\n  }\n\n  // Secondary sidebar slides in from the right\n  &#__secondary:checked ~ .bd-container .bd-sidebar-secondary {\n    visibility: visible;\n    margin-right: 0;\n  }\n}\n\n/*******************************************************************************\n* Sidebar drawer behavior\n*/\n\n/**\n * Behavior for sliding drawer elements that will be toggled with an input\n *\n * NOTE: We use this mixin to define the toggle behavior on narrow screens,\n * And the wide-screen behavior of the sections is defined in their own section\n * .scss files.\n */\n@mixin sliding-drawer($side: \"left\") {\n  position: fixed;\n  top: 0;\n  z-index: $zindex-modal;\n  height: 100vh;\n  max-height: 100vh;\n  width: 75%;\n  flex-grow: 0.75;\n  max-width: 350px;\n  transition: visibility $animation-time ease-out,\n    margin $animation-time ease-out;\n  visibility: hidden;\n\n  @if $side == \"right\" {\n    margin-right: -75%;\n    right: 0;\n  } @else {\n    margin-left: -75%;\n    left: 0;\n  }\n}\n\n// Primary sidebar hides/shows at earlier widths\n@include media-breakpoint-up($breakpoint-sidebar-primary) {\n  label.sidebar-toggle.primary-toggle {\n    display: none;\n  }\n\n  input#__primary {\n    &:checked + label.overlay.overlay-primary {\n      height: 0;\n      width: 0;\n    }\n  }\n\n  .bd-sidebar-primary {\n    margin-left: 0;\n    visibility: visible;\n  }\n}\n\n.bd-sidebar-primary {\n  @include media-breakpoint-down($breakpoint-sidebar-primary) {\n    @include sliding-drawer(\"left\");\n  }\n}\n\n.bd-sidebar-secondary {\n  @include media-breakpoint-down($breakpoint-sidebar-secondary) {\n    @include sliding-drawer(\"right\");\n  }\n}\n","/**\n * Breadcrumbs for parent pages meant for the article header\n */\nul.bd-breadcrumbs {\n  list-style: none;\n  padding-left: 0;\n  display: flex;\n  flex-wrap: wrap;\n\n  // Font size slightly smaller to avoid cluttering up space too much\n  font-size: 0.8rem;\n\n  li.breadcrumb-item {\n    display: flex;\n    align-items: baseline;\n\n    // Style should look like heavier in-page links\n    // keeping visited in the default link colour\n    font-weight: bold;\n    a {\n      @include link-style-text;\n    }\n\n    // Items that aren't the home have a caret to the left\n    &:not(.breadcrumb-home):before {\n      font: var(--fa-font-solid);\n      font-size: 0.8rem;\n      content: var(--pst-breadcrumb-divider);\n      color: var(--pst-color-text-muted);\n      padding: 0 0.5rem;\n    }\n  }\n}\n","/**\n * Icon links in the navbar\n */\n\n.navbar-icon-links {\n  display: flex;\n  flex-direction: row;\n  column-gap: 1rem;\n  flex-wrap: wrap;\n\n  // Remove the padding so that we can define it with flexbox gap above\n  li.nav-item a.nav-link {\n    padding-left: 0;\n    padding-right: 0;\n    @include icon-navbar-hover;\n  }\n\n  // Spacing and centering\n  a span {\n    display: flex;\n    align-items: center;\n  }\n\n  // Icons styling\n  i {\n    &.fa-brands,\n    &.fa-regular,\n    &.fa-solid {\n      vertical-align: middle;\n      font-style: normal;\n      font-size: var(--pst-font-size-icon);\n    }\n\n    /* Social media buttons hard-code the brand color */\n    &.fa-square-twitter:before {\n      color: #55acee;\n    }\n\n    &.fa-square-gitlab:before {\n      color: #548;\n    }\n\n    &.fa-bitbucket:before {\n      color: #0052cc;\n    }\n  }\n\n  // Force images to be icon-sized\n  img.icon-link-image {\n    height: 1.5em;\n    border-radius: 0.2rem;\n  }\n}\n","/**\n * Logo in the navbar\n */\n\n.navbar-brand {\n  position: relative;\n  height: var(--pst-header-height);\n  max-height: var(--pst-header-height);\n  padding: 0.5rem 0;\n  width: auto;\n  margin: 0;\n  display: flex;\n  // Ensure that the logo stays the same length while other content shrinks\n  flex-shrink: 0;\n  align-items: center;\n  gap: 0.5rem;\n\n  // If there's no logo image, we use a p element w/ the site title\n  p {\n    margin-bottom: 0;\n  }\n\n  // If there's a logo, it'll be in an img block\n  img {\n    max-width: 100%;\n    height: 100%;\n    width: auto;\n  }\n\n  // remove underline from brand title on default state\n  a {\n    text-decoration: none;\n  }\n  &:hover {\n    @include link-style-hover;\n  }\n  &:visited {\n    &:hover {\n      @include link-style-hover;\n    }\n  }\n}\n","/**\n * Navigation text links in the navbar\n */\n.navbar-nav {\n  ul {\n    display: block;\n    list-style: none;\n\n    // Reduce padding of nested `ul` items a bit\n    ul {\n      padding: 0 0 0 1rem;\n    }\n  }\n\n  // Navbar links - do not have an underline by default\n  li {\n    display: flex;\n    flex-direction: column;\n\n    a {\n      display: flex;\n      align-items: center;\n      height: 100%;\n      padding-top: 0.25rem;\n      padding-bottom: 0.25rem;\n      @include link-style-text;\n    }\n  }\n\n  /**\n   * Togglable expand/collapse\n   * This is only applicable to the primary sidebar which has these checkboxes\n   */\n  .toctree-checkbox {\n    position: absolute;\n    display: none;\n  }\n\n  .toctree-checkbox {\n    ~ ul {\n      display: none;\n    }\n    ~ label i {\n      transform: rotate(0deg);\n    }\n  }\n  .toctree-checkbox:checked {\n    ~ ul {\n      display: block;\n    }\n    ~ label i {\n      transform: rotate(180deg);\n    }\n  }\n}\n\n// Don't display the `site navigation` in the header menu\n.bd-header .navbar-nav > p.sidebar-header-items__title {\n  display: none;\n}\n","/**\n * The list of in-page TOC links\n */\n.page-toc {\n  .section-nav {\n    padding-left: 0;\n    border-bottom: none;\n\n    ul {\n      padding-left: 1rem;\n    }\n  }\n\n  // override bootstrap settings\n  .nav-link {\n    font-size: var(--pst-sidebar-font-size-mobile);\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      font-size: var(--pst-sidebar-font-size);\n    }\n  }\n\n  .onthispage {\n    color: var(--pst-color-text-base);\n    font-weight: var(--pst-sidebar-header-font-weight);\n    margin-bottom: 0.5rem;\n  }\n}\n","/**\n* Previous / Next navigation buttons\n**/\n.prev-next-area {\n  width: 100%;\n\n  p {\n    margin: 0 0.3em;\n    line-height: 1.3em;\n  }\n\n  i {\n    font-size: 1.2em;\n  }\n\n  a {\n    // So that buttons align with icons\n    display: flex;\n    align-items: center;\n    border: none;\n    padding: 10px;\n    max-width: 45%;\n    overflow-x: hidden;\n    color: var(--pst-color-text-muted);\n    text-decoration: none;\n\n    p.prev-next-title {\n      @include link-style-default;\n      font-weight: var(--pst-admonition-font-weight-heading);\n      font-size: 1.1em;\n    }\n\n    &:hover p.prev-next-title {\n      @include link-style-hover;\n    }\n    // Exception here - keep visited in the default link colour\n    // it still should hover on the default link hover colour\n    &:visited p.prev-next-title {\n      color: var(--pst-color-link);\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n\n    .prev-next-info {\n      flex-direction: column;\n      margin: 0 0.5em;\n\n      .prev-next-subtitle {\n        text-transform: capitalize;\n      }\n    }\n\n    &.left-prev {\n      float: left;\n    }\n\n    &.right-next {\n      float: right;\n\n      div.prev-next-info {\n        text-align: right;\n      }\n    }\n  }\n}\n","/**\n * Search field\n **/\n.bd-search {\n  position: relative;\n  padding-left: 0.5rem;\n  gap: 0.5rem;\n  background-color: var(--pst-color-background);\n  border-radius: $admonition-border-radius;\n  border: 1px solid var(--pst-color-border);\n  color: var(--pst-color-text-base);\n\n  // Background should always be same color regardless of active or not\n  &:active {\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n\n  .icon {\n    position: absolute;\n    color: var(--pst-color-border);\n    left: 25px;\n  }\n\n  .fa-solid.fa-magnifying-glass {\n    position: absolute;\n    left: calc((2.5rem - 0.7em) / 2);\n    color: var(--pst-color-text-muted);\n  }\n\n  input {\n    // Inner-text of the search bar\n    &::placeholder {\n      color: var(--pst-color-text-muted);\n    }\n\n    // Remove the little \"x\" that pops up when you start typing\n    &::-webkit-search-cancel-button,\n    &::-webkit-search-decoration {\n      -webkit-appearance: none;\n      appearance: none;\n    }\n  }\n\n  // Shows off the keyboard shortcuts for the button\n  .search-button__kbd-shortcut {\n    display: flex;\n    position: absolute;\n    right: 0.5rem;\n    color: var(--pst-color-border);\n  }\n}\n\n.form-control {\n  background-color: var(--pst-color-background);\n  color: var(--pst-color-text-base);\n\n  &:focus,\n  &:focus-visible {\n    border: none;\n    box-shadow: none;\n    outline: 3px solid var(--pst-color-accent);\n    background-color: var(--pst-color-background);\n    color: var(--pst-color-text-muted);\n  }\n}\n\n/**\n * Search button - located in the navbar\n */\n\n// Search link icon should be a bit bigger since it is separate from icon links\n.search-button {\n  display: flex;\n  align-items: center;\n  align-content: center;\n  color: var(--pst-color-text-muted);\n  // Needed to match other icons hover\n  padding: 0 0 0.25rem 0;\n  border-radius: 0;\n  @include icon-navbar-hover;\n  @include focus-indicator;\n\n  i {\n    font-size: 1.3rem;\n  }\n}\n\n// __search-container will only show up when we use the search pop-up bar\n.search-button__search-container,\n.search-button__overlay {\n  display: none;\n}\n.search-button__wrapper.show {\n  .search-button__search-container {\n    display: flex;\n    // Center in middle of screen just underneath header\n    position: fixed;\n    z-index: $zindex-modal;\n    top: 30%;\n    left: 50%;\n    transform: translate(-50%, -50%);\n    right: 1rem;\n    margin-top: 0.5rem;\n    width: 90%;\n    max-width: 800px;\n  }\n\n  .search-button__overlay {\n    display: flex;\n    position: fixed;\n    z-index: $zindex-modal-backdrop;\n    background-color: black;\n    opacity: 0.5;\n    width: 100%;\n    height: 100%;\n    top: 0px;\n    left: 0px;\n  }\n\n  form.bd-search {\n    flex-grow: 1;\n    padding-top: 0;\n    padding-bottom: 0;\n  }\n\n  // Font and input text a bit bigger\n  svg,\n  input {\n    font-size: var(--pst-font-size-icon);\n  }\n}\n\n/**\n * The search button component that looks like a field.\n * Lives at components/search-button-field.html\n */\n.search-button-field {\n  display: inline-flex;\n  align-items: center;\n  border: var(--pst-color-border) solid 1px;\n  border-radius: 1.5em;\n  color: var(--pst-color-text-muted);\n  padding: 0.5em;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    border: 2px solid var(--pst-color-link-hover);\n  }\n  &:focus-visible {\n    border: 2px solid var(--pst-color-accent);\n  }\n\n  // The keyboard shotcut text\n  .search-button__default-text {\n    font-size: var(--bs-nav-link-font-size);\n    font-weight: var(--bs-nav-link-font-weight);\n    margin-right: 0.5em;\n    margin-left: 0.5em;\n  }\n\n  .kbd-shortcut__modifier {\n    font-size: 0.75em;\n  }\n\n  // Ensures that all the text lines up in the middle\n  > * {\n    align-items: center;\n  }\n\n  // Only the icon should be visible on narrow screens\n  > :not(svg) {\n    display: none;\n\n    @include media-breakpoint-up(lg) {\n      display: flex;\n    }\n  }\n}\n","/**\n * The 'Hide Search Matches' button.\n * This only shows up when a person lands on a page after clicking a search result.\n * Clicking it removes the highlighting of the search term from the page.\n * We want it to behave like a button.\n */\ndiv#searchbox {\n  // Leave `#searchbox` rules empty so that it doesn't show at all when it is empty\n  p.highlight-link {\n    margin: 1rem 0;\n    width: fit-content;\n\n    // A bit more margin on wide screens to mimic article behavior\n    @include media-breakpoint-up($breakpoint-sidebar-secondary) {\n      margin-left: 2rem;\n    }\n\n    // Put outer shadow on this one so that we can darken the link w/ an inner shadow\n    @include box-shadow();\n\n    // Style the button to look like a Sphinx Design button\n    a {\n      border-radius: 0.25rem;\n      font-size: 1.25rem;\n      padding: 0.75rem;\n      background-color: var(--pst-color-primary);\n      color: var(--pst-color-primary-text);\n      text-decoration: none;\n\n      // The box shadow is inset so that it darkens the button on hover\n      transition: box-shadow 0.25s ease-out;\n      &:hover {\n        box-shadow: inset 0px 0px 50px 50px rgba(0, 0, 0, 0.25);\n      }\n\n      &:before {\n        content: var(--pst-icon-search-minus);\n        color: unset;\n        font: var(--fa-font-solid);\n        margin-right: 0.5rem;\n      }\n    }\n  }\n}\n","/*********************************************\n* SASS Mixins\n*********************************************/\n\n/**\n * A consistent box shadow style we apply across elements.\n */\n@mixin box-shadow() {\n  box-shadow: 0 0.2rem 0.5rem var(--pst-color-shadow),\n    0 0 0.0625rem var(--pst-color-shadow) !important;\n}\n\n/**\n   * Set background of some cell outputs to white-ish to make sure colors work\n   * This is because many libraries make output that only looks good on white\n   */\n@mixin cell-output-background {\n  color: var(--pst-color-on-background);\n  background-color: var(--pst-color-text-base);\n  border-radius: 0.25rem;\n  padding: 0.5rem;\n}\n","/**\n * Light/dark theme switcher\n */\n\n.theme-switch-button {\n  // overide bootstrap settings\n  margin: 0 -0.5rem;\n  padding: 0; // We pad the `span` not the container\n  color: var(--pst-color-text-muted);\n  border-radius: 0;\n  @include focus-indicator;\n\n  span {\n    display: none;\n    padding: 0.5em;\n\n    @include icon-navbar-hover;\n    &:active {\n      text-decoration: none;\n      color: var(--pst-color-link-hover);\n    }\n  }\n}\n\nhtml[data-mode=\"auto\"] .theme-switch-button span[data-mode=\"auto\"] {\n  display: flex;\n}\nhtml[data-mode=\"light\"] .theme-switch-button span[data-mode=\"light\"] {\n  display: flex;\n}\nhtml[data-mode=\"dark\"] .theme-switch-button span[data-mode=\"dark\"] {\n  display: flex;\n}\n","button.btn.version-switcher__button {\n  border-color: var(--pst-color-border);\n  color: var(--pst-color-text-base);\n  // Add a margin on narrow screens to avoid feeling cramped\n  margin-bottom: 1em;\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    margin-bottom: unset;\n  }\n\n  @include link-style-hover;\n  @include focus-indicator;\n  &:active {\n    color: var(--pst-color-text-base);\n    border-color: var(--pst-color-border);\n  }\n}\n\n.version-switcher__menu {\n  border-color: var(--pst-color-border);\n  border-radius: var(--bs-dropdown-border-radius);\n\n  a.list-group-item {\n    background-color: var(--pst-color-on-background);\n    color: var(--pst-color-text-base);\n    padding: 0.75rem 1.25rem;\n\n    &:not(:last-child) {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n    @include link-style-hover;\n    &:hover {\n      background-color: var(--pst-color-surface);\n    }\n    &.active {\n      @include link-sidebar-current;\n      position: relative;\n      z-index: 1;\n\n      span:before {\n        content: \"\";\n        width: 100%;\n        height: 100%;\n        position: absolute;\n        z-index: -1;\n        left: 0;\n        top: 0;\n      }\n    }\n  }\n}\n\n// Font behavior on mobile\nbutton.version-switcher__button,\n.version-switcher__menu {\n  font-size: 1.1em; // A bit smaller than other menu font\n  z-index: $zindex-modal; // higher than the sidebars\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    font-size: unset;\n  }\n}\n","/* Collapsing of the TOC sidebar while scrolling */\n/* Nav: hide second level (shown on .active) */\n\nnav.page-toc {\n  // A little extra space before the buttons\n  margin-bottom: 1rem;\n}\n\n.bd-toc .nav,\n.list-caption {\n  .nav {\n    display: none;\n\n    // So we can manually specify a level as visible in the config\n    &.visible {\n      display: block;\n    }\n  }\n\n  > .active > ul {\n    display: block;\n  }\n}\n\n// Each entry of the in-page TOC\n.toc-entry {\n  display: block;\n  a > code {\n    color: var(--pst-color-text-muted);\n  }\n\n  a.nav-link {\n    display: block;\n    padding: 0.125rem 0;\n\n    // Padding w/ negative margin so the top TOC item highlight overlaps w/ the TOC border\n    padding-left: 1rem;\n    margin-left: -1rem;\n\n    @include link-sidebar;\n\n    &.active {\n      @include link-sidebar-current;\n      background-color: transparent;\n\n      &:hover {\n        color: var(--pst-color-link-hover);\n      }\n    }\n  }\n}\n","div.versionadded,\ndiv.versionchanged,\ndiv.deprecated {\n  vertical-align: middle;\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0 0.6rem;\n  overflow: hidden;\n  page-break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n  @include box-shadow();\n  position: relative;\n\n  > p {\n    margin-bottom: 0.6rem;\n    margin-top: 0.6rem;\n  }\n}\n\ndiv.versionadded {\n  border-color: var(--pst-color-success);\n  background-color: var(--pst-color-success-bg);\n}\n\ndiv.versionchanged {\n  border-color: var(--pst-color-warning);\n  background-color: var(--pst-color-warning-bg);\n}\n\ndiv.deprecated {\n  border-color: var(--pst-color-danger);\n  background-color: var(--pst-color-danger-bg);\n}\n\nspan.versionmodified {\n  font-weight: 600;\n\n  &:before {\n    margin-right: 0.6rem;\n    color: var(--pst-color-info);\n    font: var(--fa-font-solid);\n    content: var(--pst-icon-versionmodified-default);\n  }\n}\n\nspan.versionmodified.added {\n  &:before {\n    color: var(--pst-color-success);\n    content: var(--pst-icon-versionmodified-added);\n  }\n}\n\nspan.versionmodified.changed {\n  &:before {\n    color: var(--pst-color-warning);\n    content: var(--pst-icon-versionmodified-changed);\n  }\n}\n\nspan.versionmodified.deprecated {\n  &:before {\n    color: var(--pst-color-danger);\n    content: var(--pst-icon-versionmodified-deprecated);\n  }\n}\n",".sidebar-indices-items {\n  display: flex;\n  flex-direction: column;\n  border-top: 1px solid var(--pst-color-border);\n\n  @include media-breakpoint-up($breakpoint-sidebar-primary) {\n    border-top: none;\n  }\n\n  .sidebar-indices-items__title {\n    font-weight: var(--pst-sidebar-header-font-weight);\n    font-size: var(--pst-sidebar-header-font-size);\n    color: var(--pst-color-text-base);\n    margin-bottom: 0.5rem;\n  }\n\n  ul.indices-link {\n    margin-right: -1rem;\n    list-style: none;\n    padding: 0;\n\n    li > a {\n      display: block;\n      padding: 0.25rem 0;\n      color: var(--pst-color-text-muted);\n\n      &:hover {\n        color: var(--pst-color-primary);\n        text-decoration: none;\n        background-color: transparent;\n      }\n    }\n  }\n}\n",".bd-sidebar-primary div#rtd-footer-container {\n  position: sticky;\n  bottom: -1rem;\n  margin: -1rem; // ignore sidebar padding\n\n  .rst-versions.rst-badge {\n    position: unset;\n    font-size: 0.9em;\n    font-family: var(--pst-font-family-base);\n    max-width: unset;\n\n    .rst-current-version {\n      display: flex;\n      align-items: center;\n      gap: 0.2rem;\n      height: 2.5rem;\n      transition: background-color 0.2s ease-out;\n      background-color: var(--pst-color-background);\n      color: var(--pst-color-success);\n      border-top: 1px solid var(--pst-color-border);\n    }\n\n    .fa.fa-book {\n      color: var(--pst-color-text-muted);\n      margin-right: auto;\n\n      &::after {\n        color: var(--pst-color-text-base);\n        content: \"Read The Docs\";\n        font-family: var(--pst-font-family-base);\n        font-weight: var(--pst-admonition-font-weight-heading);\n      }\n    }\n    .fa.fa-caret-down {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  .rst-versions.rst-badge.shift-up {\n    .rst-current-version {\n      border-bottom: 1px solid var(--pst-color-border);\n    }\n  }\n\n  .rst-other-versions {\n    background-color: var(--pst-color-surface);\n    color: var(--pst-color-text-base);\n\n    dl {\n      dd a {\n        color: var(--pst-color-text-muted);\n      }\n    }\n\n    hr {\n      background-color: var(--pst-color-border);\n    }\n\n    small a {\n      color: var(--pst-color-link);\n    }\n\n    input {\n      padding-left: 0.5rem;\n      border: 1px solid var(--pst-color-border);\n      background-color: var(--pst-color-surface);\n    }\n  }\n}\n","/**\n * Admonitions and blocks of styled content.\n * Admonitions CSS originally inspired by https://squidfunk.github.io/mkdocs-material/getting-started/\n */\n$admonition-border-radius: 0.25rem;\ndiv.admonition,\n.admonition {\n  margin: 1.5625em auto;\n  padding: 0 0.6rem 0.8rem 0.6rem;\n  overflow: hidden;\n  page-break-inside: avoid;\n  border-left: 0.2rem solid;\n  border-color: var(--pst-color-info);\n  border-radius: $admonition-border-radius;\n  background-color: var(--pst-color-on-background);\n  @include box-shadow();\n\n  // Last item should have no spacing since we'll control that w/ padding\n  *:last-child {\n    margin-bottom: 0;\n  }\n\n  // Items after the title should be indented\n  p.admonition-title ~ * {\n    margin-left: 1.4rem;\n    margin-right: 1.4rem;\n  }\n\n  // Lists need to have left margin so they don't spill into it\n  > ol,\n  > ul {\n    margin-left: 1em;\n  }\n\n  // Defaults for all admonitions\n  > .admonition-title {\n    margin: 0 -0.6rem;\n    padding: 0.4rem 0.6rem 0.4rem 2rem;\n    font-weight: var(--pst-admonition-font-weight-heading);\n    position: relative;\n    @include background-from-color-variable(--pst-color-info-bg);\n    // now that we use solid colors we want the title on top\n    z-index: 1;\n\n    &:after {\n      position: absolute;\n      left: 0.5rem;\n      width: 1rem;\n      height: 1rem;\n      color: var(--pst-color-info);\n      font: var(--fa-font-solid);\n      line-height: inherit;\n      content: var(--pst-icon-admonition-default);\n      opacity: 1;\n    }\n\n    // Next element after title needs some extra upper-space\n    + * {\n      margin-top: 0.4em;\n    }\n  }\n\n  &.attention {\n    border-color: var(--pst-color-attention);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-attention-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-attention);\n      }\n    }\n  }\n\n  &.caution {\n    border-color: var(--pst-color-warning);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-warning-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-caution);\n      }\n    }\n  }\n\n  &.warning {\n    border-color: var(--pst-color-warning);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-warning-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-warning);\n        content: var(--pst-icon-admonition-warning);\n      }\n    }\n  }\n\n  &.danger {\n    border-color: var(--pst-color-danger);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-danger-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-danger);\n      }\n    }\n  }\n\n  &.error {\n    border-color: var(--pst-color-danger);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-danger-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-danger);\n        content: var(--pst-icon-admonition-error);\n      }\n    }\n  }\n\n  &.hint {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-success-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-hint);\n      }\n    }\n  }\n\n  &.tip {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-success-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-tip);\n      }\n    }\n  }\n\n  &.important {\n    border-color: var(--pst-color-attention);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-attention-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-attention);\n        content: var(--pst-icon-admonition-important);\n      }\n    }\n  }\n\n  &.note {\n    border-color: var(--pst-color-info);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-info-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-info);\n        content: var(--pst-icon-admonition-note);\n      }\n    }\n  }\n\n  &.seealso {\n    border-color: var(--pst-color-success);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-success-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-success);\n        content: var(--pst-icon-admonition-seealso);\n      }\n    }\n  }\n\n  &.admonition-todo {\n    border-color: var(--pst-color-secondary);\n    > .admonition-title {\n      &:before {\n        background-color: var(--pst-color-secondary-bg);\n      }\n\n      &:after {\n        color: var(--pst-color-secondary);\n        content: var(--pst-icon-admonition-todo);\n      }\n    }\n  }\n\n  /**\n   * Special-case for a `sidebar` class that makes the admonition float to\n   * the right like the {sidebar} directive.\n   */\n  &.sidebar {\n    max-width: 40%;\n    float: right;\n    clear: both;\n    margin-left: 0.5rem;\n    margin-top: 0;\n\n    // Undo the .sidebar directive border\n    border-width: 0 0 0 0.2rem;\n\n    // TODO: these semantic-color-names border-color rules might no longer be\n    //       needed when we drop support for Sphinx 4 / docutils 0.17\n    &.attention,\n    &.important {\n      border-color: var(--pst-color-attention);\n    }\n\n    &.caution,\n    &.warning {\n      border-color: var(--pst-color-warning);\n    }\n\n    &.danger,\n    &.error {\n      border-color: var(--pst-color-danger);\n    }\n\n    &.hint,\n    &.tip,\n    &.seealso {\n      border-color: var(--pst-color-success);\n    }\n\n    &.note,\n    &.todo {\n      border-color: var(--pst-color-info);\n    }\n\n    // No inner margin since we have less horizontal space w/ the sidebar\n    p.admonition-title ~ * {\n      margin-left: 0rem;\n      margin-right: 0rem;\n    }\n  }\n}\n\n/**************************************************************\n * Similar content blocks that are not technically admonitions.\n */\n\n/**\n * Topics and the {contents} directive\n */\n// Docutils <= 0.17\ndiv.topic,\ndiv.topic.contents,\n// Docutils >= 0.18\nnav.contents,\naside.topic {\n  display: flex;\n  flex-direction: column;\n  background-color: var(--pst-color-surface);\n  border-color: var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  padding: 1rem 1.25rem;\n  @include box-shadow();\n\n  .topic-title {\n    margin: 0 0 0.5rem 0;\n  }\n\n  // Over-ride text color to ensure enough contrast\n  p {\n    color: var(--pst-color-on-surface) !important;\n  }\n\n  // Over-ride large default padding\n  ul.simple {\n    padding-left: 1rem;\n\n    ul {\n      // So that sub-lists will have a bit less padding\n      padding-left: 2em;\n    }\n  }\n}\n\n/**\n * Sidebar directive\n */\naside.sidebar {\n  border: 1px solid var(--pst-color-border);\n  background-color: var(--pst-color-surface);\n  border-radius: $admonition-border-radius;\n  // to match the admonition-styled sidebars:\n  margin-left: 0.5rem;\n  padding: 0;\n  > *:last-child {\n    padding-bottom: 1rem;\n  }\n\n  p.sidebar-title {\n    position: relative;\n    margin-bottom: 0;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n    border-bottom: 1px solid var(--pst-color-border);\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-admonition-font-weight-heading);\n  }\n\n  // Add margin to the first non-`.sidebar-title` item\n  > *:not(.sidebar-title):first-child,\n  > p.sidebar-title + * {\n    margin-top: 1rem;\n  }\n\n  > * {\n    padding-left: 1rem;\n    padding-right: 1rem;\n  }\n}\n\n/**\n * Rubrics look kind of like section headers\n */\np.rubric {\n  display: flex;\n  flex-direction: column;\n}\n\n/**\n * Seealso is kind of like a vertically-collapsed admonition\n */\n.seealso dd {\n  margin-top: 0;\n  margin-bottom: 0;\n}\n","/**\n * Miscellaneous color functions and mixins\n**/\n\n// loading the math module\n@use \"sass:math\";\n\n// Set background color from a color variable\n//\n@mixin background-from-color-variable($color-variable) {\n  &:before {\n    content: \"\";\n    width: 100%;\n    height: 100%;\n    position: absolute;\n    left: 0;\n    top: 0;\n    background-color: var(#{$color-variable});\n    z-index: -1;\n    // So that hovering over the text within background is still possible.\n    // Otherwise the background overlays the text and you cannot click or select easily.\n    // ref: https://developer.mozilla.org/en-US/docs/Web/CSS/pointer-events\n    pointer-events: none;\n  }\n}\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetc\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map-get($map, $key);\n  }\n  @return $map;\n}\n\n/**\n  * Function to check if the color needs converting to a \"color\" type\n  * if it is a string we cannot use other color manipulation functions\n  * It is used to create the sphinx-design colours as these are often interpolated\n  */\n// @param {String/Color} $color - Color definition from map\n// @return {Color} - Color type (in hex)\n@function check-color($color) {\n  @if type-of($color) == string {\n    $color: from-hex($color);\n  }\n  @return $color;\n}\n\n/**\n  * Function to convert the string representation of a color to a color type (hex)\n  */\n// @param {String} $string - String representation of a color\n@function from-hex($string) {\n  $string-lower: to-lower-case($string);\n  $r: \"\";\n  $g: \"\";\n  $b: \"\";\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $length: str-length($string);\n  $max: if($length == 4, 1, 2);\n\n  // Check for length accuracy\n  @if $length != 4 and $length != 7 {\n    @return $string;\n  }\n\n  // Loop from the second character (omitting #)\n  @for $i from 2 through $length {\n    $c: str-slice($string-lower, $i, $i);\n\n    // If wrong character, return\n    @if index($hex, $c) == null {\n      @return $string;\n    }\n\n    @if str-length($r) < $max {\n      $r: $r + $c;\n    } @else if str-length($g) < $max {\n      $g: $g + $c;\n    } @else if str-length($b) < $max {\n      $b: $b + $c;\n    }\n  }\n\n  @if $length == 4 {\n    $r: $r + $r;\n    $g: $g + $g;\n    $b: $b + $b;\n  }\n\n  @return rgb(_hex-to-dec($r), _hex-to-dec($g), _hex-to-dec($b));\n}\n\n@function _hex-to-dec($string) {\n  $hex: \"0\" \"1\" \"2\" \"3\" \"4\" \"5\" \"6\" \"7\" \"8\" \"9\" \"a\" \"b\" \"c\" \"d\" \"e\" \"f\";\n  $string: to-lower-case($string);\n  $length: str-length($string);\n\n  $dec: 0;\n  @for $i from 1 through $length {\n    $factor: 1 + (15 * ($length - $i));\n    $index: index($hex, str-slice($string, $i, $i));\n    $dec: $dec + $factor * ($index - 1);\n  }\n\n  @return $dec;\n}\n","// Style API docs from sphinx' autodoc / autosummary\n\n/*******************************************************************************\n* Styling for field lists\n*/\n\n/* grey highlighting of 'parameter' and 'returns' field */\ntable.field-list {\n  border-collapse: separate;\n  border-spacing: 10px;\n  margin-left: 1px;\n\n  th.field-name {\n    padding: 1px 8px 1px 5px;\n    white-space: nowrap;\n    background-color: var(--pst-color-surface);\n  }\n\n  /* italic font for parameter types */\n  td.field-body {\n    p {\n      font-style: italic;\n\n      > strong {\n        font-style: normal;\n      }\n    }\n\n    /* reduced space around parameter description */\n    blockquote {\n      border-left: none;\n      margin: 0em 0em 0.3em;\n      padding-left: 30px;\n    }\n  }\n}\n\n/*******************************************************************************\n* Styling for autosummary tables\n*/\n\n.table.autosummary {\n  // The first column (with the signature) should not wrap\n  td:first-child {\n    white-space: nowrap;\n  }\n}\n\n/* overriding basic.css to use our own monospace font */\n.sig {\n  font-family: var(--pst-font-family-monospace);\n}\n\n/* C++ specific styling - overriding the basic.css to avoid custom colors*/\n\n.sig-inline.c-texpr,\n.sig-inline.cpp-texpr {\n  font-family: unset;\n}\n\n.sig.c .k,\n.sig.c .kt,\n.sig.cpp .k,\n.sig.cpp .kt {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .m,\n.sig.cpp .m {\n  color: var(--pst-color-text-base);\n}\n\n.sig.c .s,\n.sig.c .sc,\n.sig.cpp .s,\n.sig.cpp .sc {\n  color: var(--pst-color-text-base);\n}\n\n// addition\n\n// .sig.c .sig-name .n,\n// .sig.cpp .sig-name .n {\n//   color: var(--pst-color-inline-code);\n// }\n\n.sig-name {\n  color: var(--pst-color-inline-code);\n}\n\n.sig-param .o,\n.sig-param .default_value {\n  color: var(--pst-color-text-muted);\n  font-weight: normal;\n}\n\n// change target color for dark theme\ndt:target,\nspan.highlighted {\n  background-color: var(--pst-color-target);\n}\n\n.viewcode-back {\n  font-family: var(--pst-font-family-base);\n}\n\n.viewcode-block:target {\n  border-top: 1px solid var(--pst-color-border);\n  border-bottom: 1px solid var(--pst-color-border);\n  position: relative;\n  background-color: var(--pst-color-target);\n}\n\n/*******************************************************************************\n* Styling for autosummary titles like \"parameters\" and \"returns\"\n*/\n\n// the API selector\n// from https://github.com/pradyunsg/furo/blob/main/src/furo/assets/styles/content/_api.sass#L6)\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) {\n  dd {\n    margin-left: 2rem;\n\n    // Fix until this will be solved to Sphinx https://github.com/sphinx-doc/sphinx/issues/10815\n    & > dl.simple > dt {\n      display: flex;\n    }\n  }\n\n  dl.field-list {\n    display: grid;\n    grid-template-columns: unset;\n  }\n\n  dt.field-odd,\n  dt.field-even {\n    margin-top: 0.2rem;\n    margin-bottom: 0.1rem;\n    background-color: var(--pst-color-surface);\n  }\n}\n","/**\n * Code block styling\n * Note that we inherit a lot of styling from Bootstrap so not many rules here.\n */\n\n// General code block behavior\n// Unset bootstrap behavior\ndiv[class*=\"highlight-\"],\ndiv.highlight,\ndiv.literal-block-wrapper {\n  display: flex;\n  flex-direction: column;\n  width: unset;\n  border-radius: $admonition-border-radius;\n}\n\n// Code blocks with captions\n// There's a wrapper when the code block has a title\ndiv.literal-block-wrapper {\n  border: 1px solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n\n  // This is where the title goes\n  div.code-block-caption {\n    margin: 0;\n    border-bottom: 1px solid var(--pst-color-border);\n    padding: 0.5rem;\n    font-size: 1rem;\n    font-weight: var(--pst-font-weight-caption);\n\n    a.headerlink {\n      font-size: inherit;\n    }\n  }\n\n  // Remove the upper border radius since we want it to connect with the title\n  // Remove the box shadow so the wrapper gets the shadow instead\n  div[class*=\"highlight-\"] {\n    margin: 0;\n    border-radius: 0;\n    pre {\n      border: none;\n      box-shadow: none;\n    }\n  }\n}\n\n/**\n * In-line code\n */\ncode.literal {\n  padding: 0.1rem 0.25rem;\n  background-color: var(--pst-color-surface);\n  border: 1px solid var(--pst-color-border);\n  border-radius: 0.25rem;\n}\n\na > code {\n  color: var(--pst-color-inline-code-links);\n}\n\n// Fix for Sphinx's \"highlight\" directive - this is an issue with our accessible pygments theme\n// and the colour we are using for the background of the code blocks.\nhtml[data-theme=\"light\"] .highlight .nf {\n  color: #0078a1 !important;\n}\n\n// Minimum opacity needed for linenos to be WCAG AA conformant\nspan.linenos {\n  opacity: 0.8 !important;\n}\n","figure {\n  a.headerlink {\n    // So that header link doesn't push caption to be off-center.\n    position: absolute;\n    font-size: inherit;\n  }\n  // Default headerlink hover doesn't trigger on figures\n  &:hover a.headerlink {\n    visibility: visible;\n  }\n\n  figcaption {\n    font-family: var(--pst-font-family-heading);\n    font-weight: var(--pst-font-weight-caption);\n    color: var(--pst-color-text-muted);\n    margin-left: auto;\n    margin-right: auto;\n\n    table.table {\n      width: fit-content;\n      margin-left: auto;\n      margin-right: auto;\n    }\n  }\n}\n","// For consistency, add bracket around footnotes/citations which are\n// cited more than once. E.g. [Newton](1,2) instead of Newton(1,2)\ndt.label > span.brackets:not(:only-child):before {\n  content: \"[\";\n}\n\ndt.label > span.brackets:not(:only-child):after {\n  content: \"]\";\n}\n\n// Make footnote as a superscript\na.footnote-reference {\n  vertical-align: super;\n  font-size: small;\n}\n\n// Docutils 0.18 uses an `aside.footnote` container with different internal structure\naside.footnote {\n  margin-bottom: 0.5rem;\n  &:last-child {\n    margin-bottom: 1rem;\n  }\n  span.label,\n  span.backrefs {\n    font-weight: bold;\n  }\n\n  &:target {\n    background-color: var(--pst-color-target);\n  }\n}\n","/**\n * Hacky fixes that don't fit cleanly into other sections\n */\n\n// Ensure user highlighting/selecting behaves properly\n// From https://stackoverflow.com/a/34372191\ntable.highlighttable td.linenos,\nspan.linenos,\ndiv.doctest > div.highlight span.gp {\n  /* gp: Generic.Prompt */\n  user-select: none;\n  -webkit-user-select: text; /* Safari fallback only */\n  -webkit-user-select: none; /* Chrome/Safari */\n  -moz-user-select: none; /* Firefox */\n  -ms-user-select: none; /* IE10+ */\n}\n","// Override bootstrap by restoring the basic theme default.\ndd {\n  margin-top: 3px;\n  margin-bottom: 10px;\n  margin-left: 30px;\n}\n\nol,\nul {\n  padding-inline-start: 2rem;\n  li > p:first-child {\n    margin-bottom: 0.25rem;\n    margin-top: 0.25rem;\n  }\n}\n","// GitHub blockquote style\nblockquote {\n  padding: 1em 1em;\n  color: var(--pst-color-text-muted);\n  border-left: 0.25em solid var(--pst-color-border);\n  border-radius: $admonition-border-radius;\n  position: relative;\n\n  p {\n    color: var(--pst-color-text-base);\n  }\n\n  // remove padding from included line-block to avoid duplication\n  .line-block {\n    margin: 0 0;\n  }\n\n  // remove margin bottom for the last p\n  p:last-child {\n    margin-bottom: 0;\n  }\n\n  @include background-from-color-variable(--pst-color-on-background);\n\n  //hack to make the text in the blockquote selectable\n  &:before {\n    z-index: -1;\n  }\n}\n","/**\n * Span-level styling within content\n */\n\nspan.guilabel {\n  border: 1px solid var(--pst-color-info);\n  color: var(--pst-color-info);\n  font-size: 80%;\n  font-weight: 700;\n  border-radius: 4px;\n  padding: 2.4px 6px;\n  margin: auto 2px;\n  position: relative;\n\n  @include background-from-color-variable(--pst-color-info-bg);\n}\n\na.reference.download:before {\n  content: var(--pst-icon-download);\n  font: var(--fa-font-solid);\n  font-size: 0.8em;\n  padding: 0 0.25em;\n  color: var(--pst-color-text-muted);\n}\n","/**\n * Tables\n */\n\n// ensure table will fit in the article width and make them y-scrollable\ntable {\n  display: table;\n  overflow: auto;\n\n  // default to table-center\n  margin-left: auto;\n  margin-right: auto;\n\n  &.table-right {\n    margin-right: 0;\n  }\n\n  &.table-left {\n    margin-left: 0;\n  }\n}\n\n// customize table caption from bootstrap\n// to display them on top and centered\ntable caption {\n  text-align: center;\n  caption-side: top;\n  color: var(--pst-color-text-muted);\n}\n\n// MyST Markdown tables use these classes to control alignment\nth,\ntd {\n  &.text-align\\:left {\n    text-align: left;\n  }\n\n  &.text-align\\:right {\n    text-align: right;\n  }\n\n  &.text-align\\:center {\n    text-align: center;\n  }\n}\n\n// override bootstrap background colors\n.table {\n  --bs-table-bg: transparent;\n}\n","/**\n * Style the toctree component in pages (avoid modifying the navbars)\n */\n.toctree-wrapper {\n  p.caption {\n    font-size: 1.5em;\n    margin-bottom: 0em;\n  }\n\n  & > ul {\n    padding-left: 0;\n  }\n\n  li[class^=\"toctree-l\"] {\n    list-style: none;\n    margin-bottom: 0.2em;\n\n    & > a {\n      list-style: none;\n      font-size: 1.1em;\n    }\n\n    & > ul {\n      list-style: none;\n      padding-inline-start: 1.5em;\n    }\n  }\n\n  // slightly bigger font for l1\n  .toctree-l1 > a {\n    font-size: 1.3em;\n  }\n}\n\ndiv.topic.contents,  // Docutils <= 0.17\nnav.contents  // Docutils >= 0.18\n{\n  // Style similarly to toctree\n  ul.simple {\n    list-style: none;\n    padding-left: 0;\n  }\n}\n","/**\n * Mathematics via MathJax.\n *\n * This is designed for MathJax v3\n * ref: https://www.sphinx-doc.org/en/master/usage/extensions/math.html#module-sphinx.ext.mathjax\n */\n\n// Applies to all math elements\nspan.math,\ndiv.math {\n  align-items: center;\n  display: flex;\n  max-width: 100%;\n  // This will be over-ridden for the y-direction and divs\n  overflow: hidden;\n}\n\n// Inline-only\nspan.math {\n  display: inline-flex;\n}\n\n// Block-level only\ndiv.math {\n  gap: 0.5em;\n  // So that the eqno shows up after the equation\n  flex-direction: row-reverse;\n\n  // The equation number / link\n  span.eqno a.headerlink {\n    position: relative;\n    font-size: 1em;\n  }\n\n  // The math container\n  mjx-container {\n    flex-grow: 1;\n    padding-bottom: 0.2rem;\n    overflow: auto;\n\n    // Set height to 0 so that it does not cause scrollbars to appear\n    // ref: https://github.com/mathjax/MathJax/issues/2521\n    mjx-assistive-mml {\n      height: 0;\n    }\n  }\n}\n","/**\n * ABlog\n * ref: https://ablog.readthedocs.io/\n */\n\n/**\n * Sidebar template components\n */\n.ablog-sidebar-item {\n  h2,\n  h3 {\n    font-size: var(--pst-sidebar-header-font-size);\n    // Remove unnecessary vertical whitespace\n    margin-top: 0.5rem;\n\n    // The headers are all links, but this makes them hard to parse\n    // So we change the colors to make them look like headers\n    a {\n      color: var(--pst-color-text-base);\n    }\n  }\n\n  ul {\n    // No bullet points for the primary sidebar items\n    list-style: none;\n    padding-left: 0;\n    // Otherwise a scrollbar randomly shows up\n    overflow-y: hidden;\n    // List of recent post items\n    display: flex;\n    flex-direction: column;\n    gap: 0.5em;\n    margin-bottom: 0;\n\n    // The ablog cloud should move horizontally\n    &.ablog-cloud {\n      flex-direction: row;\n      flex-flow: wrap;\n      gap: 0.5rem;\n\n      // Vertical-align tag clouds\n      li {\n        // Center the tag cloud items\n        display: flex;\n        align-items: center;\n      }\n    }\n  }\n}\n\n/**\n * Previous / next buttons at the bottom\n */\n.ablog__prev-next {\n  font-size: 1.2em;\n  display: flex;\n  padding: 1rem 0;\n\n  // The bottom previous / next arrows\n  > span {\n    // To ensure that the whole thing fits on one line even if there are long titles\n    display: flex;\n    max-width: 45%;\n\n    // Links within each span have the collection of icon + text\n    a {\n      display: flex;\n      align-items: center;\n      margin-left: auto;\n      gap: 1rem;\n      line-height: 1.5rem;\n\n      i:before {\n        color: var(--pst-color-text-base);\n      }\n    }\n  }\n\n  // The first span is for the previous page and aligns to the left\n  span.ablog__prev {\n    i.fa-arrow-circle-left:before {\n      content: var(--pst-icon-angle-left);\n    }\n  }\n\n  // The second span is just an empty space so we remove it because we're\n  // positioning with flex\n  span.ablog__spacer {\n    display: none;\n  }\n\n  // The third span is aligned to the right\n  span.ablog__next {\n    margin-left: auto;\n    text-align: right;\n\n    i.fa-arrow-circle-right:before {\n      content: var(--pst-icon-angle-right);\n    }\n  }\n}\n\n/**\n * {postlist} directive and posts page\n */\n.ablog__collection,\n.postlist {\n  padding-left: 0;\n\n  .ablog-post {\n    list-style: none;\n\n    // Post metadata tags (author, links ,etc) should be a bit smaller\n    .ablog-archive {\n      display: flex;\n      flex-direction: row;\n      flex-wrap: wrap;\n      gap: 1rem;\n      list-style: none;\n      font-size: 0.75rem;\n      padding-left: 0;\n    }\n\n    // Title line should be a bit bigger and bold to stand out\n    .ablog-post-title {\n      margin-top: 0;\n      font-size: 1.25rem;\n\n      a {\n        font-weight: bold;\n      }\n    }\n\n    // Read more button should be a bit bigger\n    .ablog-post-expand {\n      margin-bottom: 0.5rem;\n    }\n  }\n}\n","/**\n * Special cases for Bootstrap functionality\n */\n\n// Bootstrap adds margin to their general container class. However, sphinx/docutils\n// can also generate output with the container class, but in those cases we should\n// not add the margin from bootstrap. Same for max-width.\n.docutils.container {\n  padding-left: unset;\n  padding-right: unset;\n  margin-left: unset;\n  margin-right: unset;\n  max-width: unset;\n  width: unset;\n}\n","/**\n * Sphinx Copybutton\n * ref: https://sphinx-copybutton.readthedocs.io/\n */\n\ndiv.highlight button.copybtn {\n  // Nicer spacing\n  display: flex;\n  align-items: center;\n  justify-content: center;\n\n  // Removes the button background so we more naturally blend with the code cell.\n  background-color: unset;\n\n  // Don't over-ride the success color\n  &:not(.success) {\n    color: var(--pst-color-muted);\n  }\n  border: none;\n  background-color: var(--pst-color-surface);\n\n  &:hover {\n    &:not(.success) {\n      color: var(--pst-color-text);\n      background-color: var(--pst-color-shadow);\n    }\n  }\n\n  // Tooltip styling\n  &.o-tooltip--left:after {\n    color: var(--pst-color-text);\n    background-color: var(--pst-color-surface);\n  }\n}\n","// adapt ethical ad to the theme\n#ethical-ad-placement {\n  .ethical-sidebar a,\n  .ethical-sidebar a:visited,\n  .ethical-sidebar a:hover,\n  .ethical-sidebar a:active,\n  .ethical-footer a,\n  .ethical-footer a:visited,\n  .ethical-footer a:hover,\n  .ethical-footer a:active {\n    color: var(--pst-color-text-base);\n  }\n\n  .ethical-sidebar,\n  .ethical-footer {\n    background-color: var(--pst-color-background);\n    border: 1px solid var(--pst-color-border);\n    border-radius: 5px;\n    color: var(--pst-color-text-base);\n    font-size: 14px;\n    line-height: 20px;\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/******************************************************************************\n * Jupyter Sphinx\n */\n\n.bd-content div.jupyter_container {\n  // We don't want borders around the whole container, just around code cells\n  border: none;\n  background-color: unset;\n  box-shadow: none;\n\n  // Code cells should have the same style as our other code objects\n  div.output,\n  div.highlight {\n    border-radius: 0.25rem;\n  }\n  div.highlight {\n    background-color: var(--pst-color-surface);\n  }\n\n  // Ensure the style is the same as our code cells. Jupyter Sphinx makes it tiny.\n  .cell_input,\n  .cell_output {\n    border-radius: 0.25rem;\n    pre {\n      padding: 1rem;\n    }\n  }\n}\n","/**\n * Special-cases for packages in the PyData ecosystem\n */\n\n// xarray output display in bootstrap\n.xr-wrap[hidden] {\n  display: block !important;\n}\n","/*******************************************************************************\n* master color map. Only the colors that actually differ between light and dark\n* themes are specified separately.\n*\n* To see the full list of colors see https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1234%3A765&t=ifcFT1JtnrSshGfi-1\n*/\n\n/**\n* Function to get items from nested maps\n*/\n// @param {Map} $map - Map\n// @param {Arglist} $keys - Keys to fetch\n// @return {*}\n@function map-deep-get($map, $keys...) {\n  @each $key in $keys {\n    $map: map-get($map, $key);\n  }\n  @return $map;\n}\n\n/* Assign base colors for the PyData theme */\n$color-palette: (\n  // Primary color\n  \"teal\":\n    (\n      \"50\": #f4fbfc,\n      \"100\": #e9f6f8,\n      \"200\": #d0ecf1,\n      \"300\": #abdde6,\n      \"400\": #3fb1c5,\n      \"500\": #0a7d91,\n      \"600\": #085d6c,\n      \"700\": #064752,\n      \"800\": #042c33,\n      \"900\": #021b1f,\n    ),\n  // Secondary color\n  \"violet\":\n    (\n      \"50\": #f4eefb,\n      \"100\": #e0c7ff,\n      \"200\": #d5b4fd,\n      \"300\": #b780ff,\n      \"400\": #9c5ffd,\n      \"500\": #8045e5,\n      \"600\": #6432bd,\n      \"700\": #4b258f,\n      \"800\": #341a61,\n      \"900\": #1e0e39,\n    ),\n  // Neutrals\n  \"gray\":\n    (\n      \"50\": #f9f9fa,\n      \"100\": #f3f4f5,\n      \"200\": #e5e7ea,\n      \"300\": #d1d5da,\n      \"400\": #9ca4af,\n      \"500\": #677384,\n      \"600\": #48566b,\n      \"700\": #29313d,\n      \"800\": #222832,\n      \"900\": #14181e,\n    ),\n  // Accent color\n  \"pink\":\n    (\n      \"50\": #fcf8fd,\n      \"100\": #fcf0fa,\n      \"200\": #f8dff5,\n      \"300\": #f3c7ee,\n      \"400\": #e47fd7,\n      \"500\": #c132af,\n      \"600\": #912583,\n      \"700\": #6e1c64,\n      \"800\": #46123f,\n      \"900\": #2b0b27,\n    ),\n  \"foundation\": (\n    \"white\": #ffffff,\n    // gray-900\n    \"black\": #14181e,\n  )\n);\n\n:root {\n  // Add theme colours to the html root element\n  @each $group-color, $color in $color-palette {\n    @each $color-name, $definition in $color {\n      --pst-#{$group-color}-#{$color-name}: #{$definition};\n    }\n  }\n}\n\n// Static SCSS variables used thoroughout the theme\n// Minimum contrast ratio used for the theme.\n// Acceptable values for WCAG 2.0 are 3, 4.5 and 7.\n// See https://www.w3.org/TR/WCAG20/#visual-audio-contrast-contrast\n// 4.5 - is for text that is 14pt or less\n$min-contrast-ratio-4: 4.5;\n// 3 is for text that is 18pt or bold, or for non-text elements\n$min-contrast-ratio-3: 3;\n\n// Customize the light and dark text colors for use in our color contrast function.\n$foundation-black: #14181e;\n$foundation-white: #ffffff;\n// This is a custom - calculated  color between gray 100 and 200 - to reduce\n// the contrast ratio (avoid a jarring effect)\n$base-light-text: #ced6dd;\n// used in sphinx_design - gray 100\n$foundation-light-gray: #f3f4f5;\n// used in sphinx_design - gray 700\n$foundation-muted-gray: #29313d;\n// used in sphinx_design - gray 800\n$foundation-dark-gray: #222832;\n\n$pst-semantic-colors: (\n  \"primary\": (\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"teal\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"teal\", \"800\")},\n  ),\n  \"secondary\": (\n    \"light\": #{map-deep-get($color-palette, \"violet\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"violet\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"violet\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"violet\", \"800\")},\n  ),\n  \"accent\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"500\")},\n    \"bg-light\": #{map-deep-get($color-palette, \"pink\", \"200\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"400\")},\n    \"bg-dark\": #{map-deep-get($color-palette, \"pink\", \"800\")},\n  ),\n  \"info\": (\n    \"light\": #276be9,\n    \"bg-light\": #dce7fc,\n    \"dark\": #79a3f2,\n    \"bg-dark\": #06245d,\n  ),\n  \"warning\": (\n    \"light\": #f66a0a,\n    \"bg-light\": #f8e3d0,\n    \"dark\": #ff9245,\n    \"bg-dark\": #652a02,\n  ),\n  \"success\": (\n    \"light\": #00843f,\n    \"bg-light\": #d6ece1,\n    \"dark\": #5fb488,\n    \"bg-dark\": #002f17,\n  ),\n  // This is is based on the warning color\n  \"attention\":\n    (\n      \"light\": var(--pst-color-warning),\n      \"bg-light\": var(--pst-color-warning-bg),\n      \"dark\": var(--pst-color-warning),\n      \"bg-dark\": var(--pst-color-warning-bg),\n    ),\n  \"danger\": (\n    \"light\": #d72d47,\n    \"bg-light\": #f9e1e4,\n    \"dark\": #e78894,\n    \"bg-dark\": #4e111b,\n  ),\n  \"text-base\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    \"dark\": $base-light-text,\n  ),\n  \"text-muted\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"400\")},\n  ),\n  \"heading-color\": (\n    \"light\": #{$foundation-white},\n    \"dark\": #{$foundation-black},\n  ),\n  \"shadow\": (\n    \"light\": rgba(0, 0, 0, 0.1),\n    \"dark\": rgba(0, 0, 0, 0.2),\n  ),\n  \"border\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"300\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"600\")},\n  ),\n  \"border-muted\": (\n    \"light\": rgba(23, 23, 26, 0.2),\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  \"inline-code\": (\n    \"light\": #{map-deep-get($color-palette, \"pink\", \"600\")},\n    \"dark\": #{map-deep-get($color-palette, \"pink\", \"300\")},\n  ),\n  \"inline-code-links\": (\n    // need to make sure there is enough contrast against the code bg\n    \"light\": #{map-deep-get($color-palette, \"teal\", \"600\")},\n    // keep primary color for dark mode\n    \"dark\": #{map-deep-get($color-palette, \"teal\", \"400\")},\n  ),\n  \"target\": (\n    \"light\": #f3cf95,\n    \"dark\": #675c04,\n  ),\n  // DEPTH COLORS - you can see the elevation colours and shades\n  // in the Figma file https://www.figma.com/file/rUrrHGhUBBIAAjQ82x6pz9/PyData-Design-system---proposal-for-implementation-(2)?node-id=1492%3A922&t=sQeQZehkOzposYEg-1\n  // background: color of the canvas / the furthest back layer\n  \"background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"foundation\", \"black\")},\n    ),\n  // on-background: provides slight contrast against background\n  // (by use of shadows in light theme)\n  \"on-background\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"foundation\", \"white\")},\n      \"dark\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n    ),\n  \"surface\": (\n    \"light\": #{map-deep-get($color-palette, \"gray\", \"100\")},\n    \"dark\": #{map-deep-get($color-palette, \"gray\", \"700\")},\n  ),\n  // on_surface: object on top of surface object (without shadows)\n  \"on-surface\":\n    (\n      \"light\": #{map-deep-get($color-palette, \"gray\", \"800\")},\n      \"dark\": $foundation-light-gray,\n    ),\n);\n\n/*******************************************************************************\n* write the color rules for each theme (light/dark)\n*/\n\n/* NOTE:\n * Mixins enable us to reuse the same definitions for the different modes\n * https://sass-lang.com/documentation/at-rules/mixin\n * #{something} inserts a variable into a CSS selector or property name\n * https://sass-lang.com/documentation/interpolation\n */\n@mixin theme-colors($mode) {\n  // check if this color is defined differently for light/dark\n  @each $col-name, $definition in $pst-semantic-colors {\n    @if type-of($definition) == map {\n      @each $key, $val in $definition {\n        @if str-index($key, $mode) != null {\n          // since now we define the bg colours in the semantic colours and not\n          // by changing opacity, we need to check if the key contains bg and the\n          // correct mode (light/dark)\n          @if str-index($key, \"bg\") != null {\n            --pst-color-#{$col-name}-bg: #{$val};\n          } @else {\n            --pst-color-#{$col-name}: #{$val};\n          }\n        }\n      }\n    } @else {\n      --pst-color-#{$col-name}: #{$definition};\n    }\n  }\n\n  // assign the \"duplicate\" colors (ones that just reference other variables)\n  & {\n    --pst-color-link: var(--pst-color-primary);\n    --pst-color-link-hover: var(--pst-color-secondary);\n  }\n  // adapt to light/dark-specific content\n  @if $mode == \"light\" {\n    .only-dark,\n    .only-dark ~ figcaption {\n      display: none !important;\n    }\n  } @else {\n    .only-light,\n    .only-light ~ figcaption {\n      display: none !important;\n    }\n    /* Adjust images in dark mode (unless they have class .only-dark or\n     * .dark-light, in which case assume they're already optimized for dark\n     * mode).\n     */\n    img:not(.only-dark):not(.dark-light) {\n      filter: brightness(0.8) contrast(1.2);\n    }\n    /* Give images a light background in dark mode in case they have\n    *  transparency and black text (unless they have class .only-dark or .dark-light, in\n    *  which case assume they're already optimized for dark mode).\n    */\n    .bd-content img:not(.only-dark):not(.dark-light) {\n      background: rgb(255, 255, 255);\n      border-radius: 0.25rem;\n    }\n    // MathJax SVG outputs should be filled to same color as text.\n    .MathJax_SVG * {\n      fill: var(--pst-color-text-base);\n    }\n  }\n}\n\n/* Defaults to light mode if data-theme is not set */\nhtml:not([data-theme]) {\n  @include theme-colors(\"light\");\n}\n\n/* NOTE: @each {...} is like a for-loop\n * https://sass-lang.com/documentation/at-rules/control/each\n */\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    @include theme-colors($mode);\n    color-scheme: $mode;\n  }\n}\n\n// assign classes too, for runtime use of theme colors\n@each $col-name, $definition in $pst-semantic-colors {\n  .pst-color-#{$col-name} {\n    color: var(--pst-color-#{$col-name});\n  }\n}\n","/*******************************************************************************\n * Special-cases for the sphinx-design library, mainly to make it compatible\n * with the dark/light themes of pydata-sphinx-theme.\n *\n * NOTE: sphinx-design uses !important quite liberally, so here we must do the\n * same for our overrides to have any effect.\n */\n@use \"../variables/color\" as pst-color;\n@use \"sass:meta\";\n@use \"sass:color\";\n\n/*******************************************************************************\n  * Color and variables\n  *\n  * This is a list of the semantic color names from sphinx-design (we only\n  * need to override variables that sphinx-design has actually defined).\n  * https://github.com/executablebooks/sphinx-design/blob/9226a12a/style/_colors.scss#L31-L43\n  */\n$sd-semantic-color-names: (\n  \"primary\",\n  \"secondary\",\n  \"success\",\n  \"info\",\n  \"warning\",\n  \"danger\",\n  \"light\",\n  \"muted\",\n  \"dark\",\n  \"black\",\n  \"white\"\n);\n\n/**\n  * Here we create some extra --pst-color-* variables and use\n  * them to override the value of the corresponding sphinx-design variables.\n  * This is easier than re-writing the sphinx-design rules. Even easier would be\n  * directly assigning our values to the --sd-color-* variables, but then our\n  * downstream users couldn't override *our* colors and have it affect buttons\n  * and badges.\n  *\n  * First, define the extra keys needed to cover the full range of semantic\n  * color names used in sphinx-design, then merge them with the names we\n  * already define for our own needs.\n  * see https://sphinx-design.readthedocs.io/en/latest/css_variables.html\n  */\n$extra-semantic-colors: (\n  \"white\": $foundation-white,\n  \"light\": (\n    light: $foundation-light-gray,\n    bg-light: color.scale($foundation-light-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-light-gray, $lightness: -30%),\n  ),\n  \"muted\": (\n    light: $foundation-muted-gray,\n    bg-light: color.scale($foundation-muted-gray, $lightness: 30%),\n    dark: $foundation-light-gray,\n    bg-dark: color.scale($foundation-muted-gray, $lightness: -30%),\n  ),\n  \"dark\": $foundation-dark-gray,\n  \"black\": $foundation-black,\n);\n\n$all-colors: map-merge($pst-semantic-colors, $extra-semantic-colors);\n\n@mixin create-sd-colors($value, $name) {\n  // define the pst variables, so that downstream user overrides will work\n  --pst-color-#{$name}: #{$value};\n  // we are now using a11y-combination to calculate the text color - this is based\n  // on the WCAG color contrast guidelines\n  --pst-color-#{$name}-text: #{a11y-combination($value)};\n  //TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --pst-color-#{$name}-highlight: #{darken($value, 15%)};\n  // override the sphinx-design variables\n  --sd-color-#{$name}: var(--pst-color-#{$name});\n  --sd-color-#{$name}-text: var(--pst-color-#{$name}-text);\n  //TODO: highlight seems to be used for buttons @trallard to fix on a11y follow-up work\n  --sd-color-#{$name}-highlight: var(--pst-color-#{$name}-highlight);\n}\n\n// Now we override the --sd-color-* variables.\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    // check if this color is defined differently for light/dark\n    @each $name in $sd-semantic-color-names {\n      $definition: map-get($all-colors, $name);\n      @if type-of($definition) == map {\n        @each $key, $value in $definition {\n          @if str-index($key, $mode) != null {\n            // since now we define the bg colours in the semantic colours and not\n            // by changing opacity, we need to check if the key contains bg and the\n            // correct mode (light/dark)\n            @if str-index($key, \"bg\") != null {\n              --sd-color-#{$name}-bg: #{$value};\n              // create local variable\n              $bg-var: --sd-color-#{$name}-bg;\n              $value: check-color($value);\n              --sd-color-#{$name}-bg-text: #{a11y-combination($value)};\n            } @else {\n              $value: check-color($value);\n              @include create-sd-colors($value, $name);\n            }\n          }\n        }\n      } @else {\n        $value: map-get($all-colors, $name);\n        @include create-sd-colors($value, $name);\n      }\n    }\n  }\n}\n\n// Make sure the color border variables are set using our variables\n@each $mode in (light, dark) {\n  html[data-theme=\"#{$mode}\"] {\n    --sd-color-card-border: var(--pst-color-border);\n  }\n}\n\n/*******************************************************************************\n  * shadows\n  */\nhtml[data-theme=\"light\"] {\n  .sd-shadow-xs,\n  .sd-shadow-sm,\n  .sd-shadow-md,\n  .sd-shadow-lg {\n    @include box-shadow();\n  }\n}\n\n/*******************************************************************************\n  * cards\n  */\n\n.bd-content .sd-card {\n  border: 1px solid var(--pst-color-border);\n\n  // TODO - --pst-color-panel-background is not defined... where is this coming from?\n  .sd-card-header {\n    background-color: var(--pst-color-panel-background);\n    border-bottom: 1px solid var(--pst-color-border);\n  }\n  .sd-card-footer {\n    background-color: var(--pst-color-panel-background);\n    border-top: 1px solid var(--pst-color-border);\n  }\n\n  .sd-card-body {\n    background-color: var(--pst-color-panel-background);\n  }\n}\n/*******************************************************************************\n  * tabs\n  */\n\n.bd-content .sd-tab-set {\n  > input {\n    // Active tab label\n    &:checked + label {\n      border-color: transparent transparent var(--pst-color-primary); // top LR bottom\n      color: var(--pst-color-primary);\n    }\n\n    // Hover label\n    &:not(:checked) + label:hover {\n      border-color: var(--pst-color-secondary);\n      color: var(--pst-color-secondary);\n    }\n  }\n\n  // Tab label\n  > label {\n    color: var(--pst-color-text-muted);\n    border-top: 0.125rem solid transparent; // so hover isn't just color change\n    padding-top: 0.5em; // same as bottom padding, so hover overline looks OK\n    // Hovered label\n    html &:hover {\n      color: var(--pst-color-secondary);\n      border-color: var(--pst-color-secondary);\n    }\n  }\n}\n\n/*******************************************************************************\n* Dropdowns\n*/\n\ndetails.sd-dropdown {\n  // Remove all borders to over-ride SD behavior, and we'll add our own later\n  border: 0px !important;\n  summary.sd-card-header {\n    border: 0 !important;\n    & + div.sd-summary-content {\n      border: 0;\n    }\n  }\n  // Drop shadow should behave same as admonitions\n  @include box-shadow();\n\n  // Header is where the \"clickable\" box goes\n  summary.sd-card-header {\n    display: flex;\n    align-items: center;\n    position: relative; // So background color works\n    font-weight: 600;\n    padding-top: 0.5rem;\n    padding-bottom: 0.5rem;\n\n    // Set a variable that we can re-use for colors later\n    // We must set this in the current and content sibling container\n    // so that it is defined in both places\n    --pst-sd-dropdown-color: var(--pst-gray-500);\n    --pst-sd-dropdown-bg-color: var(--pst-color-surface);\n    & + div.sd-summary-content {\n      --pst-sd-dropdown-color: var(--sd-color-card-border);\n    }\n    @each $name in $sd-semantic-color-names {\n      &.sd-bg-#{$name} {\n        --pst-sd-dropdown-color: var(--sd-color-#{$name});\n        --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        // Otherwise it won't be defined in the sibling element\n        & + div.sd-summary-content {\n          --pst-sd-dropdown-color: var(--sd-color-#{$name});\n          --pst-sd-dropdown-bg-color: var(--sd-color-#{$name}-bg);\n        }\n      }\n      &.sd-bg-text-#{$name} {\n        // Use the WCAG conformant text color\n        color: var(--sd-color-#{$name}-bg-text) !important;\n      }\n    }\n\n    // Background color and border are grey by default\n    background-color: unset !important;\n    @include background-from-color-variable(--pst-sd-dropdown-bg-color);\n\n    // Add a left border with the same structure as our admonitions\n    border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n    & + div.sd-summary-content {\n      border-left: 0.2rem solid var(--pst-sd-dropdown-color) !important;\n      border-bottom-left-radius: calc(0.25rem - 1px);\n      background-color: var(--pst-color-on-background);\n    }\n    span.sd-summary-icon {\n      display: inline-flex;\n      align-items: center;\n      color: var(--pst-sd-dropdown-color) !important;\n      svg {\n        opacity: 1;\n      }\n    }\n\n    // Positioning of the caret\n    .sd-summary-up,\n    .sd-summary-down {\n      top: 0.7rem;\n    }\n  }\n}\n","/**\n * Sphinx togglebutton\n */\n\n.bd-content {\n  // Admonition toggles\n  .admonition {\n    button.toggle-button {\n      color: inherit;\n    }\n  }\n\n  // Details buttons\n  details.toggle-details {\n    // Over-ride border color to re-use our primary color\n    summary {\n      border-left: 3px solid var(--pst-color-primary);\n    }\n  }\n}\n","/**\n * Styles for various Sphinx execution libraries to display pre-executed notebooks.\n * For now, where these define output sections, we simply revert their background\n * to be a \"light theme\" background. This ensures that inputs/outputs behave similarly,\n * because the CSS is often controlled by each package.\n * In the future, we might add dark theme support for specific packages.\n */\n\n/*******************************************************************************\n * nbsphinx\n */\nhtml div.rendered_html\n// NBsphinx ipywidgets output selector\nhtml .jp-RenderedHTMLCommon {\n  table {\n    table-layout: auto;\n  }\n}\n\n// Dark theme special-cases\nhtml[data-theme=\"dark\"] .bd-content {\n  .nboutput {\n    .output_area.rendered_html {\n      @include cell-output-background;\n    }\n\n    .output_area.stderr {\n      background: var(--pst-color-danger);\n    }\n  }\n}\n\n// Add extra padding to the final item in an nbsphinx container\ndiv.nblast.container {\n  margin-bottom: 1rem;\n}\n\n/*******************************************************************************\n * myst NB\n */\n\ndiv.cell_output .output {\n  max-width: 100%;\n  overflow-x: auto;\n}\n\n// Dark theme special-cases\nhtml[data-theme=\"dark\"] .bd-content {\n  div.cell_output {\n    img,\n    .text_html {\n      @include cell-output-background;\n    }\n  }\n}\n\n// Prevent tables from scrunching together\n.bd-content {\n  div.cell_input {\n    display: flex;\n    flex-direction: column;\n    justify-content: stretch;\n  }\n\n  div.cell_input,\n  div.output {\n    border-radius: $admonition-border-radius;\n  }\n\n  div.output {\n    table {\n      table-layout: auto;\n    }\n  }\n}\n","/**\n * style for the various mapping libs based on leaflet (folium, geemap, ipyleaflet)\n * mainly ensure the good display of the maps in both themes and avoid the customization\n * of the tiles\n */\n\n/**\n * avoid border override from pydata-sphinx-theme\n * minimal selctor to get the priority\n */\nhtml[data-theme=\"dark\"] .bd-content img.leaflet-tile.leaflet-tile-loaded {\n  border-radius: 0;\n  padding: 0;\n}\n","/**\n * /search.html page special-cases\n */\n\n.bd-search-container {\n  /*******************************************\n  * Search results\n  */\n  // Whitespace\n  div#search-results {\n    > h2 {\n      font-size: var(--pst-font-size-icon);\n      margin-top: 0;\n    }\n\n    p.search-summary {\n      color: var(--pst-color-text-muted);\n    }\n  }\n\n  ul.search {\n    margin: 0;\n    list-style: none;\n\n    li {\n      background-image: none;\n      padding: 1rem 0;\n      margin: 1rem 0;\n      border-top: 1px solid var(--pst-color-text-muted);\n\n      // First link is the page title, it should be a bit bigger\n      > a {\n        font-size: 1.2em;\n      }\n\n      div.context,\n      p.context {\n        color: var(--pst-color-text-base);\n        margin: 0.5em 0 0 0;\n\n        // Add a # before page section titles to make it clear they are sections\n        a:before {\n          content: \"#\";\n          padding-right: 0.2em;\n          color: var(--pst-color-text-muted);\n        }\n      }\n    }\n  }\n}\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/stable/_static/underscore-1.13.1.js b/stable/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af9..00000000
--- a/stable/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria;
-      var b = right.criteria;
-      if (a !== b) {
-        if (a > b || a === void 0) return 1;
-        if (a < b || b === void 0) return -1;
-      }
-      return left.index - right.index;
-    }), 'value');
-  }
-
-  // An internal function used for aggregate "group by" operations.
-  function group(behavior, partition) {
-    return function(obj, iteratee, context) {
-      var result = partition ? [[], []] : {};
-      iteratee = cb(iteratee, context);
-      each(obj, function(value, index) {
-        var key = iteratee(value, index, obj);
-        behavior(result, value, key);
-      });
-      return result;
-    };
-  }
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
-
-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
-    if (isString(obj)) {
-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
-
-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
-    if (isFunction$1(iteratee)) {
-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
-
-  // Returns everything but the first entry of the `array`. Especially useful on
-  // the `arguments` object. Passing an **n** will return the rest N values in the
-  // `array`.
-  function rest(array, n, guard) {
-    return slice.call(array, n == null || guard ? 1 : n);
-  }
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array.
-  function last(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten$1(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
-
-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten$1(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
-      for (j = 1; j < argsLength; j++) {
-        if (!contains(arguments[j], item)) break;
-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
-
-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
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diff --git a/stable/_static/underscore.js b/stable/_static/underscore.js
deleted file mode 100644
index cf177d42..00000000
--- a/stable/_static/underscore.js
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/stable/_static/vendor/fontawesome/6.1.2/css/all.min.css b/stable/_static/vendor/fontawesome/6.1.2/css/all.min.css
index b9a01fbb..aad4b1d9 100644
--- a/stable/_static/vendor/fontawesome/6.1.2/css/all.min.css
+++ b/stable/_static/vendor/fontawesome/6.1.2/css/all.min.css
@@ -2,5 +2,4 @@
  * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
  * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
  * Copyright 2022 Fonticons, Inc.
- */
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\ No newline at end of file
diff --git a/stable/_static/vendor/fontawesome/6.1.2/js/all.min.js.LICENSE.txt b/stable/_static/vendor/fontawesome/6.1.2/js/all.min.js.LICENSE.txt
new file mode 100644
index 00000000..b71b9f01
--- /dev/null
+++ b/stable/_static/vendor/fontawesome/6.1.2/js/all.min.js.LICENSE.txt
@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2022 Fonticons, Inc.
+ */
diff --git a/stable/_static/versions.json b/stable/_static/versions.json
index 4f6cf4c7..704a3fcd 100644
--- a/stable/_static/versions.json
+++ b/stable/_static/versions.json
@@ -1,14 +1,19 @@
 [
     {
-        "name": "0.8 (devel)",
+        "name": "0.9 (devel)",
         "version": "dev",
         "url": "https://braindecode.org/dev/"
     },
     {
-        "name": "0.7 (stable)",
-        "version": "stable",
+        "name": "0.8 (stable)",
+        "version": "dev",
         "url": "https://braindecode.org/stable/"
     },
+    {
+        "name": "0.7",
+        "version": "0.7",
+        "url": "https://braindecode.org/0.7/"
+    },
     {
         "name": "0.6",
         "version": "0.6",
diff --git a/stable/_static/webpack-macros.html b/stable/_static/webpack-macros.html
index c90a6f33..f4836945 100644
--- a/stable/_static/webpack-macros.html
+++ b/stable/_static/webpack-macros.html
@@ -4,28 +4,28 @@
 -->
 {# Load FontAwesome icons #}
 {% macro head_pre_icons() %}
-  <link rel="stylesheet"
-    href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}">
+  <link href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=365ca57ee442770a23c6" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" />
 {% endmacro %}
 
 {% macro head_pre_assets() %}
   <!-- Loaded before other Sphinx assets -->
-  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=1e1de1a1873e13ef5536" rel="stylesheet">
-<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=1e1de1a1873e13ef5536" rel="stylesheet">
+  <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=365ca57ee442770a23c6" rel="stylesheet" />
 {% endmacro %}
 
 {% macro head_js_preload() %}
   <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=1e1de1a1873e13ef5536">
+  <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=365ca57ee442770a23c6" />
+<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=365ca57ee442770a23c6" />
+  <script src="{{ pathto('_static/vendor/fontawesome/6.1.2/js/all.min.js', 1) }}?digest=365ca57ee442770a23c6"></script>
 {% endmacro %}
 
 {% macro body_post() %}
   <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=1e1de1a1873e13ef5536"></script>
+  <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=365ca57ee442770a23c6"></script>
+<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=365ca57ee442770a23c6"></script>
 {% endmacro %}
\ No newline at end of file
diff --git a/stable/api.html b/stable/api.html
index ccb949dc..289c9253 100644
--- a/stable/api.html
+++ b/stable/api.html
@@ -1,362 +1,429 @@
 
 <!DOCTYPE html>
 
-<html lang="en">
+
+<html lang="en" data-theme="light">
+
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>API Reference &#8212; Braindecode 0.7 documentation</title>
-  <script>
+    <title>API Reference &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
     document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
     document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
   </script>
   
   <!-- Loaded before other Sphinx assets -->
-  <link href="_static/styles/theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
-<link href="_static/styles/pydata-sphinx-theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
+  <link href="_static/styles/theme.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="_static/styles/bootstrap.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="_static/styles/pydata-sphinx-theme.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
 
   
-  <link rel="stylesheet"
-    href="_static/vendor/fontawesome/6.1.2/css/all.min.css">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2">
+  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
 
-    <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
-    <link rel="stylesheet" type="text/css" href="_static/style.css" />
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
+    <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
+    <link rel="stylesheet" type="text/css" href="_static/style.css?v=09706775" />
   
   <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536">
+  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=365ca57ee442770a23c6" />
+<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6" />
+  <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
 
-    <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-    <script src="_static/jquery.js"></script>
-    <script src="_static/underscore.js"></script>
-    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="_static/doctools.js"></script>
-    <script src="_static/sphinx_highlight.js"></script>
+    <script src="_static/documentation_options.js?v=aad85e6b"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/design-tabs.js?v=36754332"></script>
+    <script async="async" src="https://www.googletagmanager.com/gtag/js?id=G-7Q43R82K6D"></script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'api';</script>
     <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.14.3';
         DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = false;
         </script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="braindecode.classifier.EEGClassifier" href="generated/braindecode.classifier.EEGClassifier.html" />
-    <link rel="prev" title="How to train, test and tune your model" href="auto_examples/plot_how_train_test_and_tune.html" />
-  <meta name="viewport" content="width=device-width, initial-scale=1" />
-  <meta name="docsearch:language" content="en">
+    <link rel="prev" title="Process a big data EEG resource (TUH EEG Corpus)" href="auto_examples/applied_examples/plot_tuh_eeg_corpus.html" />
+    <link rel="canonical" href="braindecode.org/index.html" />
+    
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+
   </head>
   
   
-  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="180" data-default-mode="light">
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
-  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
+  </button>
 
   
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
   <div class="search-button__wrapper">
     <div class="search-button__overlay"></div>
     <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="search.html" method="get">
+<form class="bd-search d-flex align-items-center"
+      action="search.html"
+      method="get">
   <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
+  <input type="search"
+         class="form-control"
+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
   <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
-</form>
-    </div>
+</form></div>
   </div>
-
   
-  <nav class="bd-header navbar navbar-expand-lg bd-navbar" id="navbar-main"><div class="bd-header__inner bd-page-width">
+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
   <label class="sidebar-toggle primary-toggle" for="__primary">
-      <span class="fa-solid fa-bars"></span>
+    <span class="fa-solid fa-bars"></span>
   </label>
-  <div id="navbar-start">
-    
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
     
+      <div class="navbar-item">
   
 
-
 <a class="navbar-brand logo" href="index.html">
-
-  
-  
   
   
   
   
   
-
-  
-    <img src="_static/braindecode_small.svg" class="logo__image only-light" alt="Logo image">
-    <img src="_static/braindecode_small.svg" class="logo__image only-dark" alt="Logo image">
+    
+    
+    
+    <img src="_static/braindecode_symbol.png" class="logo__image only-light" alt="Braindecode Logo"/>
+    <script>document.write(`<img src="_static/braindecode_symbol.png" class="logo__image only-dark" alt="Braindecode Logo"/>`);</script>
   
   
-</a>
+</a></div>
     
   </div>
-
   
   <div class="col-lg-9 navbar-header-items">
-    <div id="navbar-center" class="mr-auto">
+    
+    <div class="me-auto navbar-header-items__center">
       
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
-        
-                <li class="nav-item">
-                    <a class="nav-link" href="install.html">
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
                         Install
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="starting.html">
-                        Get Started
-                    </a>
-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item current active">
-                    <a class="nav-link" href="#">
+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="#">
                         API Reference
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-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
-                </button>
-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
+  </ul>
+</nav></div>
       
     </div>
-
-    <div id="navbar-end">
-      <div class="navbar-end-item navbar-end__search-button-container">
-        
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-      </div>
+    
+    
+    <div class="navbar-header-items__end">
       
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
-    <a class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></a>
-</span>
-      </div>
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+        </div>
       
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
-      </div>
+  </div>
+`);
+</script></div>
       
     </div>
+    
   </div>
-
-
   
-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-  </div>
+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
       <span class="fa-solid fa-outdent"></span>
-  </label>
+    </label>
   
-
 </div>
-  </nav>
-  
 
+    </nav>
+  
   <div class="bd-container">
     <div class="bd-container__inner bd-page-width">
       
       <div class="bd-sidebar-primary bd-sidebar">
         
+
   
   <div class="sidebar-header-items sidebar-primary__section">
     
     
       <div class="sidebar-header-items__center">
-      
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
         
-                <li class="nav-item">
-                    <a class="nav-link" href="install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
                         Install
-                    </a>
-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="starting.html">
-                        Get Started
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
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-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,151 +530,181 @@
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
-
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+  
   <section id="api-reference">
-<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">#</a></h1>
+<span id="id1"></span><h1>API Reference<a class="headerlink" href="#api-reference" title="Link to this heading">#</a></h1>
 <p>This is the reference for classes (<code class="docutils literal notranslate"><span class="pre">CamelCase</span></code> names) and functions
 (<code class="docutils literal notranslate"><span class="pre">underscore_case</span></code> names) of Braindecode.</p>
-<div class="contents local topic" id="contents">
+<nav class="contents local" id="contents">
 <ul class="simple">
-<li><p><a class="reference internal" href="#classifier" id="id6">Classifier</a></p></li>
-<li><p><a class="reference internal" href="#regressor" id="id7">Regressor</a></p></li>
-<li><p><a class="reference internal" href="#models" id="id8">Models</a></p></li>
-<li><p><a class="reference internal" href="#training" id="id9">Training</a></p></li>
-<li><p><a class="reference internal" href="#datasets" id="id10">Datasets</a></p></li>
-<li><p><a class="reference internal" href="#preprocessing" id="id11">Preprocessing</a></p></li>
-<li><p><a class="reference internal" href="#data-utils" id="id12">Data Utils</a></p></li>
-<li><p><a class="reference internal" href="#samplers" id="id13">Samplers</a></p></li>
-<li><p><a class="reference internal" href="#augmentation" id="id14">Augmentation</a></p></li>
-<li><p><a class="reference internal" href="#utils" id="id15">Utils</a></p></li>
-<li><p><a class="reference internal" href="#visualization" id="id16">Visualization</a></p></li>
+<li><p><a class="reference internal" href="#classifier" id="id8">Classifier</a></p></li>
+<li><p><a class="reference internal" href="#regressor" id="id9">Regressor</a></p></li>
+<li><p><a class="reference internal" href="#models" id="id10">Models</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id11">Training</a></p></li>
+<li><p><a class="reference internal" href="#datasets" id="id12">Datasets</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id13">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#data-utils" id="id14">Data Utils</a></p></li>
+<li><p><a class="reference internal" href="#samplers" id="id15">Samplers</a></p></li>
+<li><p><a class="reference internal" href="#augmentation" id="id16">Augmentation</a></p></li>
+<li><p><a class="reference internal" href="#utils" id="id17">Utils</a></p></li>
+<li><p><a class="reference internal" href="#visualization" id="id18">Visualization</a></p></li>
 </ul>
-</div>
+</nav>
 <p><a class="reference internal" href="#module-braindecode" title="braindecode"><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode</span></code></a>:</p>
 <span class="target" id="module-braindecode"></span><section id="classifier">
-<h2><a class="toc-backref" href="#id6">Classifier</a><a class="headerlink" href="#classifier" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Classifier</a><a class="headerlink" href="#classifier" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.classifier</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier" title="braindecode.classifier.EEGClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a>(*args[, cropped, callbacks, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier" title="braindecode.classifier.EEGClassifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a>(module, *args[, criterion, ...])</p></td>
 <td><p>Classifier that does not assume softmax activation.</p></td>
 </tr>
 </tbody>
 </table>
 </section>
 <section id="regressor">
-<h2><a class="toc-backref" href="#id7">Regressor</a><a class="headerlink" href="#regressor" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Regressor</a><a class="headerlink" href="#regressor" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.regressor</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor" title="braindecode.regressor.EEGRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a>(*args[, cropped, callbacks, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor" title="braindecode.regressor.EEGRegressor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a>(module, *args[, cropped, ...])</p></td>
 <td><p>Regressor that calls loss function directly.</p></td>
 </tr>
 </tbody>
 </table>
 </section>
 <section id="models">
-<h2><a class="toc-backref" href="#id8">Models</a><a class="headerlink" href="#models" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id10" role="doc-backlink">Models</a><a class="headerlink" href="#models" title="Link to this heading">#</a></h2>
+<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models.base</span></code>:</p>
+<table class="autosummary longtable table autosummary">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin" title="braindecode.models.EEGModuleMixin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a>([n_outputs, n_chans, ...])</p></td>
+<td><p>Mixin class for all EEG models in braindecode.</p></td>
+</tr>
+</tbody>
+</table>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code></a>(in_chans, n_classes[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code></a>([n_chans, n_outputs, ...])</p></td>
 <td><p>Shallow ConvNet model from Schirrmeister et al 2017.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Deep4Net</span></code></a>(in_chans, n_classes, ...[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Deep4Net</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>Deep ConvNet model from Schirrmeister et al 2017.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInception</span></code></a>(in_channels, n_classes[, ...])</p></td>
-<td><p>EEG Inception.</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet" title="braindecode.models.DeepSleepNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DeepSleepNet</span></code></a>([n_outputs, return_feats, ...])</p></td>
+<td><p>Sleep staging architecture from Supratak et al 2017.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer" title="braindecode.models.EEGConformer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGConformer</span></code></a>([n_outputs, n_chans, ...])</p></td>
+<td><p>EEG Conformer.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet" title="braindecode.models.EEGITNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGITNet</span></code></a>(n_classes, in_channels, ...[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInception</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
+<td><p>EEG Inception for ERP-based classification</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP" title="braindecode.models.EEGInceptionERP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInceptionERP</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>EEG Inception for ERP-based classification</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI" title="braindecode.models.EEGInceptionMI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>EEG Inception for Motor Imagery, as proposed in <a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#rc36ed781f4f5-1" id="id2"><span>[Rc36ed781f4f5-1]</span></a></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet" title="braindecode.models.ATCNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ATCNet</span></code></a>([n_chans, n_outputs, ...])</p></td>
+<td><p>ATCNet model from <a class="reference internal" href="generated/braindecode.models.ATCNet.html#r2ecdb73d6ab9-1" id="id3"><span>[R2ecdb73d6ab9-1]</span></a></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet" title="braindecode.models.EEGITNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGITNet</span></code></a>([n_outputs, n_chans, n_times, ...])</p></td>
 <td><p>EEG-ITNet: An Explainable Inception Temporal</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1" title="braindecode.models.EEGNetv1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv1</span></code></a>(in_chans, n_classes[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1" title="braindecode.models.EEGNetv1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv1</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>EEGNet model from Lawhern et al. 2016.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4" title="braindecode.models.EEGNetv4"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv4</span></code></a>(in_chans, n_classes[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4" title="braindecode.models.EEGNetv4"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGNetv4</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>EEGNet v4 model from Lawhern et al 2018.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet" title="braindecode.models.HybridNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HybridNet</span></code></a>(in_chans, n_classes, ...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet" title="braindecode.models.HybridNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HybridNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>Hybrid ConvNet model from Schirrmeister et al 2017.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet" title="braindecode.models.EEGResNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGResNet</span></code></a>(in_chans, n_classes, ...[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet" title="braindecode.models.EEGResNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EEGResNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>Residual Network for EEG.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.TCN.html#braindecode.models.TCN" title="braindecode.models.TCN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TCN</span></code></a>(n_in_chans, n_outputs, n_blocks, ...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.TCN.html#braindecode.models.TCN" title="braindecode.models.TCN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TCN</span></code></a>([n_chans, n_outputs, n_blocks, ...])</p></td>
 <td><p>Temporal Convolutional Network (TCN) from Bai et al 2018.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a>(n_channels, sfreq[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a>([n_chans, sfreq, ...])</p></td>
 <td><p>Sleep staging architecture from Chambon et al 2018.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020" title="braindecode.models.SleepStagerBlanco2020"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a>(n_channels, sfreq[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020" title="braindecode.models.SleepStagerBlanco2020"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a>([n_chans, sfreq, ...])</p></td>
 <td><p>Sleep staging architecture from Blanco et al 2020.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a>(sfreq[, n_tce, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a>([sfreq, n_tce, ...])</p></td>
 <td><p>Sleep Staging Architecture from Eldele et al 2021.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-obj docutils literal notranslate"><span class="pre">USleep</span></code></a>([in_chans, sfreq, depth, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-obj docutils literal notranslate"><span class="pre">USleep</span></code></a>([n_chans, sfreq, depth, ...])</p></td>
 <td><p>Sleep staging architecture from Perslev et al 2021.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TIDNet</span></code></a>(in_chans, n_classes, input_window_samples)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TIDNet</span></code></a>([n_chans, n_outputs, n_times, ...])</p></td>
 <td><p>Thinker Invariance DenseNet model from Kostas et al 2020.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_shape</span></code></a>(model, in_chans, ...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_output_shape</span></code></a>(model, in_chans, ...)</p></td>
 <td><p>Returns shape of neural network output for batch size equal 1.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeDistributed</span></code></a>(module)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TimeDistributed</span></code></a>(module)</p></td>
 <td><p>Apply module on multiple windows.</p></td>
 </tr>
 </tbody>
 </table>
 </section>
 <section id="training">
-<h2><a class="toc-backref" href="#id9">Training</a><a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id11" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.training</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CroppedLoss</span></code></a>(loss_function)</p></td>
 <td><p>Compute Loss after averaging predictions across time.</p></td>
@@ -632,13 +734,9 @@ <h2><a class="toc-backref" href="#id9">Training</a><a class="headerlink" href="#
 </table>
 </section>
 <section id="datasets">
-<h2><a class="toc-backref" href="#id10">Datasets</a><a class="headerlink" href="#datasets" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id12" role="doc-backlink">Datasets</a><a class="headerlink" href="#datasets" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.datasets</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseDataset</span></code></a>(raw[, description, target_name, ...])</p></td>
 <td><p>Returns samples from an mne.io.Raw object along with a target.</p></td>
@@ -653,7 +751,7 @@ <h2><a class="toc-backref" href="#id10">Datasets</a><a class="headerlink" href="
 <td><p>A class for moabb datasets.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.datasets.HGD.html#braindecode.datasets.HGD" title="braindecode.datasets.HGD"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HGD</span></code></a>(subject_ids)</p></td>
-<td><p>High-gamma dataset discribed in Schirrmeister et al. 2017.</p></td>
+<td><p>High-gamma dataset described in Schirrmeister et al. 2017.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.datasets.BNCI2014001.html#braindecode.datasets.BNCI2014001" title="braindecode.datasets.BNCI2014001"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BNCI2014001</span></code></a>(subject_ids)</p></td>
 <td><p>BNCI 2014-001 Motor Imagery dataset.</p></td>
@@ -683,13 +781,9 @@ <h2><a class="toc-backref" href="#id10">Datasets</a><a class="headerlink" href="
 </table>
 </section>
 <section id="preprocessing">
-<h2><a class="toc-backref" href="#id11">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.preprocessing</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_windows_from_events</span></code></a>(concat_ds[, ...])</p></td>
 <td><p>Create windows based on events in mne.Raw.</p></td>
@@ -706,29 +800,40 @@ <h2><a class="toc-backref" href="#id11">Preprocessing</a><a class="headerlink" h
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential_moving_standardize</span></code></a>(data[, ...])</p></td>
 <td><p>Perform exponential moving standardization.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scale</span></code></a>(data, factor)</p></td>
-<td><p>DEPRECATED: will be removed in 0.8.0.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank" title="braindecode.preprocessing.filterbank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filterbank</span></code></a>(raw, frequency_bands[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank" title="braindecode.preprocessing.filterbank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filterbank</span></code></a>(raw, frequency_bands[, ...])</p></td>
 <td><p>Applies multiple bandpass filters to the signals in raw.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>(concat_ds, preprocessors[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>(concat_ds, preprocessors[, ...])</p></td>
 <td><p>Apply preprocessors to a concat dataset.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Preprocessor</span></code></a>(fn, *[, apply_on_array])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Preprocessor</span></code></a>(fn, *[, apply_on_array])</p></td>
 <td><p>Preprocessor for an MNE Raw or Epochs object.</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Resample</span></code></a>([up, down, npad, axis, window, ...])</p></td>
+<td><p>Resample an array.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropChannels</span></code></a>(ch_names[, on_missing])</p></td>
+<td><p>Drop channel(s).</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a>([ref_channels, projection, ...])</p></td>
+<td><p>Specify which reference to use for EEG data.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Filter</span></code></a>(l_freq, h_freq[, picks, ...])</p></td>
+<td><p>Filter a subset of channels.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Pick</span></code></a>(picks[, exclude, verbose])</p></td>
+<td><p>Pick a subset of channels.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Crop</span></code></a>([tmin, tmax, include_tmax, verbose])</p></td>
+<td><p>Crop raw data file.</p></td>
+</tr>
 </tbody>
 </table>
 </section>
 <section id="data-utils">
-<h2><a class="toc-backref" href="#id12">Data Utils</a><a class="headerlink" href="#data-utils" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Data Utils</a><a class="headerlink" href="#data-utils" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.datautil</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.datautil.save_concat_dataset.html#braindecode.datautil.save_concat_dataset" title="braindecode.datautil.save_concat_dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_concat_dataset</span></code></a>(path, concat_dataset[, ...])</p></td>
 <td><p></p></td>
@@ -740,13 +845,9 @@ <h2><a class="toc-backref" href="#id12">Data Utils</a><a class="headerlink" href
 </table>
 </section>
 <section id="samplers">
-<h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="#samplers" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Samplers</a><a class="headerlink" href="#samplers" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.samplers</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler" title="braindecode.samplers.RecordingSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a>(metadata[, random_state])</p></td>
 <td><p>Base sampler simplifying sampling from recordings.</p></td>
@@ -755,7 +856,7 @@ <h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="
 <td><p>Sample sequences of consecutive windows.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RelativePositioningSampler</span></code></a>(metadata, ...[, ...])</p></td>
-<td><p>Sample examples for the relative positioning task from <a class="reference internal" href="generated/braindecode.samplers.RelativePositioningSampler.html#r0467437a2408-banville2020" id="id2"><span>[R0467437a2408-Banville2020]</span></a>.</p></td>
+<td><p>Sample examples for the relative positioning task from <a class="reference internal" href="generated/braindecode.samplers.RelativePositioningSampler.html#r0467437a2408-banville2020" id="id4"><span>[R0467437a2408-Banville2020]</span></a>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler" title="braindecode.samplers.BalancedSequenceSampler"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a>(metadata, n_windows)</p></td>
 <td><p>Balanced sampling of sequences of consecutive windows with categorical targets.</p></td>
@@ -764,13 +865,9 @@ <h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="
 </table>
 </section>
 <section id="augmentation">
-<span id="augmentation-api"></span><h2><a class="toc-backref" href="#id14">Augmentation</a><a class="headerlink" href="#augmentation" title="Permalink to this heading">#</a></h2>
+<span id="augmentation-api"></span><h2><a class="toc-backref" href="#id16" role="doc-backlink">Augmentation</a><a class="headerlink" href="#augmentation" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.augmentation</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Transform</span></code></a>([probability, random_state])</p></td>
 <td><p>Basic transform class used for implementing data augmentation operations.</p></td>
@@ -791,7 +888,7 @@ <h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="
 <td><p>Flip the sign axis of each input with a given probability.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a>(probability[, ...])</p></td>
-<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="generated/braindecode.augmentation.FTSurrogate.html#ra7c6c14d9bd9-1" id="id3"><span>[Ra7c6c14d9bd9-1]</span></a>.</p></td>
+<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="generated/braindecode.augmentation.FTSurrogate.html#ra7c6c14d9bd9-1" id="id5"><span>[Ra7c6c14d9bd9-1]</span></a>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle" title="braindecode.augmentation.ChannelsShuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a>(probability[, p_shuffle, ...])</p></td>
 <td><p>Randomly shuffle channels in EEG data matrix.</p></td>
@@ -839,7 +936,7 @@ <h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="
 <td><p>Flip the sign axis of each input.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.augmentation.functional.ft_surrogate.html#braindecode.augmentation.functional.ft_surrogate" title="braindecode.augmentation.functional.ft_surrogate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.ft_surrogate</span></code></a>(X, y, ...[, ...])</p></td>
-<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="generated/braindecode.augmentation.functional.ft_surrogate.html#r52a4658fffa7-1" id="id4"><span>[R52a4658fffa7-1]</span></a>.</p></td>
+<td><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="generated/braindecode.augmentation.functional.ft_surrogate.html#r52a4658fffa7-1" id="id6"><span>[R52a4658fffa7-1]</span></a>.</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.augmentation.functional.channels_dropout.html#braindecode.augmentation.functional.channels_dropout" title="braindecode.augmentation.functional.channels_dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">functional.channels_dropout</span></code></a>(X, y, p_drop[, ...])</p></td>
 <td><p>Randomly set channels to flat signal.</p></td>
@@ -872,13 +969,9 @@ <h2><a class="toc-backref" href="#id13">Samplers</a><a class="headerlink" href="
 </table>
 </section>
 <section id="utils">
-<h2><a class="toc-backref" href="#id15">Utils</a><a class="headerlink" href="#utils" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">Utils</a><a class="headerlink" href="#utils" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.util</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_random_seeds</span></code></a>(seed, cuda[, cudnn_benchmark])</p></td>
 <td><p>Set seeds for python random module numpy.random and torch.</p></td>
@@ -887,19 +980,15 @@ <h2><a class="toc-backref" href="#id15">Utils</a><a class="headerlink" href="#ut
 </table>
 </section>
 <section id="visualization">
-<h2><a class="toc-backref" href="#id16">Visualization</a><a class="headerlink" href="#visualization" title="Permalink to this heading">#</a></h2>
+<h2><a class="toc-backref" href="#id18" role="doc-backlink">Visualization</a><a class="headerlink" href="#visualization" title="Link to this heading">#</a></h2>
 <p><code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.visualization</span></code>:</p>
 <table class="autosummary longtable table autosummary">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
 <tbody>
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.visualization.compute_amplitude_gradients.html#braindecode.visualization.compute_amplitude_gradients" title="braindecode.visualization.compute_amplitude_gradients"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_amplitude_gradients</span></code></a>(model, dataset, ...)</p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_confusion_matrix</span></code></a>(confusion_mat[, ...])</p></td>
-<td><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html#r8046536b33dd-1" id="id5"><span>[R8046536b33dd-1]</span></a>.</p></td>
+<td><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html#r8046536b33dd-1" id="id7"><span>[R8046536b33dd-1]</span></a>.</p></td>
 </tr>
 </tbody>
 </table>
@@ -907,148 +996,105 @@ <h2><a class="toc-backref" href="#id16">Visualization</a><a class="headerlink" h
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-   Samplers
-  </a>
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+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#datasets">Datasets</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#data-utils">Data Utils</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#samplers">Samplers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#augmentation">Augmentation</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#utils">Utils</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualization">Visualization</a></li>
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+<p>Examples explaining more advanced topics in neural network training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Advanced neural network training strategies</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+      
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+              
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="fingers-flexion-cropped-decoding-on-bcic-iv-4-ecog-dataset">
+<span id="sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"></span><h1>Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-cropped-decoding-on-bcic-iv-4-ecog-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train and test deep learning models with
+Braindecode on ECoG BCI IV competition dataset 4 using cropped mode. For this
+dataset we will predict 5 regression targets corresponding to flexion of each finger.
+The targets were recorded as a time series (each 25 Hz), so this tutorial is
+an example of time series target prediction.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
+<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preparing-the-dataset">
+<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of braindecode
+to load <a class="reference external" href="http://www.bbci.de/competition/iv/#dataset4">BCI IV competition dataset 4</a>.
+The dataset is available as a part of ECoG library:
+<a class="reference external" href="https://searchworks.stanford.edu/view/zk881ps0522">https://searchworks.stanford.edu/view/zk881ps0522</a></p>
+<p>The dataset contains ECoG signal and time series of 5 targets corresponding
+to each finger flexion. This is different than standard decoding setup for EEG with
+multiple trials and usually one target per trial. Here, fingers flexions change in time
+and are recorded with sampling frequency equals to 25 Hz.</p>
+<p>If this dataset is used please cite [1].</p>
+<p>[1] Miller, Kai J. “A library of human electrocorticographic data and analyses.
+“Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">copy</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">sklearn</span>
+<span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">1</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-test">
+<h3>Split dataset into train and test<a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select <cite>train</cite> dataset
+for training and validation and <cite>test</cite> for final evaluation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Now we apply preprocessing like bandpass filtering to our dataset. You
+can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Preprocessing steps are taken from a standard EEG processing pipeline.
+The only change is the cutoff frequency of the filter. For a proper ECoG
+decoding other preprocessing steps may be needed.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>These prepocessings are now directly applied to the loaded
+data, and not on-the-fly applied as transformations in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">1.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">200.</span>  <span class="c1"># high cut frequency for filtering, for ECoG higher than for EEG</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+</pre></div>
+</div>
+<p>We select only first 30 seconds from the training dataset to limit time and memory
+to run this example. We split training dataset into train and validation (only 6 seconds).
+To obtain full results whole datasets should be used.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/copy.html#copy.deepcopy" title="copy.deepcopy" class="sphx-glr-backref-module-copy sphx-glr-backref-type-py-function"><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span>
+                       <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)],</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4542b6bbb0&gt;
+</pre></div>
+</div>
+<p>In time series targets setup, targets variables are stored in mne.Raw object as channels
+of type <cite>misc</cite>. Thus those channels have to be selected for further processing. However,
+many mne functions ignore <cite>misc</cite> channels and perform operations only on data channels
+(see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="c1"># TODO: ensure that misc is not removed</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Extract target sampling frequency</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;temp&#39;</span><span class="p">][</span><span class="s1">&#39;target_sfreq&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>In contrast to trialwise decoding, we first have to create the model
+before we can cut the dataset into windows. This is because we need to
+know the receptive field of the network to know how large the window
+stride should be.</p>
+<p>We first choose the compute/input window size that will be fed to the
+network during training This has to be larger than the networks
+receptive field size and can otherwise be chosen for computational
+efficiency (see explanations in the beginning of this tutorial). Here we
+choose 1000 samples, which is 1 second for the 1000 Hz sampling rate.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+</pre></div>
+</div>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the shallow ConvNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
+learning with convolutional neural networks for EEG decoding and
+visualization</a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">1</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mi">5</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">,</span> <span class="n">to_dense_prediction_model</span>
+
+<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function to_dense_prediction_model is deprecated; will be removed in version 1.0. Use EEGModuleMixin.to_dense_prediction_model method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+</pre></div>
+</div>
+<p>To know the models’ receptive field, we calculate the shape of model
+output for a dummy input.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function get_output_shape is deprecated; will be removed in version 1.0. Use EEGModuleMixin.get_output_shape method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+</pre></div>
+</div>
+<section id="cut-compute-windows">
+<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span>
+    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;channels&#39;</span><span class="p">,</span>
+    <span class="n">last_target_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span>
+    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;channels&#39;</span><span class="p">,</span>
+    <span class="n">last_target_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span>
+    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">targets_from</span><span class="o">=</span><span class="s1">&#39;channels&#39;</span><span class="p">,</span>
+    <span class="n">last_target_only</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We select only the thumb’s finger flexion to create one model per finger.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Methods to predict all 5 fingers flexion with the same model may be considered as well.
+We encourage you to find your own way to use braindecode models to predict fingers flexions.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>In difference to trialwise decoding, we now should supply
+<code class="docutils literal notranslate"><span class="pre">cropped=True</span></code> to the EEGClassifier, and <code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code> as the
+criterion, as well as <code class="docutils literal notranslate"><span class="pre">criterion__loss_function</span></code> as the loss function
+applied to the meaned predictions.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for EEG motor decoding, however we strongly
+encourage you to perform your own hyperparameter optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">,</span> <a href="../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a>
+
+<span class="c1"># These values we found good for shallow network for EEG MI decoding:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">27</span>  <span class="c1"># only 27 examples in train set, otherwise set to 64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">8</span>
+
+<span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">aggregate_predictions</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss" title="braindecode.training.TimeSeriesLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">TimeSeriesLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+        <span class="p">(</span><span class="s1">&#39;r2_train&#39;</span><span class="p">,</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
+                                                   <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                                   <span class="n">name</span><span class="o">=</span><span class="s1">&#39;r2_train&#39;</span><span class="p">)</span>
+         <span class="p">),</span>
+        <span class="p">(</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">,</span> <a href="../../generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring" title="braindecode.training.CroppedTimeSeriesEpochScoring" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedTimeSeriesEpochScoring</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">sklearn</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">r2_score</span></a><span class="p">,</span>
+                                                   <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                   <span class="n">name</span><span class="o">=</span><span class="s1">&#39;r2_valid&#39;</span><span class="p">)</span>
+         <span class="p">)</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Model training for a specified number of epochs. <code class="docutils literal notranslate"><span class="pre">y</span></code> is None as it is already supplied
+in the dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
+-------  ----------  ----------  ------------  ------------  ------  ------
+      1    -16.9037     -8.3086        2.5378       20.7324  0.0006  0.4844
+      2    -13.7539     -7.1244        1.9596       18.0720  0.0006  0.4082
+      3    -12.7768     -6.7402        1.6787       17.1870  0.0005  0.4264
+      4    -11.7970     -6.3910        1.5638       16.4409  0.0004  0.4111
+      5    -11.2712     -6.1840        1.4237       16.0095  0.0002  0.4141
+      6    -10.5735     -5.9203        1.3549       15.4436  0.0001  0.4042
+      7     -9.8228     -5.6181        1.3132       14.7799  0.0000  0.4198
+      8     -9.0643     -5.3047        1.3316       14.0884  0.0000  0.4033
+</pre></div>
+</div>
+<p>Obtaining predictions and targets for the test, train, and validation dataset</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">pad_and_select_predictions</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">preds</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="numpy.pad" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">pad</span></a><span class="p">(</span><span class="n">preds</span><span class="p">,</span>
+                   <span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">0</span><span class="p">)),</span>
+                   <span class="s1">&#39;constant&#39;</span><span class="p">,</span>
+                   <span class="n">constant_values</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="n">mask</span> <span class="o">=</span> <span class="o">~</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">isnan</span></a><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:])</span>
+    <span class="n">preds</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.squeeze.html#numpy.squeeze" title="numpy.squeeze" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span></a><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">mask</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.squeeze.html#numpy.squeeze" title="numpy.squeeze" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span></a><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">mask</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">preds</span><span class="o">.</span><span class="n">T</span>
+
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">predict_trials</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">return_targets</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <span class="n">pad_and_select_predictions</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We plot target and predicted finger flexion on training, validation, and test sets.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The model is trained and validated on limited dataset (to decrease the time needed to run
+this example) which does not contain diverse dataset in terms of fingers flexions and may
+cause overfitting. To obtain better results use whole dataset as well as improve the decoding
+pipeline which may be not optimal for ECoG.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Training dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+             <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Validation dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+             <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Test dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time [s]&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+        <a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+    <span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.round.html#numpy.round" title="numpy.round" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">round</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Correlation coefficient for each dimension:  [-0.1]
+</pre></div>
+</div>
+<p>Now we use the history stored by Skorch throughout training to plot
+accuracy and loss curves.
+Extract loss and accuracy values for plotting from history object</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;r2_train&#39;</span><span class="p">,</span> <span class="s1">&#39;r2_valid&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;r2_train&#39;</span><span class="p">,</span> <span class="s1">&#39;r2_valid&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;R2 score&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span>
+                      <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span>
+                      <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center right&#39;</span><span class="p">)</span>
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+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 24.674 seconds)</p>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../model_building/index.html">Basic model building and training</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Advanced neural network training strategies</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="data-augmentation-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"></span><h1>Data Augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#data-augmentation-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train EEG deep models with data augmentation. It
+follows the trial-wise decoding example and also illustrates the effect of a
+transform on the input signals.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id1">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id2">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id3">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-windows" id="id4">Extracting windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id5">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#defining-a-transform" id="id6">Defining a Transform</a></p>
+<ul>
+<li><p><a class="reference internal" href="#manipulating-one-session-and-visualizing-the-transformed-data" id="id7">Manipulating one session and visualizing the transformed data</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#training-a-model-with-data-augmentation" id="id8">Training a model with data augmentation</a></p>
+<ul>
+<li><p><a class="reference internal" href="#create-model" id="id9">Create model</a></p></li>
+<li><p><a class="reference internal" href="#create-an-eegclassifier-with-the-desired-augmentation" id="id10">Create an EEGClassifier with the desired augmentation</a></p></li>
+<li><p><a class="reference internal" href="#manually-composing-transforms" id="id11">Manually composing Transforms</a></p></li>
+<li><p><a class="reference internal" href="#setting-the-data-augmentation-at-the-dataset-level" id="id12">Setting the data augmentation at the Dataset level</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
+<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id2" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45423c4c10&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-windows">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
+<span class="c1"># define how trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="defining-a-transform">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Defining a Transform</a><a class="headerlink" href="#defining-a-transform" title="Link to this heading">#</a></h2>
+<p>Data can be manipulated by transforms, which are callable objects. A
+transform is usually handled by a custom data loader, but can also be called
+directly on input data, as demonstrated below for illutrative purposes.</p>
+<p>First, we need to define a Transform. Here we chose the FrequencyShift, which
+randomly translates all frequencies within a given range.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a>
+
+<a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
+    <span class="n">probability</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>  <span class="c1"># defines the probability of actually modifying the input</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<section id="manipulating-one-session-and-visualizing-the-transformed-data">
+<h3><a class="toc-backref" href="#id7" role="doc-backlink">Manipulating one session and visualizing the transformed data</a><a class="headerlink" href="#manipulating-one-session-and-visualizing-the-transformed-data" title="Link to this heading">#</a></h3>
+<p>Next, let us augment one session to show the resulting frequency shift. The
+data of an mne Epoch is used here to make usage of mne functions.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+<span class="c1"># This allows to apply the transform with a fixed shift (10 Hz) for</span>
+<span class="c1"># visualization instead of sampling the shift randomly between -2 and 2 Hz</span>
+<a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a><span class="o">.</span><span class="n">operation</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="kc">None</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>The psd of the transformed session has now been shifted by 10 Hz, as one can
+see on the psd plot.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+
+<span class="k">def</span> <span class="nf">plot_psd</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="p">):</span>
+    <span class="n">psds</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.time_frequency.psd_array_multitaper.html#mne.time_frequency.psd_array_multitaper" title="mne.time_frequency.psd_array_multitaper" class="sphx-glr-backref-module-mne-time_frequency sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">time_frequency</span><span class="o">.</span><span class="n">psd_array_multitaper</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+                                                          <span class="n">fmin</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">psds</span> <span class="o">=</span> <span class="mf">10.</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">log10</span></a><span class="p">(</span><span class="n">psds</span><span class="p">)</span>
+    <span class="n">psds_mean</span> <span class="o">=</span> <span class="n">psds</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freqs</span><span class="p">,</span> <span class="n">psds_mean</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">plot_psd</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">plot_psd</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="s1">&#39;shifted&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Multitaper PSD (gradiometers)&#39;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">,</span>
+       <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;Power Spectral Density (dB)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_data_augmentation_001.png" srcset="../../_images/sphx_glr_plot_data_augmentation_001.png" alt="Multitaper PSD (gradiometers)" class = "sphx-glr-single-img"/></section>
+</section>
+<section id="training-a-model-with-data-augmentation">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Training a model with data augmentation</a><a class="headerlink" href="#training-a-model-with-data-augmentation" title="Link to this heading">#</a></h2>
+<p>Now that we know how to instantiate <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
+to use them to train a model and try to improve its generalization power.
+Let’s first create a model.</p>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>The model to be trained is defined as usual.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+</pre></div>
+</div>
+</section>
+<section id="create-an-eegclassifier-with-the-desired-augmentation">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Create an EEGClassifier with the desired augmentation</a><a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Link to this heading">#</a></h3>
+<p>In order to train with data augmentation, a custom data loader can be
+for the training. Multiple transforms can be passed to it and will be applied
+sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a>
+
+<a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
+    <span class="n">probability</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">.1</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a>
+<span class="p">]</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>The model is now trained as in the trial-wise example. The
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
+transforms are passed as arguments.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">iterator_train</span><span class="o">=</span><a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
+    <span class="n">iterator_train__transforms</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>  <span class="c1"># This sets the augmentations to use</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2500        1.6077       0.2500            0.2500        3.0179  0.0006  1.7356
+      2            0.2569        1.3809       0.2569            0.2569        2.1898  0.0005  1.5168
+      3            0.3021        1.2767       0.2604            0.2604        1.7960  0.0002  1.5961
+      4            0.3229        1.1763       0.2674            0.2674        1.6417  0.0000  1.5376
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)
+</pre></div>
+</div>
+</section>
+<section id="manually-composing-transforms">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Manually composing Transforms</a><a class="headerlink" href="#manually-composing-transforms" title="Link to this heading">#</a></h3>
+<p>It would be equivalent (although more verbose) to pass to <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code> a
+composition of the same transforms:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a>
+
+<a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="setting-the-data-augmentation-at-the-dataset-level">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Setting the data augmentation at the Dataset level</a><a class="headerlink" href="#setting-the-data-augmentation-at-the-dataset-level" title="Link to this heading">#</a></h3>
+<p>Also note that it is also possible for most of the transforms to pass them
+directly to the WindowsDataset object through the <cite>transform</cite> argument, as
+most commonly done in other libraries. However, it is advised to use the
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> as above, as it is compatible with all transforms and
+can be more efficient.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><a href="../../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 16.285 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  506 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/e3845395de44913c970dbf26671a7cba/plot_data_augmentation.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1d879df548fa18be8c23d9ca0dc008d4/plot_data_augmentation.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_data_augmentation.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+</section>
+
+
+  
+</div>
+
+              
+              
+              
+              
+                <footer class="prev-next-footer">
+                  <!-- Previous / next buttons -->
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+        <p class="prev-next-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</p>
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+       title="next page">
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+        <p class="prev-next-title">Searching the best data augmentation on BCIC IV 2a Dataset</p>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-windows">Extracting windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#defining-a-transform">Defining a Transform</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#manipulating-one-session-and-visualizing-the-transformed-data">Manipulating one session and visualizing the transformed data</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-with-data-augmentation">Training a model with data augmentation</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-an-eegclassifier-with-the-desired-augmentation">Create an EEGClassifier with the desired augmentation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#manually-composing-transforms">Manually composing Transforms</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#setting-the-data-augmentation-at-the-dataset-level">Setting the data augmentation at the Dataset level</a></li>
+</ul>
+</li>
+</ul>
+  </nav></div>
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diff --git a/stable/auto_examples/advanced_training/plot_data_augmentation_search.html b/stable/auto_examples/advanced_training/plot_data_augmentation_search.html
new file mode 100644
index 00000000..fc19d40e
--- /dev/null
+++ b/stable/auto_examples/advanced_training/plot_data_augmentation_search.html
@@ -0,0 +1,1083 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
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+to download the full example code</p>
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+<section class="sphx-glr-example-title" id="searching-the-best-data-augmentation-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"></span><h1>Searching the best data augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#searching-the-best-data-augmentation-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to search data augmentations using braindecode.
+Indeed, it is known that the best augmentation to use often dependent on the task
+or phenomenon studied. Here we follow the methodology proposed in <a class="footnote-reference brackets" href="#id6" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> on the
+openly available BCI IV 2a Dataset.</p>
+<aside class="topic">
+<p class="topic-title">Data Augmentation</p>
+<p>Data augmentation could be a step in training deep learning models.
+For decoding brain signals, recent studies have shown that artificially
+generating samples may increase the final performance of a deep learning model <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+Other studies have shown that data augmentation can be used to cast
+a self-supervised paradigm, presenting a more diverse
+view of the data, both with pretext tasks and contrastive learning <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+</aside>
+<p>Data augmentation and self-supervised learning approaches demand an intense comparison
+to find the best fit with the data. This view is demonstrated in <a class="footnote-reference brackets" href="#id6" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and shows the
+importance of selecting the right transformation and strength for different type of
+task considered. Here, we use the augmentation module present in braindecode in
+the context of trialwise decoding with the BCI IV 2a dataset.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id8">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id9">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id10">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-windows" id="id11">Extracting windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id12">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#defining-a-list-of-transforms" id="id13">Defining a list of transforms</a></p></li>
+<li><p><a class="reference internal" href="#training-a-model-with-data-augmentation" id="id14">Training a model with data augmentation</a></p>
+<ul>
+<li><p><a class="reference internal" href="#create-model" id="id15">Create model</a></p></li>
+<li><p><a class="reference internal" href="#create-an-eegclassifier-with-the-desired-augmentation" id="id16">Create an EEGClassifier with the desired augmentation</a></p></li>
+<li><p><a class="reference internal" href="#analysing-the-best-fit" id="id17">Analysing the best fit</a></p></li>
+<li><p><a class="reference internal" href="#plot-results" id="id18">Plot results</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id19">References</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Bruno Aristimunha &lt;a.bruno@ufabc.edu.br&gt;</span>
+<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of braindecode
+to load BCI IV competition dataset 1. The dataset is available on the BNCI website.
+There is 9 subjects recorded with 22 electrodes while doing a motor imagery task,
+with 144 trials per class. We will load this dataset through the MOABB library.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>We apply a bandpass filter, from 4 to 38 Hz to focus motor imagery-related
+brain activity</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+</pre></div>
+</div>
+<p>In time series targets setup, targets variables are stored in mne.Raw object as channels
+of type <cite>misc</cite>. Thus those channels have to be selected for further processing. However,
+many mne functions ignore <cite>misc</cite> channels and perform operations only on data channels
+(see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45416e9210&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-windows">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
+<p>Now we cut out compute windows, the inputs for the deep networks during
+training. We use the braindecode function for this, provinding parameters
+to define how trials should be used.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>Following the split defined in the BCI competition</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="defining-a-list-of-transforms">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Defining a list of transforms</a><a class="headerlink" href="#defining-a-list-of-transforms" title="Link to this heading">#</a></h2>
+<p>In this tutorial, we will use three categories of augmentations.
+This categorization has been proposed by <a class="footnote-reference brackets" href="#id6" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to explain and aggregate
+the several possibilities of augmentations in EEG, being them:</p>
+<ol class="loweralpha simple">
+<li><p>Frequency domain augmentations,</p></li>
+<li><p>Time domain augmentations,</p></li>
+<li><p>Spatial domain augmentations.</p></li>
+</ol>
+<p>From this same paper, we selected the best augmentations in each type: <code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code>,
+<code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code>, <code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code>, respectively.</p>
+<p>For each augmentation, we adjustable two values from a range for one parameter
+inside the transformation.</p>
+<p>It is important to remember that you can increase the range.
+For that, we need to define three lists of transformations and range for the parameter
+∆φmax in FTSurrogate where ∆φmax ∈ [0, 2π); for ∆t in SmoothTimeMask is ∆t ∈ [0, 2];
+For the method ChannelsDropout, we analyse the parameter p_drop ∈ [0, 1].</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">,</span> <a href="../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">phase_noise_magnitude</span><span class="o">=</span><span class="n">phase_freq</span><span class="p">,</span>
+                               <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">phase_freq</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">mask_len_samples</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="n">second</span><span class="p">),</span>
+                                  <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">second</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p_drop</span><span class="o">=</span><span class="n">prob</span><span class="p">,</span>
+                                      <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">prob</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
+</pre></div>
+</div>
+</section>
+<section id="training-a-model-with-data-augmentation">
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Training a model with data augmentation</a><a class="headerlink" href="#training-a-model-with-data-augmentation" title="Link to this heading">#</a></h2>
+<p>Now that we know how to instantiate three list of <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
+to use them to train a model and try to search the best for the dataset.
+Let’s first create a model for search a parameter.</p>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id15" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>The model to be trained is defined as usual.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+<p>Set random seed to be able to roughly reproduce results
+Note that with cudnn benchmark set to True, GPU indeterminism
+may still make results substantially different between runs.
+To obtain more consistent results at the cost of increased computation time,
+you can set <code class="docutils literal notranslate"><span class="pre">cudnn_benchmark=False</span></code> in <code class="docutils literal notranslate"><span class="pre">set_random_seeds</span></code>
+or remove <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.benchmark</span> <span class="pre">=</span> <span class="pre">True</span></code></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+</pre></div>
+</div>
+</section>
+<section id="create-an-eegclassifier-with-the-desired-augmentation">
+<h3><a class="toc-backref" href="#id16" role="doc-backlink">Create an EEGClassifier with the desired augmentation</a><a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Link to this heading">#</a></h3>
+<p>In order to train with data augmentation, a custom data loader can be
+for the training. Multiple transforms can be passed to it and will be applied
+sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>The model is now trained as in the trial-wise example. The
+<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
+transforms are passed as arguments.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">iterator_train</span><span class="o">=</span><a href="../../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
+    <span class="n">iterator_train__transforms</span><span class="o">=</span><span class="p">[],</span>  <span class="c1"># This sets is handled by GridSearchCV</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># GridSearchCV will control the split and train/validation over the dataset</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s1">&#39;lr_scheduler&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>To use the skorch framework, it is necessary to transform the windows
+dataset using the module SliceData. Also, it is mandatory to eval the
+generator of the training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">train_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">(</span><span class="nb">list</span><span class="p">(</span><a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
+</pre></div>
+</div>
+<p>Given the trialwise approach, here we use the KFold approach and
+GridSearchCV.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;iterator_train__transforms&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">verbose</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 6 candidates, totalling 12 fits
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=20200220, shuffle=True),
+             error_score=&#x27;raise&#x27;,
+             estimator=&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Ou...
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+),
+             param_grid={&#x27;iterator_train__transforms&#x27;: [FTSurrogate(),
+                                                        FTSurrogate(),
+                                                        SmoothTimeMask(),
+                                                        SmoothTimeMask(),
+                                                        ChannelsDropout(),
+                                                        ChannelsDropout()]},
+             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">GridSearchCV</label><div class="sk-toggleable__content"><pre>GridSearchCV(cv=KFold(n_splits=2, random_state=20200220, shuffle=True),
+             error_score=&#x27;raise&#x27;,
+             estimator=&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Ou...
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+),
+             param_grid={&#x27;iterator_train__transforms&#x27;: [FTSurrogate(),
+                                                        FTSurrogate(),
+                                                        SmoothTimeMask(),
+                                                        SmoothTimeMask(),
+                                                        ChannelsDropout(),
+                                                        ChannelsDropout()]},
+             return_train_score=True, scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: EEGClassifier</label><div class="sk-toggleable__content"><pre>&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">EEGClassifier</label><div class="sk-toggleable__content"><pre>&lt;class &#x27;braindecode.classifier.EEGClassifier&#x27;&gt;[uninitialized](
+  module=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 47,364
+  Trainable params: 47,364
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.10
+  Forward/backward pass size (MB): 0.35
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.50
+  ============================================================================================================================================,
+)</pre></div></div></div></div></div></div></div></div></div></div>
+</div>
+<br />
+<br /></section>
+<section id="analysing-the-best-fit">
+<h3><a class="toc-backref" href="#id17" role="doc-backlink">Analysing the best fit</a><a class="headerlink" href="#analysing-the-best-fit" title="Link to this heading">#</a></h3>
+<p>Next, just perform an analysis of the best fit, and the parameters,
+remembering the order that was adjusted.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+
+<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">best_aug</span></a> <span class="o">=</span> <span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a> <span class="o">=</span> <span class="s1">&#39;Best augmentation is saved in best_aug which gave a mean validation accuracy&#39;</span> <span class="o">+</span> \
+                 <span class="s1">&#39;of </span><span class="si">{}</span><span class="s1">% (train accuracy of </span><span class="si">{}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a><span class="p">)</span>
+
+<span class="n">eval_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">eval_y</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><span class="n">eval_X</span><span class="p">,</span> <span class="n">eval_y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">%.&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best augmentation is saved in best_aug which gave a mean validation accuracyof 27.78% (train accuracy of 28.47%).
+Eval accuracy is 25.00%.
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h3><a class="toc-backref" href="#id18" role="doc-backlink">Plot results</a><a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">search_results</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span>
+    <span class="n">x</span><span class="o">=</span><span class="s2">&quot;param_iterator_train__transforms&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s2">&quot;mean_train_score&quot;</span><span class="p">,</span> <span class="n">yerr</span><span class="o">=</span><span class="s2">&quot;std_train_score&quot;</span><span class="p">,</span>
+    <span class="n">rot</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;C0&quot;</span><span class="p">,</span> <span class="s2">&quot;C0&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">],</span> <span class="n">legend</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Data augmentation strategy&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.32</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_data_augmentation_search_001.png" srcset="../../_images/sphx_glr_plot_data_augmentation_search_001.png" alt="plot data augmentation search" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h3><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h3>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id4">3</a>,<a role="doc-backlink" href="#id5">4</a>)</span>
+<p>Rommel, C., Paillard, J., Moreau, T., &amp; Gramfort, A. (2022)
+Data augmentation for learning predictive models on EEG:
+a systematic comparison. <a class="reference external" href="https://arxiv.org/abs/2206.14483">https://arxiv.org/abs/2206.14483</a></p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A. (2021).
+Uncovering the structure of clinical EEG signals with self-supervised learning.
+Journal of Neural Engineering, 18(4), 046020.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 31.872 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  522 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-data-augmentation-search-py">
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+<p><a class="reference download internal" download="" href="../../_downloads/0f763ae384277e558103757157e170fb/plot_data_augmentation_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_data_augmentation_search.py</span></code></a></p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-windows">Extracting windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#defining-a-list-of-transforms">Defining a list of transforms</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training-a-model-with-data-augmentation">Training a model with data augmentation</a><ul class="nav section-nav flex-column">
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-an-eegclassifier-with-the-desired-augmentation">Create an EEGClassifier with the desired augmentation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#analysing-the-best-fit">Analysing the best fit</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#plot-results">Plot results</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Advanced neural network training strategies</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+      
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+</div>
+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="self-supervised-learning-on-eeg-with-relative-positioning">
+<span id="sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"></span><h1>Self-supervised learning on EEG with relative positioning<a class="headerlink" href="#self-supervised-learning-on-eeg-with-relative-positioning" title="Link to this heading">#</a></h1>
+<p>This example shows how to train a neural network with self-supervision on sleep
+EEG data. We follow the relative positioning approach of <a class="footnote-reference brackets" href="#id15" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> on the openly
+accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id16" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id17" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<aside class="topic">
+<p class="topic-title">Self-supervised learning</p>
+<p>Self-supervised learning (SSL) is a learning paradigm that leverages
+unlabelled data to train neural networks. First, neural networks are
+trained on a “pretext task” which uses unlabelled data only. The pretext
+task is designed based on a prior understanding of the data under study
+(e.g., EEG has an underlying autocorrelation structure) and such that the
+processing required to perform well on this pretext task is related to the
+processing required to perform well on another task of interest.
+Once trained, these neural networks can be reused as feature extractors or
+weight initialization in a “downstream task”, which is the task that we are
+actually interested in (e.g., sleep staging). The pretext task step can
+help reduce the quantity of labelled data needed to perform well on the
+downstream task and/or improve downstream performance as compared to a
+strictly supervised approach <a class="footnote-reference brackets" href="#id15" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+</aside>
+<p>Here, we use relative positioning (RP) as our pretext task, and perform sleep
+staging as our downstream task. RP is a simple SSL task, in which a neural
+network is trained to predict whether two randomly sampled EEG windows are
+close or far apart in time. This method was shown to yield physiologically- and
+clinically-relevant features and to boost classification performance in
+low-labels data regimes <a class="footnote-reference brackets" href="#id15" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id19">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-the-raw-recordings" id="id20">Loading the raw recordings</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id21">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-windows" id="id22">Extracting windows</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing-windows" id="id23">Preprocessing windows</a></p></li>
+<li><p><a class="reference internal" href="#splitting-dataset-into-train-valid-and-test-sets" id="id24">Splitting dataset into train, valid and test sets</a></p></li>
+<li><p><a class="reference internal" href="#creating-samplers" id="id25">Creating samplers</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#creating-the-model" id="id26">Creating the model</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id27">Training</a></p></li>
+<li><p><a class="reference internal" href="#visualizing-the-results" id="id28">Visualizing the results</a></p>
+<ul>
+<li><p><a class="reference internal" href="#inspecting-pretext-task-performance" id="id29">Inspecting pretext task performance</a></p></li>
+<li><p><a class="reference internal" href="#using-the-learned-representation-for-sleep-staging" id="id30">Using the learned representation for sleep staging</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#conclusion" id="id31">Conclusion</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id32">References</a></p></li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a> <span class="o">=</span> <span class="mi">87</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a> <span class="o">=</span> <span class="mi">1</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id19" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading-the-raw-recordings">
+<h3><a class="toc-backref" href="#id20" role="doc-backlink">Loading the raw recordings</a><a class="headerlink" href="#loading-the-raw-recordings" title="Link to this heading">#</a></h3>
+<p>First, we load a few recordings from the Sleep Physionet dataset. Running
+this example with more recordings should yield better representations and
+downstream classification performance.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.sleep_physionet</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id21" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
+a lowpass filter. Since the Sleep Physionet data is already sampled at 100 Hz
+we don’t need to apply resampling.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f454279da80&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-windows">
+<h3><a class="toc-backref" href="#id22" role="doc-backlink">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in both the pretext and downstream tasks.
+As RP (and SSL in general) don’t require labelled data, the pretext task
+could be performed using unlabelled windows extracted with
+<code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.windower.create_fixed_length_window()</span></code>.
+Here however, purely for convenience, we directly extract labelled windows so
+that we can reuse them in the sleep staging downstream task later.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
+<span class="p">}</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing-windows">
+<h3><a class="toc-backref" href="#id23" role="doc-backlink">Preprocessing windows</a><a class="headerlink" href="#preprocessing-windows" title="Link to this heading">#</a></h3>
+<p>We also preprocess the windows by applying channel-wise z-score normalization.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4540a2a710&gt;
+</pre></div>
+</div>
+</section>
+<section id="splitting-dataset-into-train-valid-and-test-sets">
+<h3><a class="toc-backref" href="#id24" role="doc-backlink">Splitting dataset into train, valid and test sets</a><a class="headerlink" href="#splitting-dataset-into-train-valid-and-test-sets" title="Link to this heading">#</a></h3>
+<p>We randomly split the recordings by subject into train, validation and
+testing sets. We further define a new Dataset class which can receive a pair
+of indices and return the corresponding windows. This will be needed when
+training and evaluating on the pretext task.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subjects</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_valid</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">RelativePositioningDataset</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;BaseConcatDataset with __getitem__ that expects 2 indices and a target.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">list_of_ds</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">list_of_ds</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">return_pair</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_pair</span><span class="p">:</span>
+            <span class="n">ind1</span><span class="p">,</span> <span class="n">ind2</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <span class="n">index</span>
+            <span class="k">return</span> <span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">ind1</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">ind2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">return_pair</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_return_pair</span>
+
+    <span class="nd">@return_pair</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">return_pair</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_return_pair</span> <span class="o">=</span> <span class="n">value</span>
+
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;train&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_train</span></a><span class="p">,</span> <span class="s1">&#39;valid&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_valid</span></a><span class="p">,</span> <span class="s1">&#39;test&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subj_test</span></a><span class="p">}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">RelativePositioningDataset</span></a><span class="p">(</span>
+        <span class="p">[</span><span class="n">ds</span> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a>
+         <span class="k">if</span> <span class="n">ds</span><span class="o">.</span><span class="n">description</span><span class="p">[</span><span class="s1">&#39;subject&#39;</span><span class="p">]</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="creating-samplers">
+<h3><a class="toc-backref" href="#id25" role="doc-backlink">Creating samplers</a><a class="headerlink" href="#creating-samplers" title="Link to this heading">#</a></h3>
+<p>Next, we need to create samplers. These samplers will be used to randomly
+sample pairs of examples to train and validate our model with
+self-supervision.</p>
+<p>The RP samplers have two main hyperparameters. <cite>tau_pos</cite> and <cite>tau_neg</cite>
+control the size of the “positive” and “negative” contexts, respectively.
+Pairs of windows that are separated by less than <cite>tau_pos</cite> samples will be
+given a label of <cite>1</cite>, while pairs of windows that are separated by more than
+<cite>tau_neg</cite> samples will be given a label of <cite>0</cite>. Here, we use the same values
+as in <a class="footnote-reference brackets" href="#id15" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, i.e., <a href="#id7"><span class="problematic" id="id8">`</span></a>tau_pos`= 1 min and <a href="#id9"><span class="problematic" id="id10">`</span></a>tau_neg`= 15 mins.</p>
+<p>The samplers also control the number of pairs to be sampled (defined with
+<cite>n_examples</cite>). This number can be large to help regularize the pretext task
+training, for instance 2,000 pairs per recording as in <a class="footnote-reference brackets" href="#id15" id="id11" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>. Here, we use a
+lower number of 250 pairs per recording to reduce training time.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="mi">60</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="mi">15</span> <span class="o">*</span> <span class="mi">60</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_train</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_valid</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_test</span></a> <span class="o">=</span> <span class="mi">250</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">datasets</span><span class="p">)</span>
+
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
+    <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_train</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
+    <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_valid</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span><span class="o">.</span><span class="n">presample</span><span class="p">()</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">RelativePositioningSampler</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_pos</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tau_neg</span></a><span class="p">,</span>
+    <span class="n">n_examples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_examples_test</span></a><span class="p">,</span> <span class="n">same_rec_neg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span><span class="o">.</span><span class="n">presample</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="creating-the-model">
+<h2><a class="toc-backref" href="#id26" role="doc-backlink">Creating the model</a><a class="headerlink" href="#creating-the-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use a
+modified version of the sleep staging architecture introduced in <a class="footnote-reference brackets" href="#id18" id="id12" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a> -
+a four-layer convolutional neural network - as our embedder.
+We change the dimensionality of the last layer to obtain a 100-dimension
+embedding, use 16 convolutional channels instead of 8, and add batch
+normalization after both temporal convolution layers.</p>
+<p>We further wrap the model into a siamese architecture using the
+# <code class="xref py py-class docutils literal notranslate"><span class="pre">ContrastiveNet</span></code> class defined below. This allows us to train the
+feature extractor end-to-end.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">==</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
+
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+
+<a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span>
+    <span class="n">n_conv_chs</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
+    <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">apply_batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">ContrastiveNet</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Module</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Contrastive module with linear layer on top of siamese embedder.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    emb : nn.Module</span>
+<span class="sd">        Embedder architecture.</span>
+<span class="sd">    emb_size : int</span>
+<span class="sd">        Output size of the embedder.</span>
+<span class="sd">    dropout : float</span>
+<span class="sd">        Dropout rate applied to the linear layer of the contrastive module.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a>
+        <span class="bp">self</span><span class="o">.</span><span class="n">clf</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
+            <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="n">dropout</span><span class="p">),</span>
+            <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="n">z1</span><span class="p">,</span> <span class="n">z2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">x2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clf</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.abs.html#torch.abs" title="torch.abs" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">abs</span></a><span class="p">(</span><span class="n">z1</span> <span class="o">-</span> <span class="n">z2</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+
+
+<span class="n">model</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">ContrastiveNet</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb_size</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id27" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network on the pretext task. We use similar
+hyperparameters as in <a class="footnote-reference brackets" href="#id15" id="id13" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, but reduce the number of epochs and
+increase the learning rate to account for the smaller setting of
+this example.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">Checkpoint</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">5e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">128</span>  <span class="c1"># 512 if data large enough</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">25</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a> <span class="o">&lt;=</span> <span class="mi">1</span> <span class="k">else</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_jobs</span></a>
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cp</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">Checkpoint</span></a><span class="p">(</span><span class="n">dirname</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">f_criterion</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">f_optimizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">f_history</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">early_stopping</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EarlyStopping</span></a><span class="p">(</span><span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="o">=</span><span class="s1">&#39;train_acc&#39;</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;cp&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cp</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;patience&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EarlyStopping" title="skorch.callbacks.EarlyStopping" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">early_stopping</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;train_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_acc</span></a><span class="p">),</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="torch.nn.BCEWithLogitsLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_train__num_workers</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__num_workers</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">]),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.load_params" title="skorch.net.NeuralNet.load_params" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">load_params</span></a><span class="p">(</span><span class="n">checkpoint</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Checkpoint" title="skorch.callbacks.Checkpoint" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cp</span></a><span class="p">)</span>  <span class="c1"># Load the model with the lowest valid_loss</span>
+
+<a href="https://docs.python.org/3/library/os.html#os.remove" title="os.remove" class="sphx-glr-backref-module-os sphx-glr-backref-type-py-function"><span class="n">os</span><span class="o">.</span><span class="n">remove</span></a><span class="p">(</span><span class="s1">&#39;./params.pt&#39;</span><span class="p">)</span>  <span class="c1"># Delete parameters file</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_acc    train_loss    valid_acc    valid_loss    cp     dur
+-------  -----------  ------------  -----------  ------------  ----  ------
+      1       0.5234        0.7013       0.6680        0.6320     +  1.1152
+      2       0.5938        0.7149       0.4880        0.8358        0.9011
+      3       0.4922        1.0040       0.6440        0.6172     +  0.9228
+      4       0.5234        0.7031       0.6120        0.5990     +  0.9178
+      5       0.5391        0.6751       0.5920        0.6213        0.8892
+      6       0.6719        0.6227       0.5920        0.6263        0.8797
+      7       0.6562        0.6309       0.6240        0.6117        0.8809
+      8       0.6641        0.6272       0.6480        0.5950     +  0.8784
+      9       0.6328        0.6238       0.6680        0.5797     +  0.8630
+     10       0.6406        0.6177       0.6800        0.5746     +  0.8675
+     11       0.6250        0.6323       0.7040        0.5787        0.8569
+     12       0.6094        0.6281       0.6760        0.5772        0.8705
+     13       0.6328        0.6422       0.6880        0.5790        0.8659
+     14       0.6406        0.5920       0.6840        0.5765        0.8664
+     15       0.6562        0.6170       0.6920        0.5730     +  0.8699
+     16       0.7578        0.5608       0.6960        0.5676     +  0.8702
+     17       0.6875        0.5936       0.7120        0.5612     +  0.8605
+     18       0.7734        0.5472       0.7080        0.5500     +  0.8628
+     19       0.7656        0.5245       0.7120        0.5400     +  0.8698
+     20       0.6641        0.5641       0.7160        0.5333     +  0.9171
+     21       0.7422        0.5307       0.7200        0.5272     +  0.8718
+     22       0.7109        0.5499       0.7360        0.5211     +  0.8695
+     23       0.6250        0.6259       0.7400        0.5164     +  0.8774
+     24       0.7031        0.5712       0.7400        0.5120     +  0.8637
+     25       0.7109        0.5030       0.7280        0.5120        0.8741
+</pre></div>
+</div>
+</section>
+<section id="visualizing-the-results">
+<h2><a class="toc-backref" href="#id28" role="doc-backlink">Visualizing the results</a><a class="headerlink" href="#visualizing-the-results" title="Link to this heading">#</a></h2>
+<section id="inspecting-pretext-task-performance">
+<h3><a class="toc-backref" href="#id29" role="doc-backlink">Inspecting pretext task performance</a><a class="headerlink" href="#inspecting-pretext-task-performance" title="Link to this heading">#</a></h3>
+<p>We plot the loss and pretext task performance for the training and validation
+sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+
+<span class="n">df</span><span class="p">[</span><span class="s1">&#39;train_acc&#39;</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">100</span>
+<span class="n">df</span><span class="p">[</span><span class="s1">&#39;valid_acc&#39;</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">100</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys1</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys2</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_acc&#39;</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">styles</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">]</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys1</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ys2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">styles</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a><span class="p">):</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+             <span class="n">c</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y1</span></a><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y2</span></a><span class="p">],</span> <span class="n">ls</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">style</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">marker</span></a><span class="p">,</span> <span class="n">ms</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span>
+             <span class="n">c</span><span class="o">=</span><span class="s1">&#39;tab:orange&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y2</span></a><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:orange&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Accuracy [%]&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:orange&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Epoch&#39;</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines1</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_legend_handles_labels.html#matplotlib.axes.Axes.get_legend_handles_labels" title="matplotlib.axes.Axes.get_legend_handles_labels" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">get_legend_handles_labels</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_legend_handles_labels.html#matplotlib.axes.Axes.get_legend_handles_labels" title="matplotlib.axes.Axes.get_legend_handles_labels" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_legend_handles_labels</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines1</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lines2</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels1</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels2</span></a><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_relative_positioning_001.png" srcset="../../_images/sphx_glr_plot_relative_positioning_001.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><p>We also display the confusion matrix and classification report for the
+pretext task:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+
+<span class="c1"># Switch to the test sampler</span>
+<a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">iterator_valid__sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a>
+<a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.forward" title="skorch.net.NeuralNet.forward" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">forward</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler" title="braindecode.samplers.RelativePositioningSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_sampler</span></a><span class="p">]</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[90 31]
+ [41 88]]
+              precision    recall  f1-score   support
+
+         0.0       0.69      0.74      0.71       121
+         1.0       0.74      0.68      0.71       129
+
+    accuracy                           0.71       250
+   macro avg       0.71      0.71      0.71       250
+weighted avg       0.71      0.71      0.71       250
+</pre></div>
+</div>
+</section>
+<section id="using-the-learned-representation-for-sleep-staging">
+<h3><a class="toc-backref" href="#id30" role="doc-backlink">Using the learned representation for sleep staging</a><a class="headerlink" href="#using-the-learned-representation-for-sleep-staging" title="Link to this heading">#</a></h3>
+<p>We can now use the trained convolutional neural network as a feature
+extractor. We perform sleep stage classification from the learned feature
+representation using a linear logistic regression classifier.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
+
+<span class="c1"># Extract features with the trained embedder</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">split</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">split</span><span class="o">.</span><span class="n">return_pair</span> <span class="o">=</span> <span class="kc">False</span>  <span class="c1"># Return single windows</span>
+    <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><span class="n">split</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">num_workers</span></a><span class="p">)</span>
+    <span class="k">with</span> <a href="https://pytorch.org/docs/stable/generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span></a><span class="p">():</span>
+        <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">emb</span></a><span class="p">(</span><span class="n">batch_x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">))</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+                 <span class="k">for</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loader</span></a><span class="p">]</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feats</span></a><span class="p">),</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">split</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()[</span><span class="s1">&#39;target&#39;</span><span class="p">]</span><span class="o">.</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">values</span></a><span class="p">)</span>
+
+<span class="c1"># Initialize the logistic regression model</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">log_reg</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class"><span class="n">LogisticRegression</span></a><span class="p">(</span>
+    <span class="n">penalty</span><span class="o">=</span><span class="s1">&#39;l2&#39;</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s1">&#39;lbfgs&#39;</span><span class="p">,</span>
+    <span class="n">multi_class</span><span class="o">=</span><span class="s1">&#39;multinomial&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">random_state</span></a><span class="p">)</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf_pipe</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class"><span class="n">StandardScaler</span></a><span class="p">(),</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">log_reg</span></a><span class="p">)</span>
+
+<span class="c1"># Fit and score the logistic regression</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.fit" title="sklearn.pipeline.Pipeline.fit" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="o">*</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline.predict" title="sklearn.pipeline.Pipeline.predict" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-method"><span class="n">clf_pipe</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y_pred</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;valid&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_y_pred</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_bal_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Sleep staging performance with logistic regression:&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Train bal acc: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">train_bal_acc</span></a><span class="si">:</span><span class="s1">0.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Valid bal acc: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">valid_bal_acc</span></a><span class="si">:</span><span class="s1">0.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Test bal acc: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_bal_acc</span></a><span class="si">:</span><span class="s1">0.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Results on test set:&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+Sleep staging performance with logistic regression:
+Train bal acc: 0.8924
+Valid bal acc: 0.5215
+Test bal acc: 0.6011
+Results on test set:
+[[107  26   3   4   2]
+ [  7  75   9   2  16]
+ [ 70  60 350   2  80]
+ [  0   0  55  50   0]
+ [  0  77  14   0  79]]
+              precision    recall  f1-score   support
+
+           0       0.58      0.75      0.66       142
+           1       0.32      0.69      0.43       109
+           2       0.81      0.62      0.70       562
+           3       0.86      0.48      0.61       105
+           4       0.45      0.46      0.46       170
+
+    accuracy                           0.61      1088
+   macro avg       0.60      0.60      0.57      1088
+weighted avg       0.68      0.61      0.62      1088
+</pre></div>
+</div>
+<p>The balanced accuracy is much higher than chance-level (i.e., 20% for our
+5-class classification problem). Finally, we perform a quick 2D visualization
+of the feature space using a PCA:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class"><span class="n">PCA</span></a>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html#numpy.concatenate" title="numpy.concatenate" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span></a><span class="p">([</span><span class="n">v</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">data</span></a><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pca</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="sklearn.decomposition.PCA" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-class"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="c1"># tsne = TSNE(n_components=2)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">components</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA.fit_transform" title="sklearn.decomposition.PCA.fit_transform" class="sphx-glr-backref-module-sklearn-decomposition sphx-glr-backref-type-py-method"><span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/cm_api.html#matplotlib.cm.get_cmap" title="matplotlib.cm.get_cmap" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-function"><span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span></a><span class="p">(</span><span class="s1">&#39;viridis&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">)(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stage</span></a> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">&#39;W&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">]):</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mask</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">components</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mask</span></a><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">components</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mask</span></a><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span>
+               <span class="n">color</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">],</span> <span class="n">label</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stage</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_relative_positioning_002.png" srcset="../../_images/sphx_glr_plot_relative_positioning_002.png" alt="plot relative positioning" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/examples/advanced_training/plot_relative_positioning.py:472: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
+  colors = cm.get_cmap(&#39;viridis&#39;, 5)(range(5))
+
+&lt;matplotlib.legend.Legend object at 0x7f45409fac20&gt;
+</pre></div>
+</div>
+<p>We see that there is sleep stage-related structure in the embedding. A
+nonlinear projection method (e.g., tSNE, UMAP) might yield more insightful
+visualizations. Using a similar approach, the embedding space could also be
+explored with respect to subject-level features, e.g., age and sex.</p>
+</section>
+</section>
+<section id="conclusion">
+<h2><a class="toc-backref" href="#id31" role="doc-backlink">Conclusion</a><a class="headerlink" href="#conclusion" title="Link to this heading">#</a></h2>
+<p>In this example, we used self-supervised learning (SSL) as a way to learn
+representations from unlabelled raw EEG data. Specifically, we used the
+relative positioning (RP) pretext task to train a feature extractor on a
+subset of the Sleep Physionet dataset. We then reused these features in a
+downstream sleep staging task. We achieved reasonable downstream performance
+and further showed with a 2D projection that the learned embedding space
+contained sleep-related structure.</p>
+<p>Many avenues could be taken to improve on these results. For instance, using
+the entire Sleep Physionet dataset or training on larger datasets should help
+the feature extractor learn better representations during the pretext task.
+Other SSL tasks such as those described in <a class="footnote-reference brackets" href="#id15" id="id14" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> could further help discover
+more powerful features.</p>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id32" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id15" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id11">5</a>,<a role="doc-backlink" href="#id13">6</a>,<a role="doc-backlink" href="#id14">7</a>)</span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A.
+(2020). Uncovering the structure of clinical EEG signals with
+self-supervised learning. arXiv preprint arXiv:2007.16104.</p>
+</aside>
+<aside class="footnote brackets" id="id16" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A., &amp; Oberye, J. J.
+(2000). Analysis of a sleep-dependent neuronal feedback loop: the
+slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical
+Engineering, 47(9), 1185-1194.</p>
+</aside>
+<aside class="footnote brackets" id="id17" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov,
+P. C., Mark, R. G., … &amp; Stanley, H. E. (2000). PhysioBank,
+PhysioToolkit, and PhysioNet: components of a new research resource for
+complex physiologic signals. circulation, 101(23), e215-e220.</p>
+</aside>
+<aside class="footnote brackets" id="id18" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id12">4</a><span class="fn-bracket">]</span></span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+(2018)A Deep Learning Architecture for Temporal Sleep Stage
+Classification Using Multivariate and Multimodal Time Series.
+IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+(758-769)</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 17.568 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  199 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-advanced-training-plot-relative-positioning-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/bc6f7fe8a7751868a4ff60828f6634e5/plot_relative_positioning.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_relative_positioning.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/aa8426d97090e7b3062c4e4732c3214a/plot_relative_positioning.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_relative_positioning.ipynb</span></code></a></p>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-raw-recordings">Loading the raw recordings</a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extracting-windows">Extracting windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing-windows">Preprocessing windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#splitting-dataset-into-train-valid-and-test-sets">Splitting dataset into train, valid and test sets</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-samplers">Creating samplers</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#creating-the-model">Creating the model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#visualizing-the-results">Visualizing the results</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#inspecting-pretext-task-performance">Inspecting pretext task performance</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#using-the-learned-representation-for-sleep-staging">Using the learned representation for sleep staging</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#conclusion">Conclusion</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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diff --git a/stable/auto_examples/advanced_training/sg_execution_times.html b/stable/auto_examples/advanced_training/sg_execution_times.html
new file mode 100644
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+++ b/stable/auto_examples/advanced_training/sg_execution_times.html
@@ -0,0 +1,528 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
+
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+
+    <title>Computation times &#8212; Braindecode 0.8 documentation</title>
+  
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+  <script data-cfasync="false">
+    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
+    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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+              
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+<span id="sphx-glr-auto-examples-advanced-training-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>03:30.399</strong> total execution time for <strong>auto_examples_advanced_training</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_cropped.py</span></code>)</p></td>
+<td><p>01:24.674</p></td>
+<td><p>656.3 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_relative_positioning.py</span></code>)</p></td>
+<td><p>01:17.568</p></td>
+<td><p>198.9 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation_search.py</span></code>)</p></td>
+<td><p>00:31.872</p></td>
+<td><p>522.1 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation.py</span></code>)</p></td>
+<td><p>00:16.285</p></td>
+<td><p>505.8 MB</p></td>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<span id="sphx-glr-auto-examples-applied-examples"></span><h1>Applied examples on real-world datasets<a class="headerlink" href="#applied-examples-on-real-world-datasets" title="Link to this heading">#</a></h1>
+<p>Examples demonstrating analaysis of EEG on data from clinical studies, sleep &amp; cognitive experiments.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
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+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Applied examples on real-world datasets</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Fingers...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset">
+<span id="sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"></span><h1>Fingers flexion decoding on BCIC IV 4 ECoG Dataset<a class="headerlink" href="#fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train and test deep learning models with
+Braindecode on ECoG BCI IV competition dataset 4. For this dataset we will
+predict 5 regression targets corresponding to flexion of each finger.
+The targets were recorded as a time series (each 25 Hz), so this tutorial is
+an example of time series target prediction.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Maciej Sliwowski &lt;maciek.sliwowski@gmail.com&gt;</span>
+<span class="c1">#          Mohammed Fattouh &lt;mo.fattouh@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preparing-the-dataset">
+<h2>Loading and preparing the dataset<a class="headerlink" href="#loading-and-preparing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of braindecode
+to load <a class="reference external" href="http://www.bbci.de/competition/iv/#dataset4">BCI IV competition dataset 4</a>.
+The dataset is available as a part of ECoG library:
+<a class="reference external" href="https://searchworks.stanford.edu/view/zk881ps0522">https://searchworks.stanford.edu/view/zk881ps0522</a></p>
+<p>The dataset contains ECoG signal and time series of 5 targets corresponding
+to each finger flexion. This is different than standard decoding setup for EEG with
+multiple trials and usually one target per trial. Here, fingers flexions change in time
+and are recorded with sampling frequency equals to 25 Hz.</p>
+<p>If this dataset is used please cite [1].</p>
+<p>[1] Miller, Kai J. “A library of human electrocorticographic data and analyses.
+“Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">1</span>
+<a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BCICompetitionIVDataset4</span></a><span class="p">(</span><span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Now we apply preprocessing like bandpass filtering to our dataset. You
+can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Preprocessing steps are taken from a standard EEG processing pipeline.
+The only change is the cutoff frequency of the filter. For a proper ECoG
+decoding other preprocessing steps may be needed.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>These prepocessings are now directly applied to the loaded
+data, and not on-the-fly applied as transformations in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">1.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">200.</span>  <span class="c1"># high cut frequency for filtering, for ECoG higher than for EEG</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+</pre></div>
+</div>
+<p>We select only first 30 seconds from each dataset to limit time and memory
+to run this example. To obtain results on the whole datasets you should remove this line.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.bcicomp.BCICompetitionIVDataset4 object at 0x7f45411a7af0&gt;
+</pre></div>
+</div>
+<p>In time series targets setup, targets variables are stored in mne.Raw object as channels
+of type <cite>misc</cite>. Thus those channels have to be selected for further processing. However,
+many mne functions ignore <cite>misc</cite> channels and perform operations only on data channels
+(see <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels">https://mne.tools/stable/glossary.html#term-data-channels</a>).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">ecog</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">misc</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span> <span class="n">picks</span><span class="o">=</span><span class="s1">&#39;ecog&#39;</span><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Extract target sampling frequency</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;temp&#39;</span><span class="p">][</span><span class="s1">&#39;target_sfreq&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+</pre></div>
+</div>
+</section>
+<section id="cut-compute-windows">
+<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<p>Now we cut out compute windows, the inputs for the deep networks during
+training. In the case of trialwise decoding of time series targets, we just have to
+decide about length windows that will be selected from the signal preceding each target.
+We use different windowing function than in standard trialwise decoding as our targets
+are stored as target channels in mne.Raw.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_target_channels</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">last_target_only</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We select only the thumb’s finger flexion to create one model per finger.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Methods to predict all 5 fingers flexion with the same model may be cnosidered as well.
+We encourage you to find your own way to use braindecode models to predict finers fexions.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span> <span class="mi">1</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-valid-and-test">
+<h3>Split dataset into train, valid, and test<a class="headerlink" href="#split-dataset-into-train-valid-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select <cite>train</cite> dataset
+for training and validation and <cite>test</cite> for final evaluation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>We can split train dataset into training and validation datasets using
+<code class="docutils literal notranslate"><span class="pre">sklearn.model_selection.train_test_split</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.utils.data.Subset</span></code>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">idx_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">idx_valid</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)),</span>
+                                        <span class="n">random_state</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+                                        <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+                                        <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Subset</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">idx_valid</span></a><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Subset</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">idx_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the shallow ConvNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
+learning with convolutional neural networks for EEG decoding and
+visualization</a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_out_chans</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># 1 second long windows</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_out_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+    <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>Now we train the network! EEGRegressor is a Braindecode object
+responsible for managing the training of neural networks. It inherits
+from skorch.NeuralNetRegressor, so the training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for EEG motor decoding, however we strongly
+encourage you to perform your own hyperparameter and preprocessing optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">mne</span> <span class="kn">import</span> <a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
+
+<span class="c1"># These values we found good for shallow network for EEG MI decoding:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+
+<span class="c1"># Function to compute Pearson correlation coefficient</span>
+<span class="k">def</span> <span class="nf">pearson_r_score</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="n">preds</span> <span class="o">=</span> <span class="n">net</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+        <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><span class="n">y</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">preds</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="k">return</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">)</span>
+
+
+<span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">MSELoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s1">&#39;r2&#39;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span><span class="n">pearson_r_score</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">)),</span>
+        <span class="p">(</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span><span class="n">pearson_r_score</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                         <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">)),</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">set_log_level</span></a><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="s1">&#39;WARNING&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Model training for a specified number of epochs. <code class="docutils literal notranslate"><span class="pre">y</span></code> is None as it is already supplied
+in the dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_pearson_r    train_r2    valid_loss    valid_pearson_r    valid_r2      lr     dur
+-------  ------------  -----------------  ----------  ------------  -----------------  ----------  ------  ------
+      1        2.5083             0.1251     -0.5570        5.7337            -0.1507     -1.6139  0.0006  6.3995
+      2        1.7986             0.3712     -0.3153        5.2582            -0.1510     -1.3971  0.0000  6.2028
+</pre></div>
+</div>
+<p>Obtaining predictions and targets for the test, train, and validation dataset</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html#numpy.stack" title="numpy.stack" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">stack</span></a><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot Results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We plot target and predicted finger flexion on training, validation, and test sets.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The model is trained and validated on limited dataset (to decrease the time needed to run
+this example) which does not contain diverse dataset in terms of fingers flexions and may
+cause overfitting. To obtain better results use whole dataset as well as improve the decoding
+pipeline which may be not optimal for ECoG.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">))</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Training dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_train</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+             <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Validation dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_valid</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_valid</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+             <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Test dataset&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Target&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">target_sfreq</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predicted&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time [s]&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Finger flexion&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_001.png" alt="Training dataset, Validation dataset, Test dataset" class = "sphx-glr-single-img"/><p>We can compute correlation coefficients for each finger</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span><span class="o">.</span><span class="n">shape</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+        <a href="https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html#numpy.corrcoef" title="numpy.corrcoef" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">corrcoef</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preds_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">],</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_test</span></a><span class="p">[:,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dim</span></a><span class="p">])[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+    <span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Correlation coefficient for each dimension: &#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.round.html#numpy.round" title="numpy.round" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">round</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">corr_coeffs</span></a><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Correlation coefficient for each dimension:  [0.1]
+</pre></div>
+</div>
+<p>Now we use the history stored by Skorch throughout training to plot
+accuracy and loss curves.
+Extract loss and accuracy values for plotting from history object</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_pearson_r&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_pearson_r&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_pearson_r&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Pearson correlation coefficient&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span>
+                      <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span>
+                      <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center right&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" alt="plot bcic iv 4 ecog trial" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 25.104 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  214 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/8fb57f368d675b800d7860057478d781/plot_bcic_iv_4_ecog_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/a7ccc5c1d1d2775e08c053bb25c81a91/plot_bcic_iv_4_ecog_trial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.ipynb</span></code></a></p>
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+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+      <a href="../../index.html" class="nav-link" aria-label="Home">
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+              
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Chambon2018 network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the time distributed approach of <a class="footnote-reference brackets" href="#id10" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to learn on
+sequences of EEG windows using the openly accessible Sleep Physionet dataset
+<a class="footnote-reference brackets" href="#id11" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id12" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
+a lowpass filter. We omit the downsampling step of <a class="footnote-reference brackets" href="#id10" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> as the Sleep
+Physionet data is already sampled at a lower 100 Hz.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f4541467fd0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="window-preprocessing">
+<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Link to this heading">#</a></h3>
+<p>We also preprocess the windows by applying channel-wise z-score normalization
+in each window.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4542374550&gt;
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id10" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows, such that the embeddings of
+multiple consecutive windows can be concatenated and provided to a final
+classifier. We can achieve this by defining Sampler objects that return
+sequences of window indices.
+To simplify the example, we train the whole model end-to-end on sequences,
+rather than using the two-step approach of <a class="footnote-reference brackets" href="#id10" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> (i.e. training the feature
+extractor on single windows, then freezing its weights and training the
+classifier).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  372
+Validation examples:  383
+</pre></div>
+</div>
+<p>We also implement a transform to extract the label of the center window of a
+sequence to use it as target.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use label of center window in the sequence</span>
+<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting on the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the sleep
+staging architecture introduced in <a class="footnote-reference brackets" href="#id10" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is a four-layer convolutional
+neural network. We use the time distributed version of the model, where the
+feature vectors of a sequence of windows are concatenated and passed to a
+linear layer for classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+
+<a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
+    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id10" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, as these hyperparameters were
+optimized on a different dataset (MASS SS3) and with a different number of
+recordings. Generally speaking, it is recommended to perform
+hyperparameter optimization if reusing this code on a different dataset or
+with more recordings.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">10</span>
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_acc    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  -----------  ---------------  ------------  ------
+      1           0.2136        1.5768       0.0888           0.2000        1.6372  1.5977
+      2           0.2036        1.4788       0.0888           0.2000        1.6676  1.4921
+      3           0.2324        1.4033       0.0966           0.2102        1.6337  1.5023
+      4           0.2888        1.3407       0.1149           0.2465        1.6790  1.5318
+      5           0.3993        1.2126       0.4517           0.4013        1.6235  1.6800
+      6           0.5294        1.0967       0.2768           0.4076        1.6179  1.5747
+      7           0.5755        0.9662       0.5927           0.5671        1.3689  1.5097
+      8           0.6251        0.8904       0.7076           0.5786        1.2952  1.4943
+      9           0.6877        0.8148       0.7154           0.5876        1.3450  1.5740
+     10           0.6860        0.7449       0.5196           0.5269        1.6322  1.5012
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=Sequential(
+    (0): TimeDistributed(
+      (module): SleepStagerChambon2018(
+        (spatial_conv): Conv2d(1, 2, kernel_size=(2, 1), stride=(1, 1))
+        (feature_extractor): Sequential(
+          (0): Conv2d(1, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
+          (1): Identity()
+          (2): ReLU()
+          (3): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
+          (4): Conv2d(8, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
+          (5): Identity()
+          (6): ReLU()
+          (7): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
+        )
+      )
+    )
+    (1): Sequential(
+      (0): Flatten(start_dim=1, end_dim=-1)
+      (1): Dropout(p=0.5, inplace=False)
+      (2): Linear(in_features=816, out_features=5, bias=True)
+    )
+  ),
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span>
+                      <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Wake&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;REM&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_002.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+           0       0.14      0.33      0.20        61
+           1       0.19      0.14      0.16        21
+           2       0.56      0.34      0.42       194
+           3       0.26      0.27      0.26        75
+           4       0.03      0.03      0.03        32
+
+    accuracy                           0.28       383
+   macro avg       0.24      0.22      0.21       383
+weighted avg       0.37      0.28      0.31       383
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_chambon2018_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_chambon2018_003.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>Our model was able to learn despite the low amount of data that was available
+(only two recordings in this example) and reached a balanced accuracy of
+about 36% in a 5-class classification task (chance-level = 20%) on held-out
+data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To further improve performance, more recordings should be included in the
+training set, and hyperparameters should be selected accordingly.
+Increasing the sequence length was also shown in <a class="footnote-reference brackets" href="#id10" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to help improve
+performance, especially when few EEG channels are available.</p>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+(2018)A Deep Learning Architecture for Temporal Sleep Stage
+Classification Using Multivariate and Multimodal Time Series.
+IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+(758-769)</p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id12" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
+Circulation 101(23):e215-e220</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 59.132 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Applied examples on real-world datasets</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
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+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Applied examples on real-world datasets</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Sleep...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Eldele2021<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021" title="Link to this heading">#</a></h1>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We use the attention-based model from <a class="footnote-reference brackets" href="#id8" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> with the time distributed approach of <a class="footnote-reference brackets" href="#id9" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>
+to learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id10" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id11" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
+a lowpass filter.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f4526d57220&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.
+The Eldele2021 model takes a single channel as input. Here, the Fpz-Cz channel is used as it
+was found to give better performance than using the Pz-Oz channel</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">picks</span><span class="o">=</span><span class="s2">&quot;Fpz-Cz&quot;</span><span class="p">,</span>  <span class="c1"># the other option is Pz-Oz,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:130: UserWarning: Using reject or picks or flat or dropping bad windows means mne Epochs are created, which will be substantially slower and may be deprecated in the future.
+  warnings.warn(&#39;Using reject or picks or flat or dropping bad windows means &#39;
+</pre></div>
+</div>
+</section>
+<section id="window-preprocessing">
+<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Link to this heading">#</a></h3>
+<p>We also preprocess the windows by applying channel-wise z-score normalization
+in each window.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
+
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4526d14220&gt;
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id9" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows, such that the embeddings of
+multiple consecutive windows can be concatenated and provided to a final
+classifier. We can achieve this by defining Sampler objects that return
+sequences of window indices.
+To simplify the example, we train the whole model end-to-end on sequences,
+rather than using the two-step approach of <a class="footnote-reference brackets" href="#id9" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> (i.e. training the feature
+extractor on single windows, then freezing its weights and training the
+classifier).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  133
+Validation examples:  133
+</pre></div>
+</div>
+<p>We also implement a transform to extract the label of the center window of a
+sequence to use it as target.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<span class="c1"># Use label of center window in the sequence</span>
+<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
+        <span class="k">return</span> <span class="n">x</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting on the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the sleep
+staging architecture introduced in <a class="footnote-reference brackets" href="#id8" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is an attention-based
+neural network. We use the time distributed version of the model, where the
+feature vectors of a sequence of windows are concatenated and passed to a
+linear layer for classification.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to reproduce results</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+
+<a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a><span class="p">,</span>
+    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
+    <span class="p">)</span>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_acc    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  -----------  ---------------  ------------  ------
+      1           0.1471        3.1815       0.3459           0.1795        3.4748  2.2577
+      2           0.2712        1.8805       0.1955           0.2186        2.1537  1.9869
+      3           0.4550        1.4941       0.2256           0.2200        4.2159  2.0100
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=Sequential(
+    (0): TimeDistributed(
+      (module): SleepStagerEldele2021(
+        (feature_extractor): Sequential(
+          (0): _MRCNN(
+            (GELU): GELU(approximate=&#39;none&#39;)
+            (features1): Sequential(
+              (0): Conv1d(1, 64, kernel_size=(50,), stride=(6,), padding=(24,), bias=False)
+              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): GELU(approximate=&#39;none&#39;)
+              (3): MaxPool1d(kernel_size=8, stride=2, padding=4, dilation=1, ceil_mode=False)
+              (4): Dropout(p=0.5, inplace=False)
+              (5): Conv1d(64, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
+              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (7): GELU(approximate=&#39;none&#39;)
+              (8): Conv1d(128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
+              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (10): GELU(approximate=&#39;none&#39;)
+              (11): MaxPool1d(kernel_size=4, stride=4, padding=2, dilation=1, ceil_mode=False)
+            )
+            (features2): Sequential(
+              (0): Conv1d(1, 64, kernel_size=(400,), stride=(50,), padding=(200,), bias=False)
+              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): GELU(approximate=&#39;none&#39;)
+              (3): MaxPool1d(kernel_size=4, stride=2, padding=2, dilation=1, ceil_mode=False)
+              (4): Dropout(p=0.5, inplace=False)
+              (5): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
+              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (7): GELU(approximate=&#39;none&#39;)
+              (8): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
+              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (10): GELU(approximate=&#39;none&#39;)
+              (11): MaxPool1d(kernel_size=2, stride=2, padding=1, dilation=1, ceil_mode=False)
+            )
+            (dropout): Dropout(p=0.5, inplace=False)
+            (AFR): Sequential(
+              (0): _SEBasicBlock(
+                (conv1): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
+                (bn1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                (relu): ReLU(inplace=True)
+                (conv2): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
+                (bn2): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                (se): _SELayer(
+                  (avg_pool): AdaptiveAvgPool1d(output_size=1)
+                  (fc): Sequential(
+                    (0): Linear(in_features=30, out_features=1, bias=False)
+                    (1): ReLU(inplace=True)
+                    (2): Linear(in_features=1, out_features=30, bias=False)
+                    (3): Sigmoid()
+                  )
+                )
+                (downsample): Sequential(
+                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,), bias=False)
+                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                )
+                (features): Sequential(
+                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
+                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                  (2): ReLU(inplace=True)
+                  (3): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
+                  (4): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+                  (5): _SELayer(
+                    (avg_pool): AdaptiveAvgPool1d(output_size=1)
+                    (fc): Sequential(
+                      (0): Linear(in_features=30, out_features=1, bias=False)
+                      (1): ReLU(inplace=True)
+                      (2): Linear(in_features=1, out_features=30, bias=False)
+                      (3): Sigmoid()
+                    )
+                  )
+                )
+              )
+            )
+          )
+          (1): _TCE(
+            (layers): ModuleList(
+              (0-1): 2 x _EncoderLayer(
+                (self_attn): _MultiHeadedAttention(
+                  (convs): ModuleList(
+                    (0-2): 3 x _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
+                  )
+                  (linear): Linear(in_features=80, out_features=80, bias=True)
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+                (feed_forward): _PositionwiseFeedForward(
+                  (w_1): Linear(in_features=80, out_features=120, bias=True)
+                  (w_2): Linear(in_features=120, out_features=80, bias=True)
+                  (dropout): Dropout(p=0.1, inplace=False)
+                )
+                (sublayer_output): ModuleList(
+                  (0-1): 2 x _SublayerOutput(
+                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
+                    (dropout): Dropout(p=0.1, inplace=False)
+                  )
+                )
+                (conv): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
+              )
+            )
+            (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
+          )
+        )
+      )
+    )
+    (1): Sequential(
+      (0): Flatten(start_dim=1, end_dim=-1)
+      (1): Dropout(p=0.5, inplace=False)
+      (2): Linear(in_features=7200, out_features=5, bias=True)
+    )
+  ),
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">)</span>
+<span class="c1">#                      class_names=[&#39;Wake&#39;, &#39;N1&#39;, &#39;N2&#39;, &#39;N3&#39;, &#39;N4&#39;, &#39;REM&#39;])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_002.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+/home/runner/.local/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1471: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
+  _warn_prf(average, modifier, msg_start, len(result))
+              precision    recall  f1-score   support
+
+           0       1.00      0.02      0.03        60
+           1       0.00      0.00      0.00         6
+           2       0.00      0.00      0.00        48
+           3       0.15      1.00      0.26        19
+           4       0.00      0.00      0.00         0
+
+    accuracy                           0.15       133
+   macro avg       0.23      0.20      0.06       133
+weighted avg       0.47      0.15      0.05       133
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_eldele2021_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_eldele2021_003.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>The model was able to learn despite the low amount of data that was available
+(only two recordings in this example) and reached a balanced accuracy of
+about 43% in a 5-class classification task (chance-level = 20%) on held-out
+data over 10 epochs.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To further improve performance, the number of epochs should be increased.
+It has been reduced here for faster run-time in document generation. In
+testing, 10 epochs provided reasonable performance with around 89% balanced
+accuracy on training data and around 43% on held out validation data.
+Increasing the number of training recordings and optimizing the hyperparameters
+will also help increase performance</p>
+</div>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id8" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id7">2</a>)</span>
+<p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
+Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
+Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
+</aside>
+<aside class="footnote brackets" id="id9" role="note">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id5">2</a>,<a role="doc-backlink" href="#id6">3</a>)</span>
+<p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
+(2018)A Deep Learning Architecture for Temporal Sleep Stage
+Classification Using Multivariate and Multimodal Time Series.
+IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
+(758-769)</p>
+</aside>
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">4</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
+Circulation 101(23):e215-e220</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 12.905 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  12 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sleep_staging_eldele2021.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sleep_staging_eldele2021.ipynb</span></code></a></p>
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+</section>
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+        <p class="prev-next-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</p>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extract-windows">Extract windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#window-preprocessing">Window preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-sequence-samplers">Create sequence samplers</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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diff --git a/stable/auto_examples/applied_examples/plot_sleep_staging_usleep.html b/stable/auto_examples/applied_examples/plot_sleep_staging_usleep.html
new file mode 100644
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+
+<!DOCTYPE html>
+
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+<html lang="en" data-theme="light">
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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+    <title>Sleep staging on the Sleep Physionet dataset using U-Sleep network &#8212; Braindecode 0.8 documentation</title>
+  
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
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+        
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+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
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+  <ul class="bd-breadcrumbs">
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+      <a href="../../index.html" class="nav-link" aria-label="Home">
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+              
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+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network">
+<span id="sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"></span><h1>Sleep staging on the Sleep Physionet dataset using U-Sleep network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network" title="Link to this heading">#</a></h1>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Please take a look at the simpler sleep staging example
+<span class="xref std std-ref">here</span>
+before going through this example. The current example uses a more complex
+architecture and a sequence-to-sequence (seq2seq) approach.</p>
+</div>
+<p>This tutorial shows how to train and test a sleep staging neural network with
+Braindecode. We adapt the U-Sleep approach of <a class="footnote-reference brackets" href="#id10" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to learn on sequences of EEG
+windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id11" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> <a class="footnote-reference brackets" href="#id12" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The example is written to have a very short execution time.
+This number of epochs is here too small and very few recordings are used.
+To obtain competitive results you need to use more data and more epochs.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Theo Gnassounou &lt;theo.gnassounou@inria.fr&gt;</span>
+<span class="c1">#          Omar Chehab &lt;l-emir-omar.chehab@inria.fr&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3>Loading<a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data using the
+<a class="reference internal" href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
+two recordings from two different individuals: we will use the first one to
+train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
+sleep staging example).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
+<a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Next, we preprocess the raw data. We scale each channel recording-wise to
+have zero median and unit interquartile range. We don’t upsample to 128 Hz as
+done in <a class="footnote-reference brackets" href="#id10" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> so that we keep the example as light as possible. No filtering
+is described in <a class="footnote-reference brackets" href="#id10" id="id5" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f4542b6bbb0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extract-windows">
+<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Link to this heading">#</a></h3>
+<p>We extract 30-s windows to be used in the classification task.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
+    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+<span class="p">}</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We split the dataset into training and validation set taking
+every other subject as train or valid.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-sequence-samplers">
+<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Link to this heading">#</a></h2>
+<p>Following the sequence-to-sequence approach of <a class="footnote-reference brackets" href="#id10" id="id6" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, we need to provide our
+neural network with sequences of windows. We can achieve this by defining
+Sampler objects that return sequences of windows.
+Non-overlapping sequences of 35 windows are used in <a class="footnote-reference brackets" href="#id10" id="id7" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, however to limit
+the memory requirements for this example we use shorter sequences of 3
+windows.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows; originally 35 in paper</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Non-overlapping sequences</span>
+
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">,</span> <span class="n">randomize</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+<a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
+
+<span class="c1"># Print number of examples per class</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>133
+133
+</pre></div>
+</div>
+<p>Finally, since some sleep stages appear a lot more often than others (e.g.
+most of the night is spent in the N2 stage), the classes are imbalanced. To
+avoid overfitting to the more frequent classes, we compute weights that we
+will provide to the loss function when training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h2>Create model<a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>We can now create the deep learning model. In this tutorial, we use the
+U-Sleep architecture introduced in <a class="footnote-reference brackets" href="#id10" id="id8" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, which is fully convolutional
+neural network.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of channels and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
+<a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a><span class="p">(</span>
+    <span class="n">n_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
+    <span class="n">depth</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
+    <span class="n">with_skip_connection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a>
+<span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
+braindecode object that is responsible for managing the training of neural
+networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
+training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id10" id="id9" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, as these hyperparameters were
+optimized on different datasets and with a different number of recordings.
+Generally speaking, it is recommended to perform hyperparameter
+optimization if reusing this code on a different dataset or with more
+recordings.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
+
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a>
+
+
+<span class="k">def</span> <span class="nf">balanced_accuracy_multi</span><span class="p">(</span><a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+    <span class="k">return</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+
+
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
+    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
+    <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
+    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
+    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
+    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Deactivate the default valid_acc callback:</span>
+<a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.set_params" title="skorch.net.NeuralNet.set_params" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">set_params</span></a><span class="p">(</span><span class="n">callbacks__valid_acc</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
+<span class="c1"># supplied in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:306: UserWarning: Using padding=&#39;same&#39; with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at ../aten/src/ATen/native/Convolution.cpp:1008.)
+  return F.conv1d(input, weight, bias, self.stride,
+  epoch    train_bal_acc    train_loss    valid_bal_acc    valid_loss     dur
+-------  ---------------  ------------  ---------------  ------------  ------
+      1           0.2040        1.6129           0.1707        1.5807  2.1639
+      2           0.2224        1.5430           0.1594        1.5866  1.7210
+      3           0.3464        1.4928           0.1853        1.6026  1.7986
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=======================================================================================================================================================
+  Layer (type (var_name):depth-idx)                  Input Shape               Output Shape              Param #                   Kernel Shape
+  ======================================================================================================================================================
+  USleep (USleep)                                    [1, 2, 3000]              [1, 5]                    --                        --
+  ├─Sequential (encoder): 1-1                        --                        --                        --                        --
+  │    └─_EncoderBlock (0): 2-1                      [1, 2, 3000]              [1, 6, 1500]              --                        7
+  │    │    └─Sequential (block_prepool): 3-1        [1, 2, 3000]              [1, 6, 3000]              102                       --
+  │    │    └─MaxPool1d (maxpool): 3-2               [1, 6, 3000]              [1, 6, 1500]              --                        2
+  │    └─_EncoderBlock (1): 2-2                      [1, 6, 1500]              [1, 9, 750]               --                        7
+  │    │    └─Sequential (block_prepool): 3-3        [1, 6, 1500]              [1, 9, 1500]              405                       --
+  │    │    └─MaxPool1d (maxpool): 3-4               [1, 9, 1500]              [1, 9, 750]               --                        2
+  │    └─_EncoderBlock (2): 2-3                      [1, 9, 750]               [1, 11, 375]              --                        7
+  │    │    └─Sequential (block_prepool): 3-5        [1, 9, 750]               [1, 11, 750]              726                       --
+  │    │    └─MaxPool1d (maxpool): 3-6               [1, 11, 750]              [1, 11, 375]              --                        2
+  │    └─_EncoderBlock (3): 2-4                      [1, 11, 375]              [1, 15, 188]              --                        7
+  │    │    └─Sequential (block_prepool): 3-7        [1, 11, 375]              [1, 15, 375]              1,200                     --
+  │    │    └─ConstantPad1d (pad): 3-8               [1, 15, 375]              [1, 15, 377]              --                        --
+  │    │    └─MaxPool1d (maxpool): 3-9               [1, 15, 377]              [1, 15, 188]              --                        2
+  │    └─_EncoderBlock (4): 2-5                      [1, 15, 188]              [1, 20, 94]               --                        7
+  │    │    └─Sequential (block_prepool): 3-10       [1, 15, 188]              [1, 20, 188]              2,160                     --
+  │    │    └─MaxPool1d (maxpool): 3-11              [1, 20, 188]              [1, 20, 94]               --                        2
+  │    └─_EncoderBlock (5): 2-6                      [1, 20, 94]               [1, 28, 47]               --                        7
+  │    │    └─Sequential (block_prepool): 3-12       [1, 20, 94]               [1, 28, 94]               4,004                     --
+  │    │    └─MaxPool1d (maxpool): 3-13              [1, 28, 94]               [1, 28, 47]               --                        2
+  │    └─_EncoderBlock (6): 2-7                      [1, 28, 47]               [1, 40, 24]               --                        7
+  │    │    └─Sequential (block_prepool): 3-14       [1, 28, 47]               [1, 40, 47]               7,960                     --
+  │    │    └─ConstantPad1d (pad): 3-15              [1, 40, 47]               [1, 40, 49]               --                        --
+  │    │    └─MaxPool1d (maxpool): 3-16              [1, 40, 49]               [1, 40, 24]               --                        2
+  │    └─_EncoderBlock (7): 2-8                      [1, 40, 24]               [1, 55, 12]               --                        7
+  │    │    └─Sequential (block_prepool): 3-17       [1, 40, 24]               [1, 55, 24]               15,565                    --
+  │    │    └─MaxPool1d (maxpool): 3-18              [1, 55, 24]               [1, 55, 12]               --                        2
+  │    └─_EncoderBlock (8): 2-9                      [1, 55, 12]               [1, 77, 6]                --                        7
+  │    │    └─Sequential (block_prepool): 3-19       [1, 55, 12]               [1, 77, 12]               29,876                    --
+  │    │    └─MaxPool1d (maxpool): 3-20              [1, 77, 12]               [1, 77, 6]                --                        2
+  │    └─_EncoderBlock (9): 2-10                     [1, 77, 6]                [1, 108, 3]               --                        7
+  │    │    └─Sequential (block_prepool): 3-21       [1, 77, 6]                [1, 108, 6]               58,536                    --
+  │    │    └─MaxPool1d (maxpool): 3-22              [1, 108, 6]               [1, 108, 3]               --                        2
+  │    └─_EncoderBlock (10): 2-11                    [1, 108, 3]               [1, 152, 2]               --                        7
+  │    │    └─Sequential (block_prepool): 3-23       [1, 108, 3]               [1, 152, 3]               115,368                   --
+  │    │    └─ConstantPad1d (pad): 3-24              [1, 152, 3]               [1, 152, 5]               --                        --
+  │    │    └─MaxPool1d (maxpool): 3-25              [1, 152, 5]               [1, 152, 2]               --                        2
+  │    └─_EncoderBlock (11): 2-12                    [1, 152, 2]               [1, 214, 1]               --                        7
+  │    │    └─Sequential (block_prepool): 3-26       [1, 152, 2]               [1, 214, 2]               228,338                   --
+  │    │    └─MaxPool1d (maxpool): 3-27              [1, 214, 2]               [1, 214, 1]               --                        2
+  ├─Sequential (bottom): 1-2                         [1, 214, 1]               [1, 302, 1]               --                        --
+  │    └─Conv1d (0): 2-13                            [1, 214, 1]               [1, 302, 1]               452,698                   [7]
+  │    └─ELU (1): 2-14                               [1, 302, 1]               [1, 302, 1]               --                        --
+  │    └─BatchNorm1d (2): 2-15                       [1, 302, 1]               [1, 302, 1]               604                       --
+  ├─Sequential (decoder): 1-3                        --                        --                        --                        --
+  │    └─_DecoderBlock (0): 2-16                     [1, 302, 1]               [1, 214, 2]               --                        7
+  │    │    └─Sequential (block_preskip): 3-28       [1, 302, 1]               [1, 214, 2]               129,898                   --
+  │    │    └─Sequential (block_postskip): 3-29      [1, 428, 2]               [1, 214, 2]               641,786                   --
+  │    └─_DecoderBlock (1): 2-17                     [1, 214, 2]               [1, 152, 3]               --                        7
+  │    │    └─Sequential (block_preskip): 3-30       [1, 214, 2]               [1, 152, 4]               65,512                    --
+  │    │    └─Sequential (block_postskip): 3-31      [1, 304, 3]               [1, 152, 3]               323,912                   --
+  │    └─_DecoderBlock (2): 2-18                     [1, 152, 3]               [1, 108, 6]               --                        7
+  │    │    └─Sequential (block_preskip): 3-32       [1, 152, 3]               [1, 108, 6]               33,156                    --
+  │    │    └─Sequential (block_postskip): 3-33      [1, 216, 6]               [1, 108, 6]               163,620                   --
+  │    └─_DecoderBlock (3): 2-19                     [1, 108, 6]               [1, 77, 12]               --                        7
+  │    │    └─Sequential (block_preskip): 3-34       [1, 108, 6]               [1, 77, 12]               16,863                    --
+  │    │    └─Sequential (block_postskip): 3-35      [1, 154, 12]              [1, 77, 12]               83,237                    --
+  │    └─_DecoderBlock (4): 2-20                     [1, 77, 12]               [1, 55, 24]               --                        7
+  │    │    └─Sequential (block_preskip): 3-36       [1, 77, 12]               [1, 55, 24]               8,635                     --
+  │    │    └─Sequential (block_postskip): 3-37      [1, 110, 24]              [1, 55, 24]               42,515                    --
+  │    └─_DecoderBlock (5): 2-21                     [1, 55, 24]               [1, 40, 47]               --                        7
+  │    │    └─Sequential (block_preskip): 3-38       [1, 55, 24]               [1, 40, 48]               4,520                     --
+  │    │    └─Sequential (block_postskip): 3-39      [1, 80, 47]               [1, 40, 47]               22,520                    --
+  │    └─_DecoderBlock (6): 2-22                     [1, 40, 47]               [1, 28, 94]               --                        7
+  │    │    └─Sequential (block_preskip): 3-40       [1, 40, 47]               [1, 28, 94]               2,324                     --
+  │    │    └─Sequential (block_postskip): 3-41      [1, 56, 94]               [1, 28, 94]               11,060                    --
+  │    └─_DecoderBlock (7): 2-23                     [1, 28, 94]               [1, 20, 188]              --                        7
+  │    │    └─Sequential (block_preskip): 3-42       [1, 28, 94]               [1, 20, 188]              1,180                     --
+  │    │    └─Sequential (block_postskip): 3-43      [1, 40, 188]              [1, 20, 188]              5,660                     --
+  │    └─_DecoderBlock (8): 2-24                     [1, 20, 188]              [1, 15, 375]              --                        7
+  │    │    └─Sequential (block_preskip): 3-44       [1, 20, 188]              [1, 15, 376]              645                       --
+  │    │    └─Sequential (block_postskip): 3-45      [1, 30, 375]              [1, 15, 375]              3,195                     --
+  │    └─_DecoderBlock (9): 2-25                     [1, 15, 375]              [1, 11, 750]              --                        7
+  │    │    └─Sequential (block_preskip): 3-46       [1, 15, 375]              [1, 11, 750]              363                       --
+  │    │    └─Sequential (block_postskip): 3-47      [1, 22, 750]              [1, 11, 750]              1,727                     --
+  │    └─_DecoderBlock (10): 2-26                    [1, 11, 750]              [1, 9, 1500]              --                        7
+  │    │    └─Sequential (block_preskip): 3-48       [1, 11, 750]              [1, 9, 1500]              225                       --
+  │    │    └─Sequential (block_postskip): 3-49      [1, 18, 1500]             [1, 9, 1500]              1,161                     --
+  │    └─_DecoderBlock (11): 2-27                    [1, 9, 1500]              [1, 6, 3000]              --                        7
+  │    │    └─Sequential (block_preskip): 3-50       [1, 9, 1500]              [1, 6, 3000]              126                       --
+  │    │    └─Sequential (block_postskip): 3-51      [1, 12, 3000]             [1, 6, 3000]              522                       --
+  ├─Sequential (clf): 1-4                            [1, 6, 3000]              [1, 6, 1]                 --                        --
+  │    └─Conv1d (0): 2-28                            [1, 6, 3000]              [1, 6, 3000]              42                        [1]
+  │    └─Tanh (1): 2-29                              [1, 6, 3000]              [1, 6, 3000]              --                        --
+  │    └─AvgPool1d (2): 2-30                         [1, 6, 3000]              [1, 6, 1]                 --                        [3000]
+  ├─Sequential (final_layer): 1-5                    [1, 6, 1]                 [1, 5, 1]                 --                        --
+  │    └─Conv1d (0): 2-31                            [1, 6, 1]                 [1, 5, 1]                 35                        [1]
+  │    └─ELU (1): 2-32                               [1, 5, 1]                 [1, 5, 1]                 --                        --
+  │    └─Conv1d (2): 2-33                            [1, 5, 1]                 [1, 5, 1]                 30                        [1]
+  │    └─Identity (3): 2-34                          [1, 5, 1]                 [1, 5, 1]                 --                        --
+  ======================================================================================================================================================
+  Total params: 2,482,011
+  Trainable params: 2,482,011
+  Non-trainable params: 0
+  Total mult-adds (M): 22.43
+  ======================================================================================================================================================
+  Input size (MB): 0.02
+  Forward/backward pass size (MB): 2.91
+  Params size (MB): 9.93
+  Estimated Total Size (MB): 12.86
+  ======================================================================================================================================================,
+)
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2>Plot results<a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<p>We use the history stored by Skorch during training to plot the performance of
+the model throughout training. Specifically, we plot the loss and the balanced
+balanced accuracy for the training and validation sets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">)</span>
+<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_001.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_001.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <a href="../../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span>
+                      <span class="n">class_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Wake&#39;</span><span class="p">,</span> <span class="s1">&#39;N1&#39;</span><span class="p">,</span> <span class="s1">&#39;N2&#39;</span><span class="p">,</span> <span class="s1">&#39;N3&#39;</span><span class="p">,</span> <span class="s1">&#39;REM&#39;</span><span class="p">])</span>
+
+<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_002.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_002.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>              precision    recall  f1-score   support
+
+           0       0.12      0.31      0.17        64
+           1       0.03      0.05      0.04        22
+           2       0.52      0.34      0.41       197
+           3       0.26      0.20      0.23        84
+           4       0.25      0.03      0.06        32
+
+    accuracy                           0.26       399
+   macro avg       0.24      0.19      0.18       399
+weighted avg       0.35      0.26      0.28       399
+</pre></div>
+</div>
+<p>Finally, we can also visualize the hypnogram of the recording we used for
+validation, with the predicted sleep stages overlaid on top of the true
+sleep stages. We can see that the model cannot correctly identify the
+different sleep stages with this amount of training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Expert annotations&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Predict annotations&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Time (epochs)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Sleep stage&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_sleep_staging_usleep_003.png" srcset="../../_images/sphx_glr_plot_sleep_staging_usleep_003.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(150.22222222222223, 0.5, &#39;Sleep stage&#39;)
+</pre></div>
+</div>
+<p>Our model was able to learn, as shown by the decreasing training and
+validation loss values, despite the low amount of data that was available
+(only two recordings in this example). To further improve performance, more
+recordings should be included in the training set, the model should be
+trained for more epochs and hyperparameters should be optimized.</p>
+</section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id10" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
+<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
+</aside>
+<aside class="footnote brackets" id="id11" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
+a sleep-dependent neuronal feedback loop: the slow-wave
+microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
+</aside>
+<aside class="footnote brackets" id="id12" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
+Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
+PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
+Research Resource for Complex Physiologic Signals.
+Circulation 101(23):e215-e220</p>
+</aside>
+</aside>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Applied examples on real-world datasets</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="process-a-big-data-eeg-resource-tuh-eeg-corpus">
+<span id="sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"></span><h1>Process a big data EEG resource (TUH EEG Corpus)<a class="headerlink" href="#process-a-big-data-eeg-resource-tuh-eeg-corpus" title="Link to this heading">#</a></h1>
+<p>In this example, we showcase usage of the Temple University Hospital EEG Corpus
+(<a class="reference external" href="https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg">https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg</a>)
+including simple preprocessing steps as well as cutting of compute windows.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#creating-the-dataset-using-tuhmock" id="id1">Creating the dataset using TUHMock</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id2">Preprocessing</a></p>
+<ul>
+<li><p><a class="reference internal" href="#selecting-recordings" id="id3">Selecting recordings</a></p></li>
+<li><p><a class="reference internal" href="#combining-preprocessing-steps" id="id4">Combining preprocessing steps</a></p></li>
+<li><p><a class="reference internal" href="#cut-compute-windows" id="id5">Cut Compute Windows</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">tempfile</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
+</pre></div>
+</div>
+<section id="creating-the-dataset-using-tuhmock">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Creating the dataset using TUHMock</a><a class="headerlink" href="#creating-the-dataset-using-tuhmock" title="Link to this heading">#</a></h2>
+<p>Since the data is not available at the time of the creation of this example,
+we are required to mock some of the dataset functionality. Therefore, if you
+want to try this code with the actual data, please disconsider this section.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
+</pre></div>
+</div>
+<p>Firstly, we start by creating a TUH mock dataset using braindecode’s _TUHMock class.
+The complete code can be found at <a class="reference internal" href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datasets.TUH()</span></code></a>, but we will give
+a small description of how it works.
+This class is able to read the recordings from TUH_PATH and generate a description
+by parsing information from file paths, such as patient id and recording data.
+THis description can later be accessed by the object’s .description method.
+After that, the files are sorted chronologically by year, month, day,
+patient id, recording session and segment, and then use the description corresponding
+to the specified by recording ids.
+FInally, additional information regarding age and gender of the subjects are parsed
+directly to the description.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
+<span class="c1"># specify the number of jobs for loading and windowing</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a> <span class="o">=</span> <span class="mi">2</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
+    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span> <span class="k">if</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;_TUHMock&#39;</span> <span class="k">else</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We can visualize our data’s statistics using the class’ “description” method</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plt_histogram</span><span class="p">(</span><span class="n">df_of_ages_genders</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">fs</span><span class="o">=</span><span class="mi">24</span><span class="p">,</span> <span class="n">ylim</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">show_title</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="c1"># Dafarame containing info about gender and age of subjects</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df_of_ages_genders</span>
+    <span class="n">male_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;M&#39;</span><span class="p">]</span>
+    <span class="n">female_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;gender&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;F&#39;</span><span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">18</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">show_title</span><span class="p">:</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html#matplotlib.pyplot.suptitle" title="matplotlib.pyplot.suptitle" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">suptitle</span></a><span class="p">(</span><span class="s2">&quot;Age information&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">+</span> <span class="mi">5</span><span class="p">)</span>
+
+    <span class="c1"># First plot: Male individuals</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span>
+             <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html#matplotlib.pyplot.axhline" title="matplotlib.pyplot.axhline" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axhline</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;mean age </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> &quot;</span>
+                      <span class="sa">f</span><span class="s2">&quot;(±</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html#matplotlib.pyplot.barh" title="matplotlib.pyplot.barh" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">height</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">male_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span>
+             <span class="n">width</span><span class="o">=</span><span class="n">ylim</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+    <span class="c1"># Legend</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ylim</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper left&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;male (</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">male_df</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%)&quot;</span><span class="p">,</span>
+              <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.gca.html#matplotlib.pyplot.gca" title="matplotlib.pyplot.gca" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">gca</span></a><span class="p">()</span><span class="o">.</span><span class="n">invert_xaxis</span><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tick_params.html#matplotlib.pyplot.tick_params" title="matplotlib.pyplot.tick_params" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+
+    <span class="c1"># First plot: Female individuals</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="matplotlib.pyplot.subplot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">101</span><span class="p">),</span>
+             <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;orange&quot;</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s2">&quot;horizontal&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.axhline.html#matplotlib.pyplot.axhline" title="matplotlib.pyplot.axhline" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">axhline</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span>
+                <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;mean age </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> (&quot;</span>
+                      <span class="sa">f</span><span class="s2">&quot;±</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">])</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.barh.html#matplotlib.pyplot.barh" title="matplotlib.pyplot.barh" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span> <span class="n">height</span><span class="o">=</span><span class="mi">2</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><span class="n">female_df</span><span class="p">[</span><span class="s2">&quot;age&quot;</span><span class="p">]),</span>
+             <span class="n">width</span><span class="o">=</span><span class="n">ylim</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;black&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+    <span class="c1"># Label</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;upper right&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html#matplotlib.pyplot.xlim" title="matplotlib.pyplot.xlim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ylim</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;female (</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">female_df</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%)&quot;</span><span class="p">,</span>
+              <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;right&quot;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html#matplotlib.pyplot.ylim" title="matplotlib.pyplot.ylim" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="matplotlib.pyplot.subplots_adjust" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s2">&quot;age [years]&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s2">&quot;count&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">labelpad</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="matplotlib.pyplot.yticks" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">11</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tick_params.html#matplotlib.pyplot.tick_params" title="matplotlib.pyplot.tick_params" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="n">fs</span> <span class="o">-</span> <span class="mi">5</span><span class="p">)</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+
+
+<span class="n">df</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="o">.</span><span class="n">description</span>
+<span class="n">plt_histogram</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" srcset="../../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" alt="Age information, male (60.0%), female (40.0%)" class = "sphx-glr-single-img"/></section>
+<section id="preprocessing">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h2>
+<section id="selecting-recordings">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Selecting recordings</a><a class="headerlink" href="#selecting-recordings" title="Link to this heading">#</a></h3>
+<p>First, we will do some selection of available recordings based on the duration.
+We will select those recordings that have at least five minutes duration.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">select_by_duration</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">inf</span></a>
+    <span class="c1"># determine length of the recordings and select based on tmin and tmax</span>
+    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">d_i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
+        <span class="n">duration</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">/</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+        <span class="c1"># select the ones in the required duration range</span>
+        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">&lt;=</span> <span class="n">duration</span> <span class="o">&lt;=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">:</span>
+            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d_i</span><span class="p">)</span>
+    <span class="n">splits</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)</span>
+    <span class="n">split</span> <span class="o">=</span> <span class="n">splits</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">split</span>
+
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="kc">None</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_duration</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>Next, we will discard all recordings that have an incomplete channel
+configuration on the channels that we are interested. The subdivisions of the
+recordings into ‘le’ and ‘ar’ labels represents the channels for
+the re-referencing of the signals.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;FP1&#39;</span><span class="p">,</span> <span class="s1">&#39;FP2&#39;</span><span class="p">,</span> <span class="s1">&#39;F3&#39;</span><span class="p">,</span> <span class="s1">&#39;F4&#39;</span><span class="p">,</span> <span class="s1">&#39;C3&#39;</span><span class="p">,</span> <span class="s1">&#39;C4&#39;</span><span class="p">,</span> <span class="s1">&#39;P3&#39;</span><span class="p">,</span> <span class="s1">&#39;P4&#39;</span><span class="p">,</span> <span class="s1">&#39;O1&#39;</span><span class="p">,</span> <span class="s1">&#39;O2&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;F7&#39;</span><span class="p">,</span> <span class="s1">&#39;F8&#39;</span><span class="p">,</span> <span class="s1">&#39;T3&#39;</span><span class="p">,</span> <span class="s1">&#39;T4&#39;</span><span class="p">,</span> <span class="s1">&#39;T5&#39;</span><span class="p">,</span> <span class="s1">&#39;T6&#39;</span><span class="p">,</span> <span class="s1">&#39;FZ&#39;</span><span class="p">,</span> <span class="s1">&#39;CZ&#39;</span><span class="p">,</span> <span class="s1">&#39;PZ&#39;</span><span class="p">])</span>
+
+<span class="c1"># TUH data is subdivided into &#39;le&#39; and &#39;ar&#39; recordings references</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;EEG A1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG FP1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG C4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG F7-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-REF&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG T6-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-REF&#39;</span><span class="p">])</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
+    <span class="s1">&#39;EEG A1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG FP1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG C4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG F7-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-LE&#39;</span><span class="p">,</span>
+    <span class="s1">&#39;EEG T6-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-LE&#39;</span><span class="p">])</span>
+<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ar&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a><span class="p">,</span> <span class="s1">&#39;le&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a><span class="p">}</span>
+
+
+<span class="k">def</span> <span class="nf">select_by_channels</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">):</span>
+    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
+        <span class="n">ref</span> <span class="o">=</span> <span class="s1">&#39;ar&#39;</span> <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;-REF&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;le&#39;</span>
+        <span class="c1"># these are the channels we are looking for</span>
+        <span class="n">seta</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">[</span><span class="n">ref</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+        <span class="c1"># these are the channels of the recoding</span>
+        <span class="n">setb</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">)</span>
+        <span class="c1"># if recording contains all channels we are looking for, include it</span>
+        <span class="k">if</span> <span class="n">seta</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">setb</span><span class="p">):</span>
+            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
+
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_channels</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="combining-preprocessing-steps">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Combining preprocessing steps</a><a class="headerlink" href="#combining-preprocessing-steps" title="Link to this heading">#</a></h3>
+<p>Next, we use braindecode’s preprocess to combine and execute several preprocessing
+steps that are executed through ‘mne’:</p>
+<ul class="simple">
+<li><p>Crop the recordings to a region of interest</p></li>
+<li><p>Re-reference all recordings to ‘ar’ (requires load)</p></li>
+<li><p>Rename channels to short channel names</p></li>
+<li><p>Pick channels of interest</p></li>
+<li><p>Scale signals to micro volts (requires load)</p></li>
+<li><p>Clip outlier values to +/- 800 micro volts (requires load)</p></li>
+<li><p>Resample recordings to a common frequency (requires load)</p></li>
+</ul>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">custom_rename_channels</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">mapping</span><span class="p">):</span>
+    <span class="c1"># rename channels which are dependent on referencing:</span>
+    <span class="c1"># le: EEG 01-LE, ar: EEG 01-REF</span>
+    <span class="c1"># mne fails if the mapping contains channels as keys that are not present</span>
+    <span class="c1"># in the raw</span>
+    <span class="n">reference</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+    <span class="k">assert</span> <span class="n">reference</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;le&#39;</span><span class="p">,</span> <span class="s1">&#39;ref&#39;</span><span class="p">],</span> <span class="s1">&#39;unexpected referencing&#39;</span>
+    <span class="n">reference</span> <span class="o">=</span> <span class="s1">&#39;le&#39;</span> <span class="k">if</span> <span class="n">reference</span> <span class="o">==</span> <span class="s1">&#39;le&#39;</span> <span class="k">else</span> <span class="s1">&#39;ar&#39;</span>
+    <span class="n">raw</span><span class="o">.</span><span class="n">rename_channels</span><span class="p">(</span><span class="n">mapping</span><span class="p">[</span><span class="n">reference</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">custom_crop</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+    <span class="c1"># crop recordings to tmin – tmax. can be incomplete if recording</span>
+    <span class="c1"># has lower duration than tmax</span>
+    <span class="c1"># by default mne fails if tmax is bigger than duration</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="nb">min</span><span class="p">((</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
+    <span class="n">raw</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="n">include_tmax</span><span class="p">)</span>
+
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">60</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_crop</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;set_eeg_reference&#39;</span><span class="p">,</span> <span class="n">ref_channels</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">,</span> <span class="n">ch_type</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_rename_channels</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">,</span>
+                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_channels&#39;</span><span class="p">,</span> <span class="n">ch_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">,</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">),</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.clip.html#numpy.clip" title="numpy.clip" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">clip</span></a><span class="p">,</span> <span class="n">a_min</span><span class="o">=-</span><span class="mi">800</span><span class="p">,</span> <span class="n">a_max</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">),</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+</pre></div>
+</div>
+<p>Next, we can apply the defined preprocessors on the selected recordings in parallel.
+We additionally use the serialization functionality of
+<a class="reference internal" href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> to limit memory usage during
+preprocessing, as each file must be loaded into memory for some of the
+preprocessing steps to work.
+This also makes it possible to use the lazy
+loading capabilities of <a class="reference internal" href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a>, as
+the preprocessed data is automatically reloaded with <code class="docutils literal notranslate"><span class="pre">preload=False</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Here we use <code class="docutils literal notranslate"><span class="pre">n_jobs=2</span></code> as the machines the documentation is build on
+only have two cores. This number should be modified based on the machine
+that is available for preprocessing.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># please insert actual output directory here</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
+    <span class="n">save_dir</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="cut-compute-windows">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Cut Compute Windows</a><a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<p>We can finally generate compute windows. The resulting dataset is now ready
+to be used for model training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Generate compute windows here and store them to disk</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.981 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  12 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applied-examples-plot-tuh-eeg-corpus-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tuh_eeg_corpus.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tuh_eeg_corpus.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+</section>
+</section>
+
+
+  
+</div>
+
+              
+              
+              
+              
+                <footer class="prev-next-footer">
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+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
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+              
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+  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-applied-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>02:55.372</strong> total execution time for <strong>auto_examples_applied_examples</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_chambon2018.py</span></code>)</p></td>
+<td><p>01:59.132</p></td>
+<td><p>9.6 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code>)</p></td>
+<td><p>00:25.104</p></td>
+<td><p>214.3 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_eldele2021.py</span></code>)</p></td>
+<td><p>00:12.905</p></td>
+<td><p>11.5 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_usleep.py</span></code>)</p></td>
+<td><p>00:11.250</p></td>
+<td><p>92.4 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_eeg_corpus.py</span></code>)</p></td>
+<td><p>00:06.981</p></td>
+<td><p>11.7 MB</p></td>
+</tr>
+</tbody>
+</table>
+</section>
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+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Benchmarking...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-eager-and-lazy-loading">
+<span id="sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"></span><h1>Benchmarking eager and lazy loading<a class="headerlink" href="#benchmarking-eager-and-lazy-loading" title="Link to this heading">#</a></h1>
+<p>In this example, we compare the execution time and memory requirements of 1)
+eager loading, i.e., preloading the entire data into memory and 2) lazy loading,
+i.e., only loading examples from disk when they are required. We also include
+some other experiment parameters in the comparison for the sake of completeness
+(e.g., <cite>num_workers</cite>, <cite>cuda</cite>, <cite>batch_size</cite>, etc.).</p>
+<p>While eager loading might be required for some preprocessing steps that require
+continuous data (e.g., temporal filtering, resampling), it also allows
+fast access to the data during training. However, this might come at the expense
+of large memory usage, and can ultimately become impossible if the dataset does
+not fit into memory (e.g., the TUH EEG dataset’s &gt;1,5 TB of recordings will
+not fit in the memory of most machines).</p>
+<p>Lazy loading avoids this potential memory issue by loading examples from disk
+when they are required. This means large datasets can be used for training,
+however this introduces some file-reading overhead every time an example must
+be extracted. Some preprocessing steps that require continuous data also have to
+be implemented differently to accommodate the nature of windowed data. Overall
+though, we can reduce the impact of lazy loading by using the <cite>num_workers</cite>
+parameter of pytorch’s <cite>Dataloader</cite> class, which dispatches the data loading to
+multiple processes.</p>
+<p>To enable lazy loading in braindecode, data files must be saved in an
+MNE-compatible format (e.g., ‘fif’, ‘edf’, etc.), and the <cite>Dataset</cite> object must
+have been instantiated with parameter <cite>preload=False</cite>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
+<span class="kn">import</span> <span class="nn">time</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span><span class="p">,</span> <span class="n">optim</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DataLoader</span></a>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a>
+
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;WARNING&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
+</pre></div>
+</div>
+<p>We start by setting two pytorch internal parameters that can affect the
+comparison:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">..</span> <span class="n">GENERATED</span> <span class="n">FROM</span> <span class="n">PYTHON</span> <span class="n">SOURCE</span> <span class="n">LINES</span> <span class="mi">57</span><span class="o">-</span><span class="mi">62</span>
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">N_JOBS</span> <span class="o">=</span> <span class="mi">8</span>
+<a href="https://pytorch.org/docs/stable/backends.html#torch.backends.cudnn.benchmark" title="torch.backends.cudnn.benchmark" class="sphx-glr-backref-module-torch-backends-cudnn sphx-glr-backref-type-py-attribute"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>  <span class="c1"># Enables automatic algorithm optimizations</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.set_num_threads.html#torch.set_num_threads" title="torch.set_num_threads" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span></a><span class="p">(</span><span class="n">N_JOBS</span><span class="p">)</span>  <span class="c1"># Sets the available number of threads</span>
+</pre></div>
+</div>
+<p>Next, we define a few functions to automate the benchmarking.
+For the purpose of this example, we load some recordings from the TUH Abnormal
+corpus, extract sliding windows, and bundle them in a braindecode Dataset.
+We then train a neural network for a few epochs.</p>
+<p>Each one of these steps will be timed, so we can report the total time taken
+to prepare the data and train the model.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">load_example_data</span><span class="p">(</span><span class="n">preload</span><span class="p">,</span> <span class="n">window_len_s</span><span class="p">,</span> <span class="n">n_recordings</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create windowed dataset from subjects of the TUH Abnormal dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    preload: bool</span>
+<span class="sd">        If True, use eager loading, otherwise use lazy loading.</span>
+<span class="sd">    window_len_s: int</span>
+<span class="sd">        Window length in seconds.</span>
+<span class="sd">    n_recordings: list of int</span>
+<span class="sd">        Number of recordings to load.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    windows_ds: BaseConcatDataset</span>
+<span class="sd">        Windowed data.</span>
+
+<span class="sd">    .. warning::</span>
+<span class="sd">        The recordings from the TUH Abnormal corpus do not all share the same</span>
+<span class="sd">        sampling rate. The following assumes that the files have already been</span>
+<span class="sd">        resampled to a common sampling rate.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">recording_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_recordings</span><span class="p">))</span>
+
+    <span class="n">ds</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUHAbnormal.html#braindecode.datasets.TUHAbnormal" title="braindecode.datasets.TUHAbnormal" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUHAbnormal</span></a><span class="p">(</span>
+        <span class="n">TUH_PATH</span><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="n">recording_ids</span><span class="p">,</span>
+        <span class="n">target_name</span><span class="o">=</span><span class="s1">&#39;pathological&#39;</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
+
+    <span class="n">fs</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+    <span class="n">window_len_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs</span> <span class="o">*</span> <span class="n">window_len_s</span><span class="p">)</span>
+    <span class="n">window_stride_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs</span> <span class="o">*</span> <span class="mi">4</span><span class="p">)</span>
+    <span class="c1"># window_stride_samples = int(fs * window_len_s)</span>
+    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+        <span class="n">ds</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">window_size_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
+        <span class="n">window_stride_samples</span><span class="o">=</span><span class="n">window_stride_samples</span><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
+
+    <span class="c1"># Drop bad epochs</span>
+    <span class="c1"># XXX: This could be parallelized.</span>
+    <span class="c1"># XXX: Also, this could be implemented in the Dataset object itself.</span>
+    <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <span class="n">windows_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">:</span>
+        <span class="n">ds</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">drop_bad</span><span class="p">()</span>
+        <span class="k">assert</span> <span class="n">ds</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">preload</span> <span class="o">==</span> <span class="n">preload</span>
+
+    <span class="k">return</span> <span class="n">windows_ds</span>
+
+
+<span class="k">def</span> <span class="nf">create_example_model</span><span class="p">(</span><span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">window_len_samples</span><span class="p">,</span>
+                         <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;shallow&#39;</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create model, loss and optimizer.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_channels : int</span>
+<span class="sd">        Number of channels in the input</span>
+<span class="sd">    n_times : int</span>
+<span class="sd">        Window length in the input</span>
+<span class="sd">    n_classes : int</span>
+<span class="sd">        Number of classes in the output</span>
+<span class="sd">    kind : str</span>
+<span class="sd">        &#39;shallow&#39; or &#39;deep&#39;</span>
+<span class="sd">    cuda : bool</span>
+<span class="sd">        If True, move the model to a CUDA device.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    model : torch.nn.Module</span>
+<span class="sd">        Model to train.</span>
+<span class="sd">    loss :</span>
+<span class="sd">        Loss function</span>
+<span class="sd">    optimizer :</span>
+<span class="sd">        Optimizer</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">&#39;shallow&#39;</span><span class="p">:</span>
+        <span class="n">model</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+            <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
+            <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+            <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">75</span><span class="p">,</span> <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+            <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
+            <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s1">&#39;deep&#39;</span><span class="p">:</span>
+        <span class="n">model</span> <span class="o">=</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">Deep4Net</span></a><span class="p">(</span>
+            <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">input_window_samples</span><span class="o">=</span><span class="n">window_len_samples</span><span class="p">,</span>
+            <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+            <span class="n">filter_time_length</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pool_time_length</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">pool_time_stride</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+            <span class="n">n_filters_2</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">filter_length_2</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_filters_3</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+            <span class="n">filter_length_3</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">n_filters_4</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">filter_length_4</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+            <span class="n">first_pool_mode</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span> <span class="n">later_pool_mode</span><span class="o">=</span><span class="s2">&quot;max&quot;</span><span class="p">,</span> <span class="n">drop_prob</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+            <span class="n">double_time_convs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">split_first_layer</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_norm</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">batch_norm_alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">stride_before_pool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span>
+
+    <span class="k">if</span> <span class="n">cuda</span><span class="p">:</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+    <span class="n">optimizer</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
+    <span class="n">loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span>
+
+
+<span class="k">def</span> <span class="nf">run_training</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Run training loop.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    model : torch.nn.Module</span>
+<span class="sd">        Model to train.</span>
+<span class="sd">    dataloader : torch.utils.data.Dataloader</span>
+<span class="sd">        Data loader which will serve examples to the model during training.</span>
+<span class="sd">    loss :</span>
+<span class="sd">        Loss function.</span>
+<span class="sd">    optimizer :</span>
+<span class="sd">        Optimizer.</span>
+<span class="sd">    n_epochs : int</span>
+<span class="sd">        Number of epochs to train the model for.</span>
+<span class="sd">    cuda : bool</span>
+<span class="sd">        If True, move X and y to CUDA device.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    model : torch.nn.Module</span>
+<span class="sd">        Trained model.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_epochs</span><span class="p">):</span>
+        <span class="n">loss_vals</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">dataloader</span><span class="p">:</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+
+            <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">cuda</span><span class="p">:</span>
+                <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">y</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+            <span class="n">loss_val</span> <span class="o">=</span> <span class="n">loss</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">y</span><span class="p">)</span>
+            <span class="n">loss_vals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss_val</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
+
+            <span class="n">loss_val</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Epoch </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1"> - mean training loss: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">loss_vals</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span>
+</pre></div>
+</div>
+<p>Next, we define the different hyperparameters that we want to compare:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">PRELOAD</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span>  <span class="c1"># True -&gt; eager loading; False -&gt; lazy loading</span>
+<span class="n">N_RECORDINGS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">]</span>  <span class="c1"># Number of recordings to load from the TUH Abnormal corpus</span>
+<span class="n">WINDOW_LEN_S</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">15</span><span class="p">]</span>  <span class="c1"># Window length, in seconds</span>
+<span class="n">N_EPOCHS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>  <span class="c1"># Number of epochs to train the model for</span>
+<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">256</span><span class="p">]</span>  <span class="c1"># Training minibatch size</span>
+<span class="n">MODEL</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;shallow&#39;</span><span class="p">,</span> <span class="s1">&#39;deep&#39;</span><span class="p">]</span>
+
+<span class="n">NUM_WORKERS</span> <span class="o">=</span> <span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>  <span class="c1"># number of processes used by pytorch&#39;s Dataloader</span>
+<span class="n">PIN_MEMORY</span> <span class="o">=</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span>  <span class="c1"># whether to use pinned memory</span>
+<span class="n">CUDA</span> <span class="o">=</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="p">[</span><span class="kc">False</span><span class="p">]</span>  <span class="c1"># whether to use a CUDA device</span>
+
+<span class="n">N_REPETITIONS</span> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Number of times to repeat the experiment (to get better time estimates)</span>
+</pre></div>
+</div>
+<p>The following path needs to be changed to your local folder containing the
+TUH Abnormal corpus:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">TUH_PATH</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;/storage/store/data/tuh_eeg/www.isip.piconepress.com/projects/&#39;</span>
+            <span class="s1">&#39;tuh_eeg/downloads/tuh_eeg_abnormal/v2.0.0/edf/&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can finally cycle through all the different combinations of the parameters
+we set above to evaluate their execution time:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">all_results</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+<span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <span class="n">n_recordings</span><span class="p">,</span> <span class="n">win_len_s</span><span class="p">,</span> <span class="n">n_epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">model_kind</span><span class="p">,</span>
+        <span class="n">num_workers</span><span class="p">,</span> <span class="n">pin_memory</span><span class="p">,</span> <span class="n">cuda</span><span class="p">)</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a><span class="p">(</span>
+            <span class="nb">range</span><span class="p">(</span><span class="n">N_REPETITIONS</span><span class="p">),</span> <span class="n">PRELOAD</span><span class="p">,</span> <span class="n">N_RECORDINGS</span><span class="p">,</span> <span class="n">WINDOW_LEN_S</span><span class="p">,</span> <span class="n">N_EPOCHS</span><span class="p">,</span>
+            <span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="n">MODEL</span><span class="p">,</span> <span class="n">NUM_WORKERS</span><span class="p">,</span> <span class="n">PIN_MEMORY</span><span class="p">,</span> <span class="n">CUDA</span><span class="p">):</span>
+
+    <span class="n">results</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s1">&#39;repetition&#39;</span><span class="p">:</span> <span class="n">i</span><span class="p">,</span>
+        <span class="s1">&#39;preload&#39;</span><span class="p">:</span> <span class="n">preload</span><span class="p">,</span>
+        <span class="s1">&#39;n_recordings&#39;</span><span class="p">:</span> <span class="n">n_recordings</span><span class="p">,</span>
+        <span class="s1">&#39;win_len_s&#39;</span><span class="p">:</span> <span class="n">win_len_s</span><span class="p">,</span>
+        <span class="s1">&#39;n_epochs&#39;</span><span class="p">:</span> <span class="n">n_epochs</span><span class="p">,</span>
+        <span class="s1">&#39;batch_size&#39;</span><span class="p">:</span> <span class="n">batch_size</span><span class="p">,</span>
+        <span class="s1">&#39;model_kind&#39;</span><span class="p">:</span> <span class="n">model_kind</span><span class="p">,</span>
+        <span class="s1">&#39;num_workers&#39;</span><span class="p">:</span> <span class="n">num_workers</span><span class="p">,</span>
+        <span class="s1">&#39;pin_memory&#39;</span><span class="p">:</span> <span class="n">pin_memory</span><span class="p">,</span>
+        <span class="s1">&#39;cuda&#39;</span><span class="p">:</span> <span class="n">cuda</span>
+    <span class="p">}</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Repetition </span><span class="si">{</span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">N_REPETITIONS</span><span class="si">}</span><span class="s1">:</span><span class="se">\n</span><span class="si">{</span><span class="n">results</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Load the dataset</span>
+    <span class="n">data_loading_start</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">load_example_data</span><span class="p">(</span><span class="n">preload</span><span class="p">,</span> <span class="n">win_len_s</span><span class="p">,</span> <span class="n">n_recordings</span><span class="o">=</span><span class="n">n_recordings</span><span class="p">)</span>
+    <span class="n">data_loading_end</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+
+    <span class="c1"># Create the data loader</span>
+    <span class="n">training_setup_start</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+    <span class="n">dataloader</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">DataLoader</span></a><span class="p">(</span>
+        <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">pin_memory</span><span class="o">=</span><span class="n">pin_memory</span><span class="p">,</span>
+        <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span> <span class="n">worker_init_fn</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="c1"># Instantiate model and optimizer</span>
+    <span class="n">n_channels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">ch_names</span><span class="p">)</span>
+    <span class="n">n_times</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">times</span><span class="p">)</span>
+    <span class="n">n_classes</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span> <span class="o">=</span> <span class="n">create_example_model</span><span class="p">(</span>
+        <span class="n">n_channels</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_times</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">model_kind</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
+    <span class="n">training_setup_end</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+
+    <span class="c1"># Start training loop</span>
+    <span class="n">model_training_start</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+    <span class="n">trained_model</span> <span class="o">=</span> <span class="n">run_training</span><span class="p">(</span>
+        <span class="n">model</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">n_epochs</span><span class="o">=</span><span class="n">n_epochs</span><span class="p">,</span> <span class="n">cuda</span><span class="o">=</span><span class="n">cuda</span><span class="p">)</span>
+    <span class="n">model_training_end</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+
+    <span class="k">del</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">trained_model</span>
+
+    <span class="c1"># Record timing results</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;data_preparation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_loading_end</span> <span class="o">-</span> <span class="n">data_loading_start</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;training_setup&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">training_setup_end</span> <span class="o">-</span> <span class="n">training_setup_start</span>
+    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;model_training&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">model_training_end</span> <span class="o">-</span> <span class="n">model_training_start</span>
+    <span class="n">all_results</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The results are formatted into a pandas DataFrame and saved locally as a CSV
+file.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">all_results</span><span class="p">)</span>
+<span class="n">fname</span> <span class="o">=</span> <span class="s1">&#39;lazy_vs_eager_loading_results.csv&#39;</span>
+<span class="n">results_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Results saved under </span><span class="si">{</span><span class="n">fname</span><span class="si">}</span><span class="s1">.&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>We can finally summarize this information into the following plot:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sns</span><span class="o">.</span><span class="n">catplot</span><span class="p">(</span>
+    <span class="n">data</span><span class="o">=</span><span class="n">results_df</span><span class="p">,</span> <span class="n">row</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">&#39;model_kind&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;model_training&#39;</span><span class="p">,</span>
+    <span class="n">hue</span><span class="o">=</span><span class="s1">&#39;num_workers&#39;</span><span class="p">,</span> <span class="n">col</span><span class="o">=</span><span class="s1">&#39;preload&#39;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;strip&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The results of this comparison will change depending on the hyperparameters
+that were set above, and on the actual hardware that is being used.</p>
+</div>
+<p>Generally speaking, we expect lazy loading to be slower than eager loading
+during model training, but to potentially be pretty competitive if multiple
+workers were enabled (i.e.., <cite>num_workers &gt; 0</cite>). Training on a CUDA device
+should also yield substantial speedups.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 0.000 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  0 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-benchmark-lazy-eager-loading-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/bee463a2563e9377210dd6396f8af549/benchmark_lazy_eager_loading.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">benchmark_lazy_eager_loading.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/0f2bf063e08b7d05b80e0004fcbbb6f9/benchmark_lazy_eager_loading.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">benchmark_lazy_eager_loading.ipynb</span></code></a></p>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
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+<p>Examples introducing data loading and basic data processing.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
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+  <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
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+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="benchmarking-preprocessing-with-parallelization-and-serialization">
+<span id="sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"></span><h1>Benchmarking preprocessing with parallelization and serialization<a class="headerlink" href="#benchmarking-preprocessing-with-parallelization-and-serialization" title="Link to this heading">#</a></h1>
+<p>In this example, we compare the execution time and memory requirements of
+preprocessing data with the parallelization and serialization functionalities
+available in <a class="reference internal" href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a>.</p>
+<p>We compare 4 cases:</p>
+<ol class="arabic simple">
+<li><p>Sequential, no serialization</p></li>
+<li><p>Sequential, with serialization</p></li>
+<li><p>Parallel, no serialization</p></li>
+<li><p>Parallel, with serialization</p></li>
+</ol>
+<p>Case 1 is the simplest approach, in which all recordings in a
+<a class="reference internal" href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> are preprocessed one after the
+other. In this scenario, <a class="reference internal" href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> acts
+inplace, which means memory usage will likely stay stable (depending on the
+preprocessing operations) if recordings have been preloaded. However, two
+potential issues arise when working with large datasets: (1) if recordings have
+not been preloaded before preprocessing, <cite>preprocess()</cite> will need to load them
+and keep them in memory, in which case memory can become a bottleneck, and (2)
+sequential preprocessing can take a considerable amount of time to run when
+working with many recordings.</p>
+<p>A solution to the first issue (memory usage) is to save the preprocessed data
+to a file so it can be cleared from memory before moving on to the next
+recording (case 2). The recordings can then be reloaded with <cite>preload=False</cite>
+once they have all been saved to disk. This enables using the lazy loading
+capabilities of <a class="reference internal" href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> and avoids
+potential memory bottlenecks. The downside is that the writing to disk can take
+some time and of course requires disk space.</p>
+<p>A solution to the second issue (slow preprocessing) is to parallelize the
+preprocessing over multiple cores whenever possible (case 3). This can speed up
+preprocessing significantly. However, this approach will increase memory usage
+because of the way parallelization is implemented internally (with
+<cite>joblib</cite>, copies of (part of) the data must be made when sending arguments to
+parallel processes).</p>
+<p>Finally, case 4 (combining parallelization and serialization) is likely to be
+both fast and memory efficient. As shown in this example, this remains a
+tradeoff though, and the selected configuration should depend on the size of
+the dataset and the specific operations applied to the recordings.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">tempfile</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
+
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a>
+<span class="kn">from</span> <span class="nn">memory_profiler</span> <span class="kn">import</span> <span class="n">memory_usage</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>We create a function that goes through the usual three steps of data
+preparation: (1) data loading, (2) continuous data preprocessing,
+(3) windowing and (4) windowed data preprocessing. We use the
+<a class="reference internal" href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> dataset for testing purposes.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">):</span>
+    <span class="k">if</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">:</span>
+        <span class="n">tmp_dir</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory" title="tempfile.TemporaryDirectory" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-class"><span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span></a><span class="p">()</span>
+        <span class="n">save_dir</span> <span class="o">=</span> <span class="n">tmp_dir</span><span class="o">.</span><span class="n">name</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">save_dir</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="c1"># (1) Load the data</span>
+    <span class="n">concat_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
+        <span class="n">subject_ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">),</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
+    <span class="n">sfreq</span> <span class="o">=</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+
+    <span class="c1"># (2) Preprocess the continuous data</span>
+    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span>
+        <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
+        <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
+    <span class="p">]</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+
+    <span class="c1"># (3) Window the data</span>
+    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+        <span class="n">concat_ds</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+
+    <span class="c1"># Preprocess the windowed data</span>
+    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>Next, we can run our function and measure its run time and peak memory usage
+for each one of our 4 cases above. We call the function multiple times with
+each configuration to get better estimates.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To better characterize the run time vs. memory usage tradeoff for your
+specific configuration (as this will differ based on available hardware,
+data size and preprocessing operations), we recommend adapting this example
+to your use case and running it on your machine.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Number of repetitions</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Number of recordings to load and preprocess</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>  <span class="c1"># Number of parallel processes</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a><span class="p">(</span>
+        <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a><span class="p">),</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="p">],</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">):</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">memory_usage</span><span class="p">(</span>
+        <span class="n">proc</span><span class="o">=</span><span class="p">(</span><span class="n">prepare_data</span><span class="p">,</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="p">{})))</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a>
+
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="o">.</span><span class="n">append</span><span class="p">({</span>
+        <span class="s1">&#39;n_recs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span>
+        <span class="s1">&#39;max_mem&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a><span class="p">,</span>
+        <span class="s1">&#39;save&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span>
+        <span class="s1">&#39;n_jobs&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">,</span>
+        <span class="s1">&#39;time&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a>
+    <span class="p">})</span>
+</pre></div>
+</div>
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+100%|████████████████████████████████████▉| 51.1M/51.1M [00:43&lt;00:00, 1.14MB/s]
+  0%|                                              | 0.00/51.1M [00:00&lt;?, ?B/s]
+100%|██████████████████████████████████████| 51.1M/51.1M [00:00&lt;00:00, 326GB/s]
+
+  0%|                                              | 0.00/3.90k [00:00&lt;?, ?B/s]
+  0%|                                              | 0.00/3.90k [00:00&lt;?, ?B/s]
+100%|█████████████████████████████████████| 3.90k/3.90k [00:00&lt;00:00, 31.7MB/s]
+Download complete in 44s (48.7 MB)
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmplkulbaqd/0/0-raw.fif
+Closing /tmp/tmplkulbaqd/0/0-raw.fif
+[done]
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmplkulbaqd/1/1-raw.fif
+Closing /tmp/tmplkulbaqd/1/1-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmplkulbaqd contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmplkulbaqd/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmplkulbaqd/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Writing /tmp/tmplkulbaqd/0/0-raw.fif
+Closing /tmp/tmplkulbaqd/0/0-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:700: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmplkulbaqd contains other subdirectories or files [&#39;1&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Writing /tmp/tmplkulbaqd/1/1-raw.fif
+Closing /tmp/tmplkulbaqd/1/1-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmplkulbaqd contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmplkulbaqd/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmplkulbaqd/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Opening raw data file /tmp/tmp6e7o1nnr/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmp6e7o1nnr/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Opening raw data file /tmp/tmp6e7o1nnr/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmp6e7o1nnr/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmppcdjcl1n/0/0-raw.fif
+Closing /tmp/tmppcdjcl1n/0/0-raw.fif
+[done]
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Writing /tmp/tmppcdjcl1n/1/1-raw.fif
+Closing /tmp/tmppcdjcl1n/1/1-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmppcdjcl1n contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmppcdjcl1n/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmppcdjcl1n/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Writing /tmp/tmppcdjcl1n/0/0-raw.fif
+Closing /tmp/tmppcdjcl1n/0/0-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:700: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmppcdjcl1n contains other subdirectories or files [&#39;1&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Writing /tmp/tmppcdjcl1n/1/1-raw.fif
+Closing /tmp/tmppcdjcl1n/1/1-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmppcdjcl1n contains other subdirectories or files [&#39;0&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+Opening raw data file /tmp/tmppcdjcl1n/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmppcdjcl1n/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Opening raw data file /tmp/tmpilhgpjxk/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmpilhgpjxk/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Opening raw data file /tmp/tmpilhgpjxk/0/0-raw.fif...
+Isotrak not found
+    Range : 2884000 ... 5391000 =  28840.000 ... 53910.000 secs
+Ready.
+Opening raw data file /tmp/tmpilhgpjxk/1/1-raw.fif...
+Isotrak not found
+    Range : 1975000 ... 5235000 =  19750.000 ... 52350.000 secs
+Ready.
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 2507000  =      0.000 ... 25070.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Reading 0 ... 3260000  =      0.000 ... 32600.000 secs...
+Filtering raw data in 1 contiguous segment
+Setting up low-pass filter at 30 Hz
+
+FIR filter parameters
+---------------------
+Designing a one-pass, zero-phase, non-causal lowpass filter:
+- Windowed time-domain design (firwin) method
+- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
+- Upper passband edge: 30.00 Hz
+- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
+- Filter length: 45 samples (0.450 s)
+
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4001E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Extracting EDF parameters from /home/runner/mne_data/physionet-sleep-data/SC4011E0-PSG.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+</pre></div>
+</div>
+<p>Finally, we can plot the results:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span> <span class="s1">&#39;tab:orange&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span> <span class="s1">&#39;tab:blue&#39;</span><span class="p">}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">m</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">])}</span>
+<span class="k">for</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">),</span> <span class="n">sub_df</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;save&#39;</span><span class="p">,</span> <span class="s1">&#39;n_jobs&#39;</span><span class="p">]):</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;max_mem&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">],</span>
+               <span class="n">marker</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;save=</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.bool_" title="numpy.bool_" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="si">}</span><span class="s1">, n_jobs=</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Execution time (s)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Memory usage (MiB)&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Loading and preprocessing </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="si">}</span><span class="s1"> recordings from Sleep &#39;</span>
+             <span class="s1">&#39;Physionet&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_benchmark_preprocessing_001.png" srcset="../../_images/sphx_glr_plot_benchmark_preprocessing_001.png" alt="Loading and preprocessing 2 recordings from Sleep Physionet" class = "sphx-glr-single-img"/><p>We see that parallel preprocessing without serialization (blue crosses) is
+faster than simple sequential processing (blue circles), however it uses more
+memory.</p>
+<p>Combining parallel preprocessing and serialization (orange crosses) reduces
+memory usage significantly, however it increases run time by a few seconds.
+Depending on available resources (e.g. in limited memory settings), it might
+therefore be more advantageous to use both parallelization and serialization
+together.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (1 minutes 24.901 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  95 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-benchmark-preprocessing-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/60d8dafd5347638108915bae984c26bd/plot_benchmark_preprocessing.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark_preprocessing.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/1c6590d134fa0befda654e89aa55b2ac/plot_benchmark_preprocessing.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark_preprocessing.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
+
+
+  
+</div>
+
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+                  <!-- Previous / next buttons -->
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="custom-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"></span><h1>Custom Dataset Example<a class="headerlink" href="#custom-dataset-example" title="Link to this heading">#</a></h1>
+<p>This example shows how to convert data X and y as numpy arrays to a braindecode
+compatible data format.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a>
+</pre></div>
+</div>
+<p>To set up the example, we first fetch some data using mne:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
+<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
+
+<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
+<span class="c1"># and then return the paths to the files.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Load each of the files</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
+         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+</pre></div>
+</div>
+<p>We take the required data, targets and additional information sampling
+frequency and channel names from the loaded data. Note that this data and
+information can originate from any source.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">raw</span><span class="o">.</span><span class="n">get_data</span><span class="p">()</span> <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;ch_names&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Convert to data format compatible with skorch and braindecode:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>  <span class="c1"># look as dataset description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+Creating RawArray with float64 data, n_channels=64, n_times=20000
+    Range : 0 ... 19999 =      0.000 ...   124.994 secs
+Ready.
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>target</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>6</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>9</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>10</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>13</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>14</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>You can manipulate the dataset</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">))</span>  <span class="c1"># get the number of samples</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>240
+</pre></div>
+</div>
+<p>You can now index the data</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span><span class="o">.</span><span class="n">shape</span></a>  <span class="c1"># the EEG data</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;n_channels=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="si">}</span><span class="s2">  -- n_times=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a><span class="si">}</span><span class="s2"> -- y_i=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;start_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="si">}</span><span class="s2"> -- stop_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>n_channels=64  -- n_times=500 -- y_i=5
+start_ind=0 -- stop_ind=500
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 1.978 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-custom-dataset-example-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/b3d02314404fffecf4e64263b47762f1/plot_custom_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_dataset_example.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/f7b38cac92c078838442753121efc297/plot_custom_dataset_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_custom_dataset_example.ipynb</span></code></a></p>
+</div>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
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+
+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Load and...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="load-and-save-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"></span><h1>Load and save dataset example<a class="headerlink" href="#load-and-save-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we show how to load and save braindecode datasets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">tempfile</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.datautil</span> <span class="kn">import</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+</pre></div>
+</div>
+<p>First, we load some dataset using MOABB.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span>
+    <span class="n">dataset_name</span><span class="o">=</span><span class="s1">&#39;BNCI2014001&#39;</span><span class="p">,</span>
+    <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+<p>We can apply preprocessing steps to the dataset. It is also possible to skip
+this step and not apply any preprocessing.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <span class="n">preprocessors</span><span class="o">=</span><span class="p">[</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">fn</span><span class="o">=</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">10</span><span class="p">)]</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45429de380&gt;
+</pre></div>
+</div>
+<p>We save the dataset to a an existing directory. It will create a ‘.fif’ file
+for every dataset in the concat dataset. Additionally it will create two
+JSON files, the first holding the description of the dataset, the second
+holding the name of the target. If you want to store to the same directory
+several times, for example due to trying different preprocessing, you can
+choose to overwrite the existing files.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># write in a temporary directory</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Writing /tmp/tmpmcfvy2r0/0/0-raw.fif
+Closing /tmp/tmpmcfvy2r0/0/0-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/1/1-raw.fif
+Closing /tmp/tmpmcfvy2r0/1/1-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/2/2-raw.fif
+Closing /tmp/tmpmcfvy2r0/2/2-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/3/3-raw.fif
+Closing /tmp/tmpmcfvy2r0/3/3-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/4/4-raw.fif
+Closing /tmp/tmpmcfvy2r0/4/4-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/5/5-raw.fif
+Closing /tmp/tmpmcfvy2r0/5/5-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/6/6-raw.fif
+Closing /tmp/tmpmcfvy2r0/6/6-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/7/7-raw.fif
+Closing /tmp/tmpmcfvy2r0/7/7-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/8/8-raw.fif
+Closing /tmp/tmpmcfvy2r0/8/8-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/9/9-raw.fif
+Closing /tmp/tmpmcfvy2r0/9/9-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/10/10-raw.fif
+Closing /tmp/tmpmcfvy2r0/10/10-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/11/11-raw.fif
+Closing /tmp/tmpmcfvy2r0/11/11-raw.fif
+[done]
+</pre></div>
+</div>
+<p>We load the saved dataset from a directory. Signals can be preloaded in
+compliance with mne. Optionally, only specific ‘.fif’ files can be loaded
+by specifying their ids. The target name can be changed, if the dataset
+supports it (TUHAbnormal for example supports ‘pathological’, ‘age’, and
+‘gender’. If you stored a preprocessed version with target ‘pathological’
+it is possible to change the target upon loading).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">ids_to_load</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opening raw data file /tmp/tmpmcfvy2r0/1/1-raw.fif...
+    Range : 0 ... 3868 =      0.000 ...   386.800 secs
+Ready.
+Reading 0 ... 3868  =      0.000 ...   386.800 secs...
+Opening raw data file /tmp/tmpmcfvy2r0/3/3-raw.fif...
+    Range : 0 ... 3868 =      0.000 ...   386.800 secs
+Ready.
+Reading 0 ... 3868  =      0.000 ...   386.800 secs...
+</pre></div>
+</div>
+<p>The serialization utility also supports WindowsDatasets, so we create
+compute windows next.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <span class="n">concat_ds</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>3</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>Again, we save the dataset to an existing directory. It will create a
+‘-epo.fif’ file for every dataset in the concat dataset. Additionally it
+will create a JSON file holding the description of the dataset. If you
+want to store to the same directory several times, for example due to
+trying different windowing parameters, you can choose to overwrite the
+existing files.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
+    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Writing /tmp/tmpmcfvy2r0/0/0-raw.fif
+Closing /tmp/tmpmcfvy2r0/0/0-raw.fif
+[done]
+Writing /tmp/tmpmcfvy2r0/1/1-raw.fif
+Closing /tmp/tmpmcfvy2r0/1/1-raw.fif
+[done]
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:700: UserWarning: The number of saved datasets (2) does not match the number of existing subdirectories (12). You may now encounter a mix of differently preprocessed datasets!
+  warnings.warn(f&quot;The number of saved datasets ({i_ds+1+offset}) &quot;
+/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:708: UserWarning: Chosen directory /tmp/tmpmcfvy2r0 contains other subdirectories or files [&#39;8&#39;, &#39;7&#39;, &#39;10&#39;, &#39;5&#39;, &#39;4&#39;, &#39;9&#39;, &#39;3&#39;, &#39;2&#39;, &#39;6&#39;, &#39;11&#39;].
+  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
+</pre></div>
+</div>
+<p>Load the saved dataset from a directory. Signals can be preloaded in
+compliance with mne. Optionally, only specific ‘-epo.fif’ files can be
+loaded by specifying their ids.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
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+    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
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+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
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diff --git a/stable/auto_examples/datasets_io/plot_mne_dataset_example.html b/stable/auto_examples/datasets_io/plot_mne_dataset_example.html
new file mode 100644
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+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Loading and organizing data</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
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+  </ul>
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="mne-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"></span><h1>MNE Dataset Example<a class="headerlink" href="#mne-dataset-example" title="Link to this heading">#</a></h1>
+<p>This example shows how to convert data from mne.Raws or mne.Epochs to a
+braindecode compatible data format.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<p>First, fetch some data using mne:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
+<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
+
+<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
+<span class="c1"># and then return the paths to the files.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Load each of the files</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
+         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
+EDF file detected
+Setting channel info structure...
+Creating raw.info structure...
+Reading 0 ... 19999  =      0.000 ...   124.994 secs...
+</pre></div>
+</div>
+<p>Convert Raw objects to a compatible data format:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;event_code&quot;</span><span class="p">:</span> <span class="n">code</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">}</span>
+                <span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">,</span>
+    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:124: UserWarning: Drop bad windows only has an effect if mne epochs are created, and this argument may be removed in the future.
+  warnings.warn(&#39;Drop bad windows only has an effect if mne epochs are created, &#39;
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:130: UserWarning: Using reject or picks or flat or dropping bad windows means mne Epochs are created, which will be substantially slower and may be deprecated in the future.
+  warnings.warn(&#39;Using reject or picks or flat or dropping bad windows means &#39;
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
+Using data from preloaded Raw for 60 events and 500 original time points ...
+0 bad epochs dropped
+</pre></div>
+</div>
+<p>If trials were already cut beforehand and are available as mne.Epochs:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+                  <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Not setting metadata
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+  <div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
+<li class="toctree-l1 has-children"><a class="reference internal" href="../model_building/index.html">Basic model building and training</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_basic_training_epochs.html">Simple training on MNE epochs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Loading and organizing data</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
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+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">MOABB...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="moabb-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"></span><h1>MOABB Dataset Example<a class="headerlink" href="#moabb-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we show how to fetch and prepare a MOABB dataset for usage
+with Braindecode.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#          Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
+<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
+<span class="c1">#          Daniel Wilson &lt;dan.c.wil@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
+</pre></div>
+</div>
+<p>First, we create a dataset based on BCIC IV 2a fetched with MOABB,</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+<p>The dataset has a pandas DataFrame with additional description of its internal datasets</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>We can iterate through dataset which yields one time point of a continuous signal x,
+and a target y (which can be None if targets are not defined for the entire
+continuous signal).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="k">break</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(26, 1) None
+</pre></div>
+</div>
+<p>We can apply preprocessing transforms that are defined in mne and work
+in-place, such as resampling, bandpass filtering, or electrode selection.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>250.0
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
+48 events found
+Event IDs: [1 2 3 4]
+48 events found
+Event IDs: [1 2 3 4]
+100.0
+</pre></div>
+</div>
+<p>We can easily split the dataset based on a criteria applied to the description
+DataFrame:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">({</span><span class="n">subset_name</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span> <span class="k">for</span> <span class="n">subset_name</span><span class="p">,</span> <span class="n">subset</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0train&#39;: 232164, &#39;1test&#39;: 232164}
+</pre></div>
+</div>
+<p>See our <a class="reference external" href="../model_building/plot_bcic_iv_2a_moabb_trial.html">Trialwise Decoding</a> and
+<a class="reference external" href="../model_building/plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding</a> examples for
+training with this dataset.</p>
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+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
+        
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
+  
+    <div class="header-article-items__start">
+      
+        <div class="header-article-item">
+
+
+
+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Loading and organizing data</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Split...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="split-dataset-example">
+<span id="sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"></span><h1>Split Dataset Example<a class="headerlink" href="#split-dataset-example" title="Link to this heading">#</a></h1>
+<p>In this example, we aim to show multiple ways of how you can split your datasets for
+training, testing, and evaluating your models.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-the-dataset" id="id1">Loading the dataset</a></p></li>
+<li><p><a class="reference internal" href="#splitting" id="id2">Splitting</a></p>
+<ul>
+<li><p><a class="reference internal" href="#by-description-information" id="id3">By description information</a></p></li>
+<li><p><a class="reference internal" href="#by-row-index" id="id4">By row index</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#observation" id="id5">Observation</a></p></li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+</pre></div>
+</div>
+<section id="loading-the-dataset">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Loading the dataset</a><a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h2>
+<p>Firstly, we create a dataset using the braindecode class &lt;MOABBDataset&gt; to load
+it fetched from MOABB. In this example, we’re using Dataset 2a from BCI
+Competition IV.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="splitting">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Splitting</a><a class="headerlink" href="#splitting" title="Link to this heading">#</a></h2>
+<section id="by-description-information">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">By description information</a><a class="headerlink" href="#by-description-information" title="Link to this heading">#</a></h3>
+<p>The class &lt;MOABBDataset&gt; has a pandas DataFrame containing additional
+description of its internal datasets, which can be used to help splitting the data
+based on recording information, such as subject, session, and run of each trial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>5</td>
+    </tr>
+    <tr>
+      <th>6</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>7</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>8</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>9</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>10</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>11</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>Here, we’re splitting the data based on different runs. The method split returns
+a dictionary with string keys corresponding to unique entries in the description
+DataFrame column.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127d090&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454125a710&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454125a320&gt;, &#39;3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454125b100&gt;, &#39;4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4541258c10&gt;, &#39;5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4541258580&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /></section>
+<section id="by-row-index">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">By row index</a><a class="headerlink" href="#by-row-index" title="Link to this heading">#</a></h3>
+<p>Another way we can split the dataset is based on a list of integers corresponding to
+rows in the description. In this case, the returned dictionary will have
+‘0’ as the only key.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127c6a0&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>0train</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>However, if we want multiple splits based on indices, we can also define a list
+containing lists of integers. In this case, the dictionary will have string keys
+representing the index of the dataset split in the order of the given list of
+integers.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127cf70&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127ce80&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f7c0&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /><p>You can also name each split in the output dictionary by specifying the keys
+of each list of indexes in the input dictionary:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;valid&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s2">&quot;test&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]}</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f45429de380&gt;, &#39;valid&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f45429de440&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4542636b30&gt;}
+</pre></div>
+</div>
+<div class="output_subarea output_html rendered_html output_result">
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>subject</th>
+      <th>session</th>
+      <th>run</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>1</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>2</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>3</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>4</td>
+    </tr>
+    <tr>
+      <th>5</th>
+      <td>1</td>
+      <td>1test</td>
+      <td>5</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+</div>
+<br />
+<br /></section>
+</section>
+<section id="observation">
+<h2><a class="toc-backref" href="#id5" role="doc-backlink">Observation</a><a class="headerlink" href="#observation" title="Link to this heading">#</a></h2>
+<p>Similarly, we can split datasets after creating windows using the same methods.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Splitting by different runs</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Using description info&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="c1"># Splitting by row index</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Splitting by row index&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Multiple row index split&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+<span class="nb">print</span><span class="p">()</span>
+
+<span class="c1"># Specifying output&#39;s keys</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Specifying keys&quot;</span><span class="p">)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">test</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]))</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using description info
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f940&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f7c0&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127d4e0&gt;, &#39;3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f340&gt;, &#39;4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127d090&gt;, &#39;5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f460ab57be0&gt;}
+
+Splitting by row index
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f4542634d90&gt;}
+
+Multiple row index split
+{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f340&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127d4e0&gt;}
+
+Specifying keys
+{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f7c0&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f454127f940&gt;}
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 4.466 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-split-dataset-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/8cc351cf606ecc565831d11752d2d9a2/plot_split_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_split_dataset.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/f3c89d39e947a121c7920b4d415413a2/plot_split_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_split_dataset.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-dataset">Loading the dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#splitting">Splitting</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#by-description-information">By description information</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#by-row-index">By row index</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#observation">Observation</a></li>
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Multiple discrete targets with the TUH EEG Corpus</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+<p class="admonition-title">Note</p>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="multiple-discrete-targets-with-the-tuh-eeg-corpus">
+<span id="sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"></span><h1>Multiple discrete targets with the TUH EEG Corpus<a class="headerlink" href="#multiple-discrete-targets-with-the-tuh-eeg-corpus" title="Link to this heading">#</a></h1>
+<dl class="simple">
+<dt>Welcome to this tutorial where we demonstrate how to work with multiple discrete</dt><dd><p>targets for each recording in the TUH EEG Corpus. We’ll guide you through the
+process step by step.</p>
+</dd>
+</dl>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+
+<span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+
+<span class="c1"># Setting Logging Level</span>
+<span class="c1"># ----------------------</span>
+<span class="c1">#</span>
+<span class="c1"># We&#39;ll set the logging level to &#39;ERROR&#39; to avoid excessive messages when</span>
+<span class="c1"># extracting windows:</span>
+
+<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages every time a window is extracted</span>
+</pre></div>
+</div>
+<p>If you want to try this code with the actual data, please delete the next
+section. We are required to mock some dataset functionality, since the data
+is not available at creation time of this example.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
+</pre></div>
+</div>
+<section id="creating-temple-university-hospital-tuh-eeg-corpus-dataset">
+<h2>Creating Temple University Hospital (TUH) EEG Corpus Dataset<a class="headerlink" href="#creating-temple-university-hospital-tuh-eeg-corpus-dataset" title="Link to this heading">#</a></h2>
+<p>We start by creating a TUH dataset. Instead of just a <a href="#id1"><span class="problematic" id="id2">`</span></a>str, we give it
+multiple strings as target names. Each of the strings has to exist as a
+column in the description DataFrame.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
+<span class="n">tuh</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
+    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
+    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">target_name</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">),</span>  <span class="c1"># use both age and gender as decoding target</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">tuh</span><span class="o">.</span><span class="n">description</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>                                                path version  ...  age  gender
+0  tuh_eeg/v1.1.0/edf/02_tcp_le/000/00000058/s001...  v1.1.0  ...    0       M
+1  tuh_eeg/v1.1.0/edf/01_tcp_ar/099/00009932/s004...  v1.1.0  ...   53       F
+2  tuh_eeg/v1.1.0/edf/03_tcp_ar_a/123/00012331/s0...  v1.1.0  ...   39       M
+3  tuh_eeg/v1.1.0/edf/01_tcp_ar/000/00000000/s001...  v1.1.0  ...   37       M
+4  tuh_eeg/v1.2.0/edf/03_tcp_ar_a/149/00014928/s0...  v1.2.0  ...   83       F
+
+[5 rows x 10 columns]
+</pre></div>
+</div>
+</section>
+<section id="exploring-data">
+<h2>Exploring Data<a class="headerlink" href="#exploring-data" title="Link to this heading">#</a></h2>
+<p>Iterating through the dataset gives <cite>x</cite> as an ndarray with shape
+<cite>(n_channels x 1)</cite> and <cite>y</cite> as a list containing <cite>[age of the subject, gender
+of the subject]</cite>.
+Let’s look at the last example as it has more interesting age/gender labels
+(compare to the last row of the dataframe above).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <span class="n">tuh</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x=array([[-0.92922518],
+       [ 1.22477157],
+       [ 0.12493701],
+       [ 1.28417889],
+       [ 0.13578887],
+       [-1.56835277],
+       [ 1.25153176],
+       [-1.7638324 ],
+       [ 0.14345092],
+       [-0.30167542],
+       [ 0.68056549],
+       [ 1.70380673],
+       [-0.70052629],
+       [-1.21543634],
+       [ 0.96330007],
+       [ 0.02736802],
+       [ 1.65902848],
+       [-0.15756383],
+       [ 0.38658058],
+       [-0.04194231],
+       [-0.15447331]])
+y=[83, &#39;F&#39;]
+</pre></div>
+</div>
+</section>
+<section id="creating-windows">
+<h2>Creating Windows<a class="headerlink" href="#creating-windows" title="Link to this heading">#</a></h2>
+<p>We will skip preprocessing steps for now, since it is not the aim of this
+example. Instead, we will directly create compute windows. We specify a
+mapping from genders ‘M’ and ‘F’ to integers, since this is required for
+decoding.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
+    <span class="n">tuh</span><span class="p">,</span>
+    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">mapping</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>  <span class="c1"># map non-digit targets</span>
+<span class="p">)</span>
+<span class="c1"># store the number of windows required for loading later on</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.set_description" title="braindecode.datasets.BaseConcatDataset.set_description" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">set_description</span></a><span class="p">({</span>
+    <span class="s2">&quot;n_windows&quot;</span><span class="p">:</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">]})</span>
+</pre></div>
+</div>
+</section>
+<section id="exploring-windows">
+<h2>Exploring Windows<a class="headerlink" href="#exploring-windows" title="Link to this heading">#</a></h2>
+<p>Iterating through the dataset gives <cite>x</cite> as an ndarray with shape
+<cite>(n_channels x 1000)</cite>, <cite>y</cite> as <cite>[age, gender]</cite>, and <cite>ind</cite>.
+Let’s look at the last example again.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x=array([[ 0.16017832,  0.11754766,  0.6515615 , ..., -0.50407195,
+         0.7429778 , -0.9292252 ],
+       [ 0.86039335,  0.5169721 , -1.0308012 , ..., -1.1209397 ,
+         1.5210943 ,  1.2247716 ],
+       [-2.373041  , -0.14827406,  0.25829056, ...,  1.1486133 ,
+         0.15352039,  0.12493701],
+       ...,
+       [ 0.30362263, -0.939559  ,  2.5686462 , ..., -0.5503989 ,
+        -1.3299779 ,  0.3865806 ],
+       [ 1.7443461 ,  1.1792846 , -0.25878426, ..., -0.5670986 ,
+        -1.2997373 , -0.04194231],
+       [ 1.0537782 ,  1.4429058 , -0.05458383, ...,  1.000525  ,
+         0.66521484, -0.15447332]], dtype=float32)
+y=[83, 1]
+ind=[3, 2600, 3600]
+</pre></div>
+</div>
+</section>
+<section id="dataloader-for-model-training">
+<h2>DataLoader for Model Training<a class="headerlink" href="#dataloader-for-model-training" title="Link to this heading">#</a></h2>
+<p>We give the dataset to a pytorch DataLoader, such that it can be used for
+model training.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span>
+    <span class="n">dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">,</span>
+    <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="exploring-dataloader">
+<h2>Exploring DataLoader<a class="headerlink" href="#exploring-dataloader" title="Link to this heading">#</a></h2>
+<p>When iterating through the DataLoader, we get <cite>batch_X</cite> as a tensor with shape
+<cite>(4 x n_channels x 1000)</cite>, <cite>batch_y</cite> as <cite>[tensor([4 x age of subject]),
+tensor([4 x gender of subject])]</cite>, and <cite>batch_ind</cite>. To view the last example,
+simply iterate through the DataLoader:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a><span class="p">:</span>
+    <span class="k">pass</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>batch_X=tensor([[[ 0.8024, -0.4564, -1.4433,  ..., -0.7404, -2.2522, -3.0508],
+         [ 0.4418,  0.5201, -1.2037,  ...,  1.0273,  1.0506,  0.1677],
+         [-2.1145,  0.7075,  0.1768,  ..., -0.0260, -0.7216,  0.2742],
+         ...,
+         [ 0.3986,  0.3912,  0.8000,  ...,  1.0593, -0.8097, -1.1729],
+         [-0.6267, -0.8419, -1.3996,  ...,  0.6685,  2.9266, -0.1319],
+         [ 1.1409,  1.6246, -0.6229,  ...,  0.5953, -0.3169, -1.4604]],
+
+        [[-0.2885, -0.0168,  1.6279,  ...,  0.6533,  2.7673, -0.6671],
+         [-0.0448, -1.1422, -0.5395,  ..., -0.4664, -0.1882,  0.2031],
+         [ 0.6282, -0.4535, -0.8585,  ..., -0.2657, -0.9942, -0.3878],
+         ...,
+         [-0.1360,  0.9132,  0.9481,  ..., -0.5033, -1.3406,  0.1251],
+         [ 0.6089, -0.9883, -0.2919,  ..., -1.3796,  0.8036,  0.7666],
+         [ 0.8174, -0.3829, -1.1508,  ..., -0.8516,  2.0426,  1.1322]],
+
+        [[-0.4122,  1.2526, -0.9711,  ..., -0.6890, -1.4660,  1.2500],
+         [-0.5510,  0.3613,  1.0280,  ..., -1.1350, -0.2249, -0.9406],
+         [ 0.7754,  0.7664, -1.1807,  ...,  1.8206,  1.1281,  0.1607],
+         ...,
+         [-0.9353,  0.0165, -0.6509,  ..., -0.7276,  0.2137, -0.5619],
+         [-0.0408, -1.9825, -0.3648,  ..., -1.6978,  0.8943,  1.2374],
+         [-1.1185, -0.4398, -0.8218,  ..., -1.5172,  2.2975,  1.0311]],
+
+        [[ 0.1602,  0.1175,  0.6516,  ..., -0.5041,  0.7430, -0.9292],
+         [ 0.8604,  0.5170, -1.0308,  ..., -1.1209,  1.5211,  1.2248],
+         [-2.3730, -0.1483,  0.2583,  ...,  1.1486,  0.1535,  0.1249],
+         ...,
+         [ 0.3036, -0.9396,  2.5686,  ..., -0.5504, -1.3300,  0.3866],
+         [ 1.7443,  1.1793, -0.2588,  ..., -0.5671, -1.2997, -0.0419],
+         [ 1.0538,  1.4429, -0.0546,  ...,  1.0005,  0.6652, -0.1545]]])
+batch_y=[tensor([83, 83, 83, 83]), tensor([1, 1, 1, 1])]
+batch_ind=[tensor([0, 1, 2, 3]), tensor([   0, 1000, 2000, 2600]), tensor([1000, 2000, 3000, 3600])]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 2.877 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  10 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
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+    </li>
+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
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+</nav>
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+  
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+              
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+  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-datasets-io-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>01:44.746</strong> total execution time for <strong>auto_examples_datasets_io</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_preprocessing.py</span></code>)</p></td>
+<td><p>01:24.901</p></td>
+<td><p>95.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_load_save_datasets.py</span></code>)</p></td>
+<td><p>00:04.689</p></td>
+<td><p>9.8 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_moabb_dataset_example.py</span></code>)</p></td>
+<td><p>00:04.514</p></td>
+<td><p>9.6 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_split_dataset.py</span></code>)</p></td>
+<td><p>00:04.466</p></td>
+<td><p>9.6 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_discrete_multitarget.py</span></code>)</p></td>
+<td><p>00:02.877</p></td>
+<td><p>10.2 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_dataset_example.py</span></code>)</p></td>
+<td><p>00:01.978</p></td>
+<td><p>9.6 MB</p></td>
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+<tr class="row-odd"><td><p><a class="reference internal" href="plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mne_dataset_example.py</span></code>)</p></td>
+<td><p>00:01.321</p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a> (<code class="docutils literal notranslate"><span class="pre">benchmark_lazy_eager_loading.py</span></code>)</p></td>
+<td><p>00:00.000</p></td>
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+<li class="toctree-l2"><a class="reference internal" href="datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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   <section id="examples">
-<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">#</a></h1>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show multiple ways of how to split datasets."><img alt="Split Dataset Example" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
-<p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+<h1>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h1>
+<p>Learn how to use braindecode by the examples. There are several ways to learn and understand how a library works. Here at braindecode, we encourage hands-on mode.</p>
+<p>See which tutorial best fits the approach you want and try to reproduce it on the examples data or on your data :)</p>
+<div class="sphx-glr-thumbnails"></div><section id="basic-model-building-and-training">
+<h2>Basic model building and training<a class="headerlink" href="#basic-model-building-and-training" title="Link to this heading">#</a></h2>
+<p>Examples introducing fundamental model building and training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div></div></section>
+<section id="loading-and-organizing-data">
+<h2>Loading and organizing data<a class="headerlink" href="#loading-and-organizing-data" title="Link to this heading">#</a></h2>
+<p>Examples introducing data loading and basic data processing.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
   <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="MNE Dataset Example" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
-<p><a class="reference internal" href="plot_mne_dataset_example.html#sphx-glr-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="Custom Dataset Example" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
-<p><a class="reference internal" href="plot_custom_dataset_example.html#sphx-glr-auto-examples-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of multiple discrete targets per recording with the TUH EEG ..."><img alt="Multiple discrete targets with the TUH EEG Corpus" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
-<p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div></div></section>
+<section id="advanced-neural-network-training-strategies">
+<h2>Advanced neural network training strategies<a class="headerlink" href="#advanced-neural-network-training-strategies" title="Link to this heading">#</a></h2>
+<p>Examples explaining more advanced topics in neural network training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="Benchmarking eager and lazy loading" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
-<p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div></div></section>
+<section id="applied-examples-on-real-world-datasets">
+<h2>Applied examples on real-world datasets<a class="headerlink" href="#applied-examples-on-real-world-datasets" title="Link to this heading">#</a></h2>
+<p>Examples demonstrating analaysis of EEG on data from clinical studies, sleep &amp; cognitive experiments.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#basic-model-building-and-training">Basic model building and training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-organizing-data">Loading and organizing data</a></li>
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+  <section id="basic-model-building-and-training">
+<span id="sphx-glr-auto-examples-model-building"></span><h1>Basic model building and training<a class="headerlink" href="#basic-model-building-and-training" title="Link to this heading">#</a></h1>
+<p>Examples introducing fundamental model building and training strategies.</p>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Basic model building and training</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Simple training on MNE epochs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="simple-training-on-mne-epochs">
+<span id="sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"></span><h1>Simple training on MNE epochs<a class="headerlink" href="#simple-training-on-mne-epochs" title="Link to this heading">#</a></h1>
+<p>The braindecode library gives you access to a large number of neural network
+architectures that were developed for EEG data decoding. This tutorial will
+show you how you can easily use any of these models to decode your own data.
+In particular, we assume that have your data in an MNE format and want to
+train one of the Braindecode models on it.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#finding-the-model-you-want" id="id3">Finding the model you want</a></p>
+<ul>
+<li><p><a class="reference internal" href="#exploring-the-braindecode-online-documentation" id="id4">Exploring the braindecode online documentation</a></p></li>
+<li><p><a class="reference internal" href="#examining-the-model" id="id5">Examining the model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#loading-your-own-data-with-mne" id="id6">Loading your own data with MNE</a></p></li>
+<li><p><a class="reference internal" href="#training-your-model-scikit-learn-compatible" id="id7">Training your model (scikit-learn compatible)</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id8">References</a></p></li>
+</ul>
+</nav>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Pierre Guetschel &lt;pierre.guetschel@gmail.com&gt;</span>
+<span class="c1">#</span>
+<span class="c1"># License: BSD (3-clause)</span>
+</pre></div>
+</div>
+<section id="finding-the-model-you-want">
+<h2><a class="toc-backref" href="#id3" role="doc-backlink">Finding the model you want</a><a class="headerlink" href="#finding-the-model-you-want" title="Link to this heading">#</a></h2>
+<section id="exploring-the-braindecode-online-documentation">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Exploring the braindecode online documentation</a><a class="headerlink" href="#exploring-the-braindecode-online-documentation" title="Link to this heading">#</a></h3>
+<p>Let’s suppose you recently stumbled upon the Schirrmeister 2017 article <a class="footnote-reference brackets" href="#id2" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+In this article, the authors mention that their novel architecture ShallowConvNet
+is performing well on the BCI Competition IV 2a dataset and you would like to use
+it on your own data. Fortunately, the authors also mentioned they published their
+architecture on Braindecode!</p>
+<p>In order to use this architecture, you first need to find what is its exact
+name in Braindecode. To do so, you can visit the Braindecode online documentation
+which lists all the available models.</p>
+<p>Models list: <a class="reference external" href="https://braindecode.org/stable/api.html#models">https://braindecode.org/stable/api.html#models</a></p>
+<p>Alternatively, the API also provide a dictionary with all available models:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models.util</span> <span class="kn">import</span> <span class="n">models_dict</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;All the Braindecode models:</span><span class="se">\n</span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">models_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>All the Braindecode models:
+[&#39;ATCNet&#39;, &#39;Deep4Net&#39;, &#39;DeepSleepNet&#39;, &#39;EEGConformer&#39;, &#39;EEGITNet&#39;, &#39;EEGInception&#39;, &#39;EEGInceptionERP&#39;, &#39;EEGInceptionMI&#39;, &#39;EEGNetv1&#39;, &#39;EEGNetv4&#39;, &#39;EEGResNet&#39;, &#39;HybridNet&#39;, &#39;ShallowFBCSPNet&#39;, &#39;SleepStagerBlanco2020&#39;, &#39;SleepStagerChambon2018&#39;, &#39;SleepStagerEldele2021&#39;, &#39;TCN&#39;, &#39;TIDNet&#39;, &#39;USleep&#39;]
+</pre></div>
+</div>
+<p>After your investigation, you found out that the model you are looking for is
+<code class="docutils literal notranslate"><span class="pre">ShallowFBCSPNet</span></code>. You can now import it from Braindecode:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+</pre></div>
+</div>
+</section>
+<section id="examining-the-model">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Examining the model</a><a class="headerlink" href="#examining-the-model" title="Link to this heading">#</a></h3>
+<p>Now that you found your model, you must check which parameters it expects.
+You can find this information either in the online documentation here:
+<a class="reference internal" href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.ShallowFBCSPNet</span></code></a> or directly in the module’s docstring:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="o">.</span><span class="vm">__doc__</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shallow ConvNet model from Schirrmeister et al 2017.
+
+Model described in [Schirrmeister2017]_.
+
+Parameters
+----------
+n_chans : int
+    Number of EEG channels.
+n_outputs : int
+    Number of outputs of the model. This is the number of classes
+    in the case of classification.
+n_times : int
+    Number of time samples of the input window.
+n_filters_time: int
+    Number of temporal filters.
+filter_time_length: int
+    Length of the temporal filter.
+n_filters_spat: int
+    Number of spatial filters.
+pool_time_length: int
+    Length of temporal pooling filter.
+pool_time_stride: int
+    Length of stride between temporal pooling filters.
+final_conv_length: int | str
+    Length of the final convolution layer.
+    If set to &quot;auto&quot;, length of the input signal must be specified.
+conv_nonlin: callable
+    Non-linear function to be used after convolution layers.
+pool_mode: str
+    Method to use on pooling layers. &quot;max&quot; or &quot;mean&quot;.
+pool_nonlin: callable
+    Non-linear function to be used after pooling layers.
+split_first_layer: bool
+    Split first layer into temporal and spatial layers (True) or just use temporal (False).
+    There would be no non-linearity between the split layers.
+batch_norm: bool
+    Whether to use batch normalisation.
+batch_norm_alpha: float
+    Momentum for BatchNorm2d.
+drop_prob: float
+    Dropout probability.
+chs_info : list of dict
+    Information about each individual EEG channel. This should be filled with
+    ``info[&quot;chs&quot;]``. Refer to :class:`mne.Info` for more details.
+input_window_seconds : float
+    Length of the input window in seconds.
+sfreq : float
+    Sampling frequency of the EEG recordings.
+in_chans : int
+    Alias for `n_chans`.
+n_classes: int
+    Alias for `n_outputs`.
+input_window_samples: int | None
+    Alias for `n_times`.
+add_log_softmax: bool
+    Whether to use log-softmax non-linearity as the output function.
+    LogSoftmax final layer will be removed in the future.
+    Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+    Check the documentation of the torch.nn loss functions:
+    https://pytorch.org/docs/stable/nn.html#loss-functions.
+
+Raises
+------
+ValueError: If some input signal-related parameters are not specified
+            and can not be inferred.
+
+FutureWarning: If add_log_softmax is True, since LogSoftmax final layer
+               will be removed in the future.
+
+Notes
+-----
+If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.
+
+References
+----------
+.. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+   L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
+   &amp; Ball, T. (2017).
+   Deep learning with convolutional neural networks for EEG decoding and
+   visualization.
+   Human Brain Mapping , Aug. 2017.
+   Online: http://dx.doi.org/10.1002/hbm.23730
+</pre></div>
+</div>
+<p>Additionally, you might be interested in visualizing the model’s architecture.
+This can be done by initializing the model and calling its <code class="docutils literal notranslate"><span class="pre">__str__()</span></code> method.
+To initialize it, we need to specify some parameters that we set at random
+values for now:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <span class="n">n_chans</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
+    <span class="n">n_times</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
+    <span class="n">n_outputs</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 32, 1000]             [1, 2]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 32, 1000]             [1, 32, 1000, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 32, 1000, 1]          [1, 1, 1000, 32]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1000, 32]          [1, 40, 976, 1]           52,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 976, 1]           [1, 40, 976, 1]           80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 976, 1]           [1, 40, 976, 1]           --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 976, 1]           [1, 40, 61, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 61, 1]            [1, 2]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 61, 1]            [1, 2, 1, 1]              4,882                     [61, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 2, 1, 1]              [1, 2, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 2, 1, 1]              [1, 2]                    --                        --
+============================================================================================================================================
+Total params: 57,202
+Trainable params: 57,202
+Non-trainable params: 0
+Total mult-adds (M): 0.00
+============================================================================================================================================
+Input size (MB): 0.13
+Forward/backward pass size (MB): 0.31
+Params size (MB): 0.02
+Estimated Total Size (MB): 0.46
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="loading-your-own-data-with-mne">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Loading your own data with MNE</a><a class="headerlink" href="#loading-your-own-data-with-mne" title="Link to this heading">#</a></h2>
+<p>In this tutorial, we demonstrate how to train the model on MNE data.
+MNE is quite a popular library for EEG data analysis as it provides methods
+to load data from many different file formats and a large collection of algorithms
+to preprocess it.
+However, Braindecode is not limited to MNE and can be used with numpy arrays or
+PyTorch tensors/datasets.</p>
+<p>For this example, we generate some random data containing 100 examples with each
+3 channels and 1024 time points. We also generate some random labels for our data
+that simulate a 4-class classification problem.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mne</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.create_info.html#mne.create_info" title="mne.create_info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">create_info</span></a><span class="p">(</span><span class="n">ch_names</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;C3&#39;</span><span class="p">,</span> <span class="s1">&#39;C4&#39;</span><span class="p">,</span> <span class="s1">&#39;Cz&#39;</span><span class="p">],</span> <span class="n">sfreq</span><span class="o">=</span><span class="mf">256.</span><span class="p">,</span> <span class="n">ch_types</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html#numpy.random.randn" title="numpy.random.randn" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1024</span><span class="p">)</span>  <span class="c1"># 100 epochs, 3 channels, 4 seconds (@256Hz)</span>
+<a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">EpochsArray</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a><span class="o">=</span><a href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="mne.Info" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">info</span></a><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>  <span class="c1"># 4 classes</span>
+<span class="nb">print</span><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Not setting metadata
+100 matching events found
+No baseline correction applied
+0 projection items activated
+&lt;EpochsArray |  100 events (all good), 0 – 3.99609 s, baseline off, ~2.4 MB, data loaded,
+ &#39;1&#39;: 100&gt;
+</pre></div>
+</div>
+</section>
+<section id="training-your-model-scikit-learn-compatible">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">Training your model (scikit-learn compatible)</a><a class="headerlink" href="#training-your-model-scikit-learn-compatible" title="Link to this heading">#</a></h2>
+<p>Now that you know which model you want to use, you know how to instantiate it,
+and that we have some fake data, it is time to train the model!</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference external" href="https://skorch.readthedocs.io">Skorch</a>  is a library that allows you to wrap
+any PyTorch module into a scikit-learn-compatible classifier or regressor.
+Braindecode provides wrappers that inherit form the original Skorch ones and simply
+implement a few additional features that facilitate the use of Braindecode models.</p>
+</div>
+<p>To train a Braindecode model, the easiest way is by using braindecode’s
+Skorch wrappers. These wrappers are <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> and
+<code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code>. As our fake data is a classification task,
+we will use the former.</p>
+<p>The wrapper <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> expects a model class as its first argument but
+to facilitate the usage, you can also simply pass the name of any braindecode model as a string.
+The wrapper automatically finds and instantiates the model for you.
+If you want to pass parameters to your model, you can give them to the wrapper
+with the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="n">net</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <span class="s1">&#39;ShallowFBCSPNet&#39;</span><span class="p">,</span>
+    <span class="n">module__final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
+    <span class="c1"># To train a neural network you need validation split, here, we use 20%.</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>In this example, we passed one additional parameter to the wrapper: <code class="docutils literal notranslate"><span class="pre">module__final_conv_length</span></code>
+that will be forwarded to the model (without the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>).</p>
+<p>We also note that the parameters <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_times</span></code> and <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code> were not specified
+even if <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.ShallowFBCSPNet</span></code> needs them to be initialized. This is because the
+wrapper will automatically infer them, along with some other signal-related parameters,
+from the input data at training time.</p>
+<p>Now that we have our model wrapped in a scikit-learn-compatible classifier,
+we can train it by simply calling the <code class="docutils literal notranslate"><span class="pre">fit</span></code> method:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">net</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://mne.tools/stable/generated/mne.EpochsArray.html#mne.EpochsArray" title="mne.EpochsArray" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    valid_acc    valid_loss     dur
+-------  -----------  ------------  ------
+      1       0.4000       18.7568  0.0190
+      2       0.4000       18.7568  0.0111
+      3       0.4000       18.7568  0.0109
+      4       0.4000       18.7568  0.0097
+      5       0.4000       18.7568  0.0068
+      6       0.4000       18.7568  0.0069
+      7       0.4000       18.7568  0.0066
+      8       0.4000       18.7568  0.0066
+      9       0.4000       18.7568  0.0065
+     10       0.4000       18.7568  0.0065
+
+&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
+  module_=============================================================================================================================================
+  Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+  ============================================================================================================================================
+  ShallowFBCSPNet (ShallowFBCSPNet)        [1, 3, 1024]              [1, 4]                    --                        --
+  ├─Ensure4d (ensuredims): 1-1             [1, 3, 1024]              [1, 3, 1024, 1]           --                        --
+  ├─Rearrange (dimshuffle): 1-2            [1, 3, 1024, 1]           [1, 1, 1024, 3]           --                        --
+  ├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1024, 3]           [1, 40, 1000, 1]          5,840                     --
+  ├─BatchNorm2d (bnorm): 1-4               [1, 40, 1000, 1]          [1, 40, 1000, 1]          80                        --
+  ├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1000, 1]          [1, 40, 1000, 1]          --                        --
+  ├─AvgPool2d (pool): 1-6                  [1, 40, 1000, 1]          [1, 40, 62, 1]            --                        [75, 1]
+  ├─Expression (pool_nonlin_exp): 1-7      [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+  ├─Dropout (drop): 1-8                    [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+  ├─Sequential (final_layer): 1-9          [1, 40, 62, 1]            [1, 4]                    --                        --
+  │    └─Conv2d (conv_classifier): 2-1     [1, 40, 62, 1]            [1, 4, 1, 1]              9,924                     [62, 1]
+  │    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+  │    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+  ============================================================================================================================================
+  Total params: 15,844
+  Trainable params: 15,844
+  Non-trainable params: 0
+  Total mult-adds (M): 0.01
+  ============================================================================================================================================
+  Input size (MB): 0.01
+  Forward/backward pass size (MB): 0.32
+  Params size (MB): 0.04
+  Estimated Total Size (MB): 0.37
+  ============================================================================================================================================,
+)
+</pre></div>
+</div>
+<p>The pre-trained model is accessible via the <code class="docutils literal notranslate"><span class="pre">module_</span></code> attribute:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 3, 1024]              [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 3, 1024]              [1, 3, 1024, 1]           --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 3, 1024, 1]           [1, 1, 1024, 3]           --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1024, 3]           [1, 40, 1000, 1]          5,840                     --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1000, 1]          [1, 40, 1000, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1000, 1]          [1, 40, 1000, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1000, 1]          [1, 40, 62, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 62, 1]            [1, 40, 62, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 62, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 62, 1]            [1, 4, 1, 1]              9,924                     [62, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 15,844
+Trainable params: 15,844
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.01
+Forward/backward pass size (MB): 0.32
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.37
+============================================================================================================================================
+</pre></div>
+</div>
+<p>And we can see that all the following parameters were automatically inferred
+from the training data:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_chans</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_times</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">n_outputs</span><span class="si">=}</span><span class="s1">&#39;</span>
+      <span class="sa">f</span><span class="s1">&#39;</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">input_window_seconds</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">sfreq</span><span class="si">=}</span><span class="se">\n</span><span class="si">{</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">net</span><span class="o">.</span><span class="n">module_</span></a><span class="o">.</span><span class="n">chs_info</span><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>net.module_.n_chans=3
+net.module_.n_times=1024
+net.module_.n_outputs=4
+net.module_.input_window_seconds=4.0
+net.module_.sfreq=256.0
+net.module_.chs_info=[{&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;C3&#39;, &#39;scanno&#39;: 1, &#39;logno&#39;: 1}, {&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;C4&#39;, &#39;scanno&#39;: 2, &#39;logno&#39;: 2}, {&#39;loc&#39;: array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), &#39;unit_mul&#39;: 0 (FIFF_UNITM_NONE), &#39;range&#39;: 1.0, &#39;cal&#39;: 1.0, &#39;kind&#39;: 2 (FIFFV_EEG_CH), &#39;coil_type&#39;: 1 (FIFFV_COIL_EEG), &#39;unit&#39;: 107 (FIFF_UNIT_V), &#39;coord_frame&#39;: 4 (FIFFV_COORD_HEAD), &#39;ch_name&#39;: &#39;Cz&#39;, &#39;scanno&#39;: 3, &#39;logno&#39;: 3}]
+</pre></div>
+</div>
+<p>Depending on the type of data used for training, some parameters might not be
+possible to infer. For example if you pass a numpy array or a
+<code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.dataset.WindowsDataset</span></code> with <code class="docutils literal notranslate"><span class="pre">target_from=&quot;metadata&quot;</span></code>,</p>
+<blockquote>
+<div><p>then only <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_times</span></code> and <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code> will be inferred.</p>
+</div></blockquote>
+<p>And if you pass other types of datasets, only <code class="docutils literal notranslate"><span class="pre">n_chans</span></code> and <code class="docutils literal notranslate"><span class="pre">n_times</span></code> will be inferred.
+In these case, you will have to pass the missing parameters manually
+(with the prefix <code class="docutils literal notranslate"><span class="pre">module__</span></code>).</p>
+</section>
+<section id="references">
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+M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T.(2017).
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Human Brain Mapping, Aug. 2017.
+Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
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+      </div>
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+        
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+          <div class="bd-content">
+            <div class="bd-article-container">
+              
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+<div class="header-article-items header-article__inner">
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+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
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+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
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+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Basic model building and training</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">Cropped...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="cropped-decoding-on-bcic-iv-2a-dataset">
+<span id="sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"></span><h1>Cropped Decoding on BCIC IV 2a Dataset<a class="headerlink" href="#cropped-decoding-on-bcic-iv-2a-dataset" title="Link to this heading">#</a></h1>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id6">Loading and preprocessing the dataset</a></p></li>
+<li><p><a class="reference internal" href="#create-model-and-compute-windowing-parameters" id="id7">Create model and compute windowing parameters</a></p></li>
+<li><p><a class="reference internal" href="#cut-the-data-into-windows" id="id8">Cut the data into windows</a></p></li>
+<li><p><a class="reference internal" href="#split-the-dataset" id="id9">Split the dataset</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id10">Training</a></p></li>
+<li><p><a class="reference internal" href="#plot-results" id="id11">Plot Results</a></p></li>
+<li><p><a class="reference internal" href="#plot-confusion-matrix" id="id12">Plot Confusion Matrix</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id13">References</a></p></li>
+</ul>
+</nav>
+<p>Building on the <a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Trialwise decoding</a>,
+we now do more data-efficient cropped decoding!</p>
+<p>In Braindecode, there are two supported configurations created for
+training models: trialwise decoding and cropped decoding. We will
+explain this visually by comparing trialwise to cropped decoding.</p>
+<img alt="Trialwise decoding" src="https://braindecode.org/stable/_static/trialwise_explanation.png" />
+<img alt="Cropped decoding" src="https://braindecode.org/stable/_static/cropped_explanation.png" />
+<p>On the left, you see trialwise decoding:</p>
+<ol class="arabic simple">
+<li><p>A complete trial is pushed through the network.</p></li>
+<li><p>The network produces a prediction.</p></li>
+<li><p>The prediction is compared to the target (label) for that trial to
+compute the loss.</p></li>
+</ol>
+<p>On the right, you see cropped decoding:</p>
+<ol class="arabic simple">
+<li><p>Instead of a complete trial, crops are pushed through the network.</p></li>
+<li><p>For computational efficiency, multiple neighbouring crops are pushed
+through the network simultaneously (these neighbouring crops are
+called compute windows)</p></li>
+<li><p>Therefore, the network produces multiple predictions (one per crop in
+the window)</p></li>
+<li><p>The individual crop predictions are <em>AVERAGED</em> before computing the
+loss function</p></li>
+</ol>
+<p>This averaging of predictions of small sub-windows is the key difference
+between trialwise and cropped decoding. It was introduced in <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and it impact
+on the parameters of the network.</p>
+<p>It is important to note that the averaging of predictions is only done
+during training. During testing, the network is still applied to crops
+and the predictions are averaged afterwards.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<ul class="simple">
+<li><p>The network architecture implicitly defines the crop size (it is the
+receptive field size, i.e., the number of timesteps the network uses
+to make a single prediction)</p></li>
+<li><p>The window size is a user-defined hyperparameter, called
+<code class="docutils literal notranslate"><span class="pre">input_window_samples</span></code> in Braindecode. It mostly affects runtime
+(larger window sizes should be faster). As a rule of thumb, you can
+set it to two times the crop size.</p></li>
+<li><p>Crop size and window size together define how many predictions the
+network makes per window: <code class="docutils literal notranslate"><span class="pre">#window</span> <span class="pre">−</span> <span class="pre">#crop</span> <span class="pre">+</span> <span class="pre">1</span> <span class="pre">=</span> <span class="pre">#predictions</span></code></p></li>
+</ul>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>For cropped decoding, the above training setup is mathematically
+similar to sampling crops in your dataset, pushing them through the
+network and training directly on the individual crops. However, the
+if their position would be randomly selected, the crops would be less
+correlated in contrast to the neighbourhood crops selected from a window.
+At the same time, the above training setup is much faster as it avoids
+redundant computations by using dilated convolutions, see <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.
+However, the two setups are only mathematically related in case (1)
+your network does not use any padding or only left padding and
+(2) your loss function leads
+to the same gradients when using the averaged output. The first is true
+for our shallow and deep ConvNet models and the second is true for the
+log-softmax outputs and negative log likelihood loss that is typically
+used for classification in PyTorch.</p>
+</div>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id6" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<p>Loading and preprocessing stays the same as in the <a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Trialwise decoding
+tutorial</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>
+    <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                 <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
+All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
+/home/runner/.local/lib/python3.10/site-packages/moabb/datasets/download.py:55: RuntimeWarning: Setting non-standard config type: &quot;MNE_DATASETS_BNCI_PATH&quot;
+  set_config(key, get_config(&quot;MNE_DATA&quot;))
+/home/runner/.local/lib/python3.10/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host &#39;lampx.tugraz.at&#39;. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
+  warnings.warn(
+
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+  0%|                                              | 0.00/44.1M [00:00&lt;?, ?B/s]
+100%|██████████████████████████████████████| 44.1M/44.1M [00:00&lt;00:00, 234GB/s]
+/home/runner/.local/lib/python3.10/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host &#39;lampx.tugraz.at&#39;. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
+  warnings.warn(
+
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+  0%|                                              | 0.00/42.3M [00:00&lt;?, ?B/s]
+100%|██████████████████████████████████████| 42.3M/42.3M [00:00&lt;00:00, 278GB/s]
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f454240cf40&gt;
+</pre></div>
+</div>
+</section>
+<section id="create-model-and-compute-windowing-parameters">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">Create model and compute windowing parameters</a><a class="headerlink" href="#create-model-and-compute-windowing-parameters" title="Link to this heading">#</a></h2>
+<p>In contrast to trialwise decoding, we first have to create the model
+before we can cut the dataset into windows. This is because we need to
+know the neural network parameters to know how large the sub-window
+stride should be.</p>
+<p>We first choose the compute/input window size that will be fed to the
+network during training. This has to be larger than the networks
+the number of timesteps size and can otherwise be chosen for computational
+efficiency (see explanations in the beginning of this tutorial). Here we
+choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
+</pre></div>
+</div>
+<p>Now we create the model. To enable it to be used in cropped decoding
+efficiently, we manually set the length of the final convolution layer
+to some length that makes the number of timesteps of the ConvNet smaller
+than <code class="docutils literal notranslate"><span class="pre">input_window_samples</span></code> (see <code class="docutils literal notranslate"><span class="pre">final_conv_length=30</span></code> in the model
+definition).</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <span class="n">_</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1000]             [1, 4, 32]                --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1000]             [1, 22, 1000, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1000, 1]          [1, 1, 1000, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1000, 22]          [1, 40, 976, 1]           36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 976, 1]           [1, 40, 976, 1]           80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 976, 1]           [1, 40, 976, 1]           --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 976, 1]           [1, 40, 61, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 61, 1]            [1, 40, 61, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 61, 1]            [1, 4, 32]                --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 61, 1]            [1, 4, 32, 1]             4,804                     [30, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 32, 1]             [1, 4, 32, 1]             --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 32, 1]             [1, 4, 32]                --                        --
+============================================================================================================================================
+Total params: 41,124
+Trainable params: 41,124
+Non-trainable params: 0
+Total mult-adds (M): 0.15
+============================================================================================================================================
+Input size (MB): 0.09
+Forward/backward pass size (MB): 0.31
+Params size (MB): 0.02
+Estimated Total Size (MB): 0.42
+============================================================================================================================================
+</pre></div>
+</div>
+<p>And now we transform model with strides to a model that outputs dense
+prediction, so we can use it to obtain predictions for all
+crops.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../../generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.to_dense_prediction_model" title="braindecode.models.EEGModuleMixin.to_dense_prediction_model" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">to_dense_prediction_model</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<p>To know the models’ output shape without the last layer, we calculate the
+shape of model output for a dummy input.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.get_output_shape" title="braindecode.models.EEGModuleMixin.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">get_output_shape</span></a><span class="p">()[</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="cut-the-data-into-windows">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Cut the data into windows</a><a class="headerlink" href="#cut-the-data-into-windows" title="Link to this heading">#</a></h2>
+<p>In contrast to trialwise decoding, we have to supply an explicit
+window size and window stride to the <code class="docutils literal notranslate"><span class="pre">create_windows_from_events</span></code>
+function.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-the-dataset">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Split the dataset</a><a class="headerlink" href="#split-the-dataset" title="Link to this heading">#</a></h2>
+<p>This code is the same as in trialwise decoding.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id10" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>In difference to trialwise decoding, we now should supply
+<code class="docutils literal notranslate"><span class="pre">cropped=True</span></code> to the EEGClassifier, and <code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code> as the
+criterion, as well as <code class="docutils literal notranslate"><span class="pre">criterion__loss_function</span></code> as the loss function
+applied to the meaned predictions.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for motor decoding, however we strongly
+encourage you to perform your own hyperparameter optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
+
+<span class="c1"># These values we found good for shallow network:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="c1"># For deep4 they should be:</span>
+<span class="c1"># lr = 1 * 0.01</span>
+<span class="c1"># weight_decay = 0.5 * 0.001</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
+    <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.nll_loss.html#torch.nn.functional.nll_loss" title="torch.nn.functional.nll_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
+<span class="c1"># in the dataset.</span>
+<span class="n">_</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  ----------------  ------------  ------  ------
+      1            0.2778        1.5658            0.2535        1.5302  0.0006  6.9939
+      2            0.3090        1.4417            0.2604        1.4977  0.0000  6.7934
+</pre></div>
+</div>
+</section>
+<section id="plot-results">
+<h2><a class="toc-backref" href="#id11" role="doc-backlink">Plot Results</a><a class="headerlink" href="#plot-results" title="Link to this heading">#</a></h2>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Note that we drop further in the classification error and
+loss as in the trialwise decoding tutorial.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span>
+                   <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<span class="c1"># get percent of misclass for better visual comparison to loss</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
+               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/></section>
+<section id="plot-confusion-matrix">
+<h2><a class="toc-backref" href="#id12" role="doc-backlink">Plot Confusion Matrix</a><a class="headerlink" href="#plot-confusion-matrix" title="Link to this heading">#</a></h2>
+<p>Generate a confusion matrix as in <a class="footnote-reference brackets" href="#id5" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<span class="c1"># generate confusion matrices</span>
+<span class="c1"># get the targets</span>
+<span class="n">y_true</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<span class="c1"># generating confusion matrix</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<span class="c1"># add class labels</span>
+<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">window_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;mapping&#39;</span><span class="p">]</span>
+<span class="c1"># sort the labels by values (values are integer class labels)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+
+<span class="c1"># plot the basic conf. matrix</span>
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 640x480 with 1 Axes&gt;
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
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+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id3">2</a>)</span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
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+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model-and-compute-windowing-parameters">Create model and compute windowing parameters</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-the-data-into-windows">Cut the data into windows</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#split-the-dataset">Split the dataset</a></li>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Basic model building and training</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_basic_training_epochs.html">Simple training on MNE epochs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Basic Brain Decoding on EEG Data</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+      
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+  
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+</div>
+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="basic-brain-decoding-on-eeg-data">
+<span id="sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"></span><h1>Basic Brain Decoding on EEG Data<a class="headerlink" href="#basic-brain-decoding-on-eeg-data" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train and test deep learning models with
+Braindecode in a classical EEG setting: you have trials of data with
+labels (e.g., Right Hand, Left Hand, etc.).</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preparing-the-data" id="id8">Loading and preparing the data</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-the-dataset" id="id9">Loading the dataset</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id10">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extracting-compute-windows" id="id11">Extracting Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#splitting-the-dataset-into-training-and-validation-sets" id="id12">Splitting the dataset into training and validation sets</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#creating-a-model" id="id13">Creating a model</a></p></li>
+<li><p><a class="reference internal" href="#model-training" id="id14">Model Training</a></p></li>
+<li><p><a class="reference internal" href="#plotting-results" id="id15">Plotting Results</a></p></li>
+<li><p><a class="reference internal" href="#plotting-a-confusion-matrix" id="id16">Plotting a  Confusion Matrix</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id17">References</a></p></li>
+</ul>
+</nav>
+<section id="loading-and-preparing-the-data">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Loading and preparing the data</a><a class="headerlink" href="#loading-and-preparing-the-data" title="Link to this heading">#</a></h2>
+<section id="loading-the-dataset">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Loading the dataset</a><a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we load the BCI Competition
+IV 2a data <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> using braindecode’s wrapper to load via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB library</a> <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To load your own datasets either via mne or from
+preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
+Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
+Tutorial</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>Now we apply preprocessing like bandpass filtering to our dataset. You
+can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Generally, braindecode prepocessing is directly applied to the loaded
+data, and not applied on-the-fly as transformations, such as in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span><a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                                       <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4542752e60&gt;
+</pre></div>
+</div>
+</section>
+<section id="extracting-compute-windows">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Extracting Compute Windows</a><a class="headerlink" href="#extracting-compute-windows" title="Link to this heading">#</a></h3>
+<p>Now we extract compute windows from the signals, these will be the inputs
+to the deep networks during training. In the case of trialwise
+decoding, we just have to decide if we want to include some part
+before and/or after the trial. For our work with this dataset,
+it was often beneficial to also include the 500 ms before the trial.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="splitting-the-dataset-into-training-and-validation-sets">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Splitting the dataset into training and validation sets</a><a class="headerlink" href="#splitting-the-dataset-into-training-and-validation-sets" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
+<code class="docutils literal notranslate"><span class="pre">T</span></code> for training and <code class="docutils literal notranslate"><span class="pre">test</span></code> for validation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="creating-a-model">
+<h2><a class="toc-backref" href="#id13" role="doc-backlink">Creating a model</a><a class="headerlink" href="#creating-a-model" title="Link to this heading">#</a></h2>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the shallow ConvNet model from <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<span class="c1"># Set random seed to be able to roughly reproduce results</span>
+<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
+<span class="c1"># may still make results substantially different between runs.</span>
+<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
+<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
+<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+<section id="model-training">
+<h2><a class="toc-backref" href="#id14" role="doc-backlink">Model Training</a><a class="headerlink" href="#model-training" title="Link to this heading">#</a></h2>
+<p>Now we will train the network! <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code> is a Braindecode object
+responsible for managing the training of neural networks. It inherits
+from skorch <a class="reference external" href="https://skorch.readthedocs.io/en/stable/classifier.html#">NeuralNetClassifier</a>,
+so the training logic is the same as in <a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>In this tutorial, we use some default parameters that we
+have found to work well for motor decoding, however we strongly
+encourage you to perform your own hyperparameter optimization using
+cross validation on your training data.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="c1"># We found these values to be good for the shallow network:</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+
+<span class="c1"># For deep4 they should be:</span>
+<span class="c1"># lr = 1 * 0.01</span>
+<span class="c1"># weight_decay = 0.5 * 0.001</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for the specified number of epochs. `y` is None as it is</span>
+<span class="c1"># already supplied in the dataset.</span>
+<span class="n">_</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2500        1.5950       0.2535            0.2535        4.6093  0.0006  1.5414
+      2            0.2708        1.2975       0.2535            0.2535        3.4426  0.0005  1.4239
+      3            0.3333        1.2141       0.2674            0.2674        2.7940  0.0002  1.4221
+      4            0.3438        1.1842       0.2778            0.2778        2.4347  0.0000  1.4208
+</pre></div>
+</div>
+</section>
+<section id="plotting-results">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Plotting Results</a><a class="headerlink" href="#plotting-results" title="Link to this heading">#</a></h2>
+<p>Now we use the history stored by Skorch throughout training to plot
+accuracy and loss curves.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
+
+<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
+                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
+
+<span class="c1"># get percent of misclass for better visual comparison to loss</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
+               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax1</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
+
+<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
+    <span class="n">ax</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax2</span></a><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+
+<span class="c1"># where some data has already been plotted to ax</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/></section>
+<section id="plotting-a-confusion-matrix">
+<h2><a class="toc-backref" href="#id16" role="doc-backlink">Plotting a  Confusion Matrix</a><a class="headerlink" href="#plotting-a-confusion-matrix" title="Link to this heading">#</a></h2>
+<p>Here we generate a confusion matrix as in <a class="footnote-reference brackets" href="#id7" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
+
+<span class="c1"># generate confusion matrices</span>
+<span class="c1"># get the targets</span>
+<span class="n">y_true</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
+
+<span class="c1"># generating confusion matrix</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
+
+<span class="c1"># add class labels</span>
+<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">window_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="s1">&#39;mapping&#39;</span><span class="p">]</span>
+<span class="c1"># sort the labels by values (values are integer class labels)</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">label_dict</span></a><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+
+<span class="c1"># plot the basic conf. matrix</span>
+<a href="../../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" srcset="../../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 640x480 with 1 Axes&gt;
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></span>
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2 current active"><a class="current reference internal" href="#">How to train, test and tune your model?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_regression.html">Convolutional neural network regression model on fake data.</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_custom_dataset_example.html">Custom Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_load_save_datasets.html">Load and save dataset example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_mne_dataset_example.html">MNE Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_moabb_dataset_example.html">MOABB Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_split_dataset.html">Split Dataset Example</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</nav></div>
+    </div>
+  
+  
+  <div class="sidebar-primary-items__end sidebar-primary__section">
+  </div>
+  
+  <div id="rtd-footer-container"></div>
+
+
+      </div>
+      
+      <main id="main-content" class="bd-main">
+        
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+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
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+    <div class="header-article-items__start">
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+
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+<nav aria-label="Breadcrumb">
+  <ul class="bd-breadcrumbs">
+    
+    <li class="breadcrumb-item breadcrumb-home">
+      <a href="../../index.html" class="nav-link" aria-label="Home">
+        <i class="fa-solid fa-home"></i>
+      </a>
+    </li>
+    
+    <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Examples</a></li>
+    
+    
+    <li class="breadcrumb-item"><a href="index.html" class="nav-link">Basic model building and training</a></li>
+    
+    <li class="breadcrumb-item active" aria-current="page">How to...</li>
+  </ul>
+</nav>
+</div>
+      
+    </div>
+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="how-to-train-test-and-tune-your-model">
+<span id="sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"></span><h1>How to train, test and tune your model?<a class="headerlink" href="#how-to-train-test-and-tune-your-model" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to properly train, tune and test your deep learning
+models with Braindecode. We will use the BCIC IV 2a dataset <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> as a showcase example.</p>
+<p>The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing of data,
+defining a model, and other details which are not exclusive to this page (see
+<a class="reference external" href="./plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding Tutorial</a>). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.</p>
+<p>In general, we distinguish between “usual” training and evaluation and hyperparameter search.
+The tutorial is therefore split into two parts, one for the three different training schemes
+and one for the two different hyperparameter tuning methods.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#why-should-i-care-about-model-evaluation" id="id8">Why should I care about model evaluation?</a></p></li>
+<li><p><a class="reference internal" href="#loading-and-preprocessing-of-data-defining-a-model-etc" id="id9">Loading and preprocessing of data, defining a model, etc.</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-data" id="id10">Loading data</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing-data" id="id11">Preprocessing data</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-windows" id="id12">Extraction of the Windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-test" id="id13">Split dataset into train and test</a></p></li>
+<li><p><a class="reference internal" href="#create-model" id="id14">Create model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-train-and-evaluate-your-model" id="id15">How to train and evaluate your model</a></p>
+<ul>
+<li><p><a class="reference internal" href="#option-1-simple-train-test-split" id="id16">Option 1: Simple Train-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-first-option-with-a-util-function" id="id17">Let’s visualize the first option with a util function.</a></p></li>
+<li><p><a class="reference internal" href="#option-2-train-val-test-split" id="id18">Option 2: Train-Val-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-second-option-with-a-util-function" id="id19">Let’s visualize the second option with a util function.</a></p></li>
+<li><p><a class="reference internal" href="#option-3-k-fold-cross-validation" id="id20">Option 3: k-Fold Cross Validation</a></p></li>
+<li><p><a class="reference internal" href="#let-s-visualize-the-third-option-with-a-util-function" id="id21">Let’s visualize the third option with a util function.</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-tune-your-hyperparameters" id="id22">How to tune your hyperparameters</a></p>
+<ul>
+<li><p><a class="reference internal" href="#option-1-train-val-test-split" id="id23">Option 1: Train-Val-Test Split</a></p></li>
+<li><p><a class="reference internal" href="#option-2-k-fold-cross-validation" id="id24">Option 2: k-Fold Cross Validation</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#references" id="id25">References</a></p></li>
+</ul>
+</nav>
+<section id="why-should-i-care-about-model-evaluation">
+<h2><a class="toc-backref" href="#id8" role="doc-backlink">Why should I care about model evaluation?</a><a class="headerlink" href="#why-should-i-care-about-model-evaluation" title="Link to this heading">#</a></h2>
+<p>Short answer: To produce reliable results!</p>
+<p>In machine learning, we usually follow the scheme of splitting the
+data into two parts, training and testing sets. It sounds like a
+simple division, right? But the story does not end here.</p>
+<ul class="simple">
+<li><p>What are model’s parameters?</p></li>
+</ul>
+<p>Model’s parameters are learnable weights which are used in the
+extraction of the relevant features and in performing the final inference.
+In the context of deep learning, these are usually fully connected weights,
+convolutional kernels, biases, etc.</p>
+<ul class="simple">
+<li><p>What are model’s hyperparameters?</p></li>
+</ul>
+<p>Model’s hyperparameters are used to set the capacity (size) of the model
+and to guide the parameter learning process.
+In the context of deep learning, examples of the hyperparameters are the
+number of convolutional layers and the number of convolutional kernels in
+each of them, the number and size of the fully connected weights,
+choice of the optimizer and its learning rate, the number of training epochs,
+choice of the nonlinearities, etc.</p>
+<p>While developing a ML model you usually have to adjust and tune
+hyperparameters of your model or pipeline (e.g., number of layers,
+learning rate, number of epochs). Deep learning models usually have
+many free parameters; they could be considered as complex models with
+many degrees of freedom. If you kept using the test dataset to
+evaluate your adjustment, you would run into data leakage.</p>
+<p>This means that if you use the test set to adjust the hyperparameters
+of your model, the model implicitly learns or memorizes the test set.
+Therefore, the trained model is no longer independent of the test set
+(even though it was never used for training explicitly!).
+If you perform any hyperparameter tuning, you need a third split,
+the so-called validation set.</p>
+<p>This tutorial shows the three basic schemes for training and evaluating
+the model as well as two methods to tune your hyperparameters.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>You might recognize that the accuracy gets better throughout
+the experiments of this tutorial. The reason behind that is that
+we always use the same model with the same parameters in every
+segment to keep the tutorial short and readable. If you do your
+own experiments you always have to reinitialize the model before
+training.</p>
+</div>
+</section>
+<section id="loading-and-preprocessing-of-data-defining-a-model-etc">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Loading and preprocessing of data, defining a model, etc.</a><a class="headerlink" href="#loading-and-preprocessing-of-data-defining-a-model-etc" title="Link to this heading">#</a></h2>
+<section id="loading-data">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Loading data</a><a class="headerlink" href="#loading-data" title="Link to this heading">#</a></h3>
+<p>In this example, we load the BCI Competition IV 2a data <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>, for one
+subject (subject id 3), using braindecode’s wrapper to load via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB library</a> <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing-data">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Preprocessing data</a><a class="headerlink" href="#preprocessing-data" title="Link to this heading">#</a></h3>
+<p>In this example, preprocessing includes signal rescaling, the bandpass filtering
+(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+the exponential moving mean and variance.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.0</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.0</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;pick_types&quot;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+        <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
+    <span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;filter&quot;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Preprocess the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f45456962c0&gt;
+</pre></div>
+</div>
+</section>
+<section id="extraction-of-the-windows">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Extraction of the Windows</a><a class="headerlink" href="#extraction-of-the-windows" title="Link to this heading">#</a></h3>
+<p>Extraction of the trials (windows) from the time series is based on the
+events inside the dataset. One event is the demarcation of the stimulus or
+the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+before the corresponding event and the duration of the event itself.
+#Therefore, we set the <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> to -0.5 [s] and the
+<code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> to 0 [s].</p>
+<p>We extract from the dataset the sampling frequency, which is the same for
+all datasets in this case, and we tested it.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> and <code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> are
+defined in seconds and need to be converted into samples (multiplication
+with the sampling frequency), relative to the event.
+This variable is dataset dependent.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the window start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
+<span class="c1"># define how windows should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-test">
+<h3><a class="toc-backref" href="#id13" role="doc-backlink">Split dataset into train and test</a><a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset BCIC IV 2a dataset using additional
+info stored in the description attribute, in this case the <code class="docutils literal notranslate"><span class="pre">session</span></code>
+column. We select <code class="docutils literal notranslate"><span class="pre">0train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">0test</span></code> for testing.
+For other datasets, you might have to choose another column and/or column.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>No matter which of the three schemes you use, this initial
+two-fold split into train_set and test_set always remains the same.
+Remember that you are not allowed to use the test_set during any
+stage of training or tuning.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="create-model">
+<h3><a class="toc-backref" href="#id14" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h3>
+<p>In this tutorial, ShallowFBCSPNet classifier <a class="footnote-reference brackets" href="#id7" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a> is explored. The model
+training is performed on GPU if it exists, otherwise on CPU.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-train-and-evaluate-your-model">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">How to train and evaluate your model</a><a class="headerlink" href="#how-to-train-and-evaluate-your-model" title="Link to this heading">#</a></h2>
+<section id="option-1-simple-train-test-split">
+<h3><a class="toc-backref" href="#id16" role="doc-backlink">Option 1: Simple Train-Test Split</a><a class="headerlink" href="#option-1-simple-train-test-split" title="Link to this heading">#</a></h3>
+<p>This is the easiest training scheme to use as the dataset is only
+split into two distinct sets (<code class="docutils literal notranslate"><span class="pre">train_set</span></code> and <code class="docutils literal notranslate"><span class="pre">test_set</span></code>).
+This scheme uses no separate validation split and should only be
+used for the final evaluation of the (previously!) found
+hyperparameters configuration.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>If you make any use of the <code class="docutils literal notranslate"><span class="pre">test_set</span></code> during training
+(e.g. by using EarlyStopping) there will be data leakage
+which will make the reported generalization capability/decoding
+performance of your model less credible.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
+<span class="c1"># in the dataset.</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># evaluated the model after training</span>
+<span class="n">y_test</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2500        1.5950  0.0006  1.1996
+      2            0.2500        1.3661  0.0000  1.1111
+Test acc: 25.35%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-first-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id17" role="doc-backlink">Let’s visualize the first option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-first-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<p>The following figure illustrates split of entire dataset into the
+training and testing subsets.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a>
+
+<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">font_scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">plot_simple_train_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training-testing split.&quot;&quot;&quot;</span>
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">),</span>
+            <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Test split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower center&#39;</span><span class="p">,</span>
+              <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+<span class="n">plot_simple_train_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+                       <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_001.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_001.png" alt="Train-Test split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;Train-Test split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+<section id="option-2-train-val-test-split">
+<h3><a class="toc-backref" href="#id18" role="doc-backlink">Option 2: Train-Val-Test Split</a><a class="headerlink" href="#option-2-train-val-test-split" title="Link to this heading">#</a></h3>
+<p>When evaluating different settings hyperparameters for your model,
+there is still a risk of overfitting on the test set because the
+parameters can be tweaked until the estimator performs optimally.
+For more information visit <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html">sklearns Cross-Validation Guide</a>.
+This second option splits the original <code class="docutils literal notranslate"><span class="pre">train_set</span></code> into two distinct
+sets, the training set and the validation set to avoid overfitting
+the hyperparameters to the test set.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If your dataset is really small, the validation split can become
+quite small. This may lead to unreliable tuning results. To
+avoid this, either use Option 3 or adjust the split ratio.</p>
+</div>
+<p>To split the <code class="docutils literal notranslate"><span class="pre">train_set</span></code> we will make use of the
+<code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument of <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code>. If you leave this empty
+(not None!), skorch will make an 80-20 train-validation split.
+If you want to control the split manually you can do that by using
+<code class="docutils literal notranslate"><span class="pre">Subset</span></code> from torch and <code class="docutils literal notranslate"><span class="pre">predefined_split</span></code> from skorch.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span><span class="p">,</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+
+<span class="n">X_train</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
+    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The parameter <code class="docutils literal notranslate"><span class="pre">shuffle</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>. For time-series
+data this should always be the case as shuffling might take
+advantage of correlated samples, which would make the validation
+performance less meaningful.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">),</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+<span class="c1"># evaluate the model after training and validation</span>
+<span class="n">y_test</span> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.3435        1.3505       0.2759            0.2759        2.2666  0.0006  0.9979
+      2            0.3478        1.1789       0.2759            0.2759        2.0685  0.0000  0.9027
+Test acc: 27.43%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-second-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id19" role="doc-backlink">Let’s visualize the second option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-second-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<p>The following figure illustrates split of entire dataset into the
+training, validation and testing subsets.
+<code class="docutils literal notranslate"><span class="pre">Making</span> <span class="pre">more</span> <span class="pre">compact</span> <span class="pre">plot_train_valid_test</span> <span class="pre">function.</span></code></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_train_valid_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training, validation, testing.&quot;&quot;&quot;</span>
+
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="p">]</span>
+
+    <span class="n">n_train</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Train-Test-Valid</span><span class="se">\n</span><span class="s2">split&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Test-Valid split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
+              <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower center&quot;</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">))</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">plot_train_valid_test</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="o">=</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+                      <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_002.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_002.png" alt="Train-Test-Valid split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;Train-Test-Valid split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+<section id="option-3-k-fold-cross-validation">
+<h3><a class="toc-backref" href="#id20" role="doc-backlink">Option 3: k-Fold Cross Validation</a><a class="headerlink" href="#option-3-k-fold-cross-validation" title="Link to this heading">#</a></h3>
+<p>As mentioned above, using only one validation split might not be
+sufficient, as there might be a shift in the data distribution.
+To compensate for this, one can run a k-fold Cross Validation,
+where every sample of the training set is in the validation set once.
+After averaging over the k validation scores afterwards, you get a
+very reliable estimate of how the model would perform on unseen
+data (test set).</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This k-Fold Cross Validation can be used without a separate
+(holdout) test set. If there is no test set available, e.g. in a
+competition, this scheme is highly recommended to get a reliable
+estimate of the generalization performance.</p>
+</div>
+<p>To implement this, we will make use of sklearn function
+<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html">cross_val_score</a> and the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">KFold</a>. CV splitter.
+The <code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument has to be set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, as sklearn
+will take care of the splitting.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a><span class="p">,</span>
+    <span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="c1"># By setting n_jobs=-1, cross-validation is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a><span class="p">(</span>
+    <span class="n">clf</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Validation accuracy: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;+-</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.3870        1.2082  0.0006  0.9258
+      2            0.3957        1.1560  0.0000  0.8180
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2870        1.2641  0.0006  0.8551
+      2            0.2870        1.2266  0.0000  0.8161
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4000        1.3003  0.0006  0.8396
+      2            0.4087        1.1477  0.0000  0.8394
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2727        1.2076  0.0006  0.8392
+      2            0.2900        1.1233  0.0000  0.8413
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4242        1.1577  0.0006  0.8404
+      2            0.4286        1.0504  0.0000  0.8377
+Validation accuracy: 31.94+-5.59%
+</pre></div>
+</div>
+</section>
+<section id="let-s-visualize-the-third-option-with-a-util-function">
+<h3><a class="toc-backref" href="#id21" role="doc-backlink">Let’s visualize the third option with a util function.</a><a class="headerlink" href="#let-s-visualize-the-third-option-with-a-util-function" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_k_fold</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">cv</span><span class="p">,</span> <span class="n">all_dataset</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a sample plot for training, validation, testing.&quot;&quot;&quot;</span>
+
+    <span class="n">bd_cmap</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="p">]</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">Dataset&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="c1"># Generate the training/validation/testing data fraction visualizations for each CV split</span>
+    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr_idx</span><span class="p">,</span> <span class="n">val_idx</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)):</span>
+        <span class="n">n_train</span><span class="p">,</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tr_idx</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)</span>
+        <span class="n">n_train2</span> <span class="o">=</span> <span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span> <span class="o">-</span> <span class="nb">max</span><span class="p">(</span><span class="n">val_idx</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="nb">min</span><span class="p">(</span><span class="n">val_idx</span><span class="p">),</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_train2</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="nb">max</span><span class="p">(</span><span class="n">val_idx</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.barh.html#matplotlib.axes.Axes.barh" title="matplotlib.axes.Axes.barh" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">barh</span></a><span class="p">(</span><span class="s2">&quot;cv&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">ii</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">n_test</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="n">n_train</span> <span class="o">+</span> <span class="n">n_val</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.invert_yaxis.html#matplotlib.axes.Axes.invert_yaxis" title="matplotlib.axes.Axes.invert_yaxis" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span></a><span class="p">()</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html#matplotlib.axes.Axes.set_xlim" title="matplotlib.axes.Axes.set_xlim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span></a><span class="p">([</span><span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="mf">0.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">)),</span> <span class="nb">int</span><span class="p">(</span><span class="mf">1.1</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_dataset</span><span class="p">))])</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;KFold Train-Test-Valid split&quot;</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">bd_cmap</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">)],</span>
+              <span class="p">[</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
+              <span class="n">loc</span><span class="o">=</span><span class="s2">&quot;lower center&quot;</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">text</span></a><span class="p">(</span><span class="o">-</span><span class="mf">0.07</span><span class="p">,</span> <span class="mf">0.45</span><span class="p">,</span> <span class="s1">&#39;Train-Valid-Test split&#39;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
+            <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><a href="https://matplotlib.org/stable/api/transformations.html#matplotlib.transforms.BboxTransformTo" title="matplotlib.transforms.BboxTransformTo" class="sphx-glr-backref-module-matplotlib-transforms sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span><span class="o">.</span><span class="n">transAxes</span></a><span class="p">)</span>
+    <span class="k">return</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
+<span class="n">plot_k_fold</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span> <span class="n">all_dataset</span><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
+            <span class="n">X_train</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,)</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_how_train_test_and_tune_003.png" srcset="../../_images/sphx_glr_plot_how_train_test_and_tune_003.png" alt="KFold Train-Test-Valid split" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Axes: title={&#39;center&#39;: &#39;KFold Train-Test-Valid split&#39;}, xlabel=&#39;Number of samples.&#39;&gt;
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-tune-your-hyperparameters">
+<h2><a class="toc-backref" href="#id22" role="doc-backlink">How to tune your hyperparameters</a><a class="headerlink" href="#how-to-tune-your-hyperparameters" title="Link to this heading">#</a></h2>
+<p>One way to do hyperparameter tuning is to run each configuration
+manually (via Option 2 or 3 from above) and compare the validation
+performance afterwards. In the early stages of your development
+process this might be sufficient to get a rough understanding of
+how your hyperparameter should look like for your model to converge.
+However, this manual tuning process quickly becomes messy as the
+number of hyperparameters you want to (jointly) tune increases.
+Therefore you should, automate this process. We will present two
+different options, analogous to Option 2 and 3 from above.</p>
+<section id="option-1-train-val-test-split">
+<h3><a class="toc-backref" href="#id23" role="doc-backlink">Option 1: Train-Val-Test Split</a><a class="headerlink" href="#option-1-train-val-test-split" title="Link to this heading">#</a></h3>
+<p>We will again make use of the <a class="reference external" href="https://scikit-learn.org/stable/">sklearn</a>
+library to do the hyperparameter search. <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a>
+will perform a Grid Search over the parameters specified in <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>.
+We use grid search for the model selection as a simple example, but you can use other strategies
+as well.
+(<a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection">List of the sklearn classes for model selection</a>.)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <span class="p">[</span>
+    <span class="nb">tuple</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
+<span class="p">]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;optimizer__lr&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.000625</span><span class="p">],</span>
+<span class="p">}</span>
+
+<span class="c1"># By setting n_jobs=-1, grid search is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+
+<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s2">&quot;rank_test_score&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">best_parameters</span></a> <span class="o">=</span> <span class="n">best_run</span><span class="p">[</span><span class="s2">&quot;params&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 1 folds for each of 2 candidates, totalling 2 fits
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.2826        1.5258  0.0063  0.9229
+      2            0.4000        1.4053  0.0000  0.8381
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4826        1.2205  0.0006  0.8599
+      2            0.5130        1.1218  0.0000  0.8271
+  epoch    train_accuracy    train_loss      lr     dur
+-------  ----------------  ------------  ------  ------
+      1            0.4375        1.2991  0.0006  1.1781
+      2            0.4688        1.1161  0.0000  1.1094
+</pre></div>
+</div>
+</section>
+<section id="option-2-k-fold-cross-validation">
+<h3><a class="toc-backref" href="#id24" role="doc-backlink">Option 2: k-Fold Cross Validation</a><a class="headerlink" href="#option-2-k-fold-cross-validation" title="Link to this heading">#</a></h3>
+<p>To perform a full k-Fold CV just replace <code class="docutils literal notranslate"><span class="pre">train_val_split</span></code> from
+above with the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">KFold</a> cross-validator from sklearn.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id25" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>)</span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">3</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
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+        <p class="prev-next-title">Hyperparameter tuning with scikit-learn</p>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-should-i-care-about-model-evaluation">Why should I care about model evaluation?</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-of-data-defining-a-model-etc">Loading and preprocessing of data, defining a model, etc.</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-data">Loading data</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing-data">Preprocessing data</a></li>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-test">Split dataset into train and test</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-train-and-evaluate-your-model">How to train and evaluate your model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-simple-train-test-split">Option 1: Simple Train-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-first-option-with-a-util-function">Let’s visualize the first option with a util function.</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-train-val-test-split">Option 2: Train-Val-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-second-option-with-a-util-function">Let’s visualize the second option with a util function.</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-3-k-fold-cross-validation">Option 3: k-Fold Cross Validation</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#let-s-visualize-the-third-option-with-a-util-function">Let’s visualize the third option with a util function.</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-tune-your-hyperparameters">How to tune your hyperparameters</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-train-val-test-split">Option 1: Train-Val-Test Split</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-k-fold-cross-validation">Option 2: k-Fold Cross Validation</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html">Training a Braindecode model in PyTorch</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
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+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
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+      
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+</div>
+              
+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="hyperparameter-tuning-with-scikit-learn">
+<span id="sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"></span><h1>Hyperparameter tuning with scikit-learn<a class="headerlink" href="#hyperparameter-tuning-with-scikit-learn" title="Link to this heading">#</a></h1>
+<p>The braindecode provides some compatibility with
+<a class="reference external" href="https://scikit-learn.org/stable/">scikit-learn</a>. This allows us
+to use scikit-learn functionality to find the best hyperparameters for our
+model. This is especially useful to tune hyperparameters or
+parameters for one decoding task or a specific dataset.</p>
+<aside class="topic">
+<p class="topic-title">Why do you need to tune the neural networks model?</p>
+<p>Deep learning models are often sensitive to the choice of hyperparameters
+and parameters. Hyperparameters are the parameters set before
+training the model. The hyeperparameters determine (1) the capacity of the model,
+e.g. its depth (the number of layers) and its width (the number of
+convolutional kernels, sizes of fully connected layers) and (2) the
+learning process via the choice of optimizer and its learning rate,
+the number of epochs, the batch size, the choice of non-linearities,
+the strategies to initialize the learning weights, etc.
+On the other hand, parameters are learned during training,
+such as the neural network weights. The choice of these can have a
+significant impact on the performance of the model.
+Therefore, it is important to tune these to maximize the discriminative
+power of the model, in the case of decoding tasks (classification,
+regression, etc.), such as sleep staging, BCI, pathology detection, etc.
+We recommend the Deep Learning Tuning Playbook by Google to learn more
+about hyperparameters and parameters tuning <a class="footnote-reference brackets" href="#id5" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.</p>
+</aside>
+<p>In this tutorial, we will use the standard decoding approach to show the impact
+of the learning rate and dropout probability on the model’s performance.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id9">Loading and preprocessing the dataset</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading" id="id10">Loading</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing" id="id11">Preprocessing</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-compute-windows" id="id12">Extraction of the Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#extraction-of-the-windows" id="id13">Extraction of the Windows</a></p></li>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id14">Split dataset into train and valid</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#create-model" id="id15">Create model</a></p></li>
+<li><p><a class="reference internal" href="#training" id="id16">Training</a></p></li>
+<li><p><a class="reference internal" href="#plotting-the-results" id="id17">Plotting the results</a></p></li>
+<li><p><a class="reference internal" href="#get-the-best-hyperparameters" id="id18">Get the best hyperparameters</a></p></li>
+<li><p><a class="reference internal" href="#references" id="id19">References</a></p></li>
+</ul>
+</nav>
+<section id="loading-and-preprocessing-the-dataset">
+<h2><a class="toc-backref" href="#id9" role="doc-backlink">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Link to this heading">#</a></h2>
+<section id="loading">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Loading</a><a class="headerlink" href="#loading" title="Link to this heading">#</a></h3>
+<p>First, we load the data. In this tutorial, we use the functionality of
+braindecode to load datasets via
+<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB</a> <a class="footnote-reference brackets" href="#id6" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a> to load the BCI
+Competition IV 2a data <a class="footnote-reference brackets" href="#id7" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>To load your own datasets either via mne or from
+preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
+Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
+Tutorial</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.moabb</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>BNCI2014001 has been renamed to BNCI2014_001. BNCI2014001 will be removed in version 1.1.
+The dataset class name &#39;BNCI2014001&#39; must be an abbreviation of its code &#39;BNCI2014-001&#39;. See moabb.datasets.base.is_abbrev for more information.
+</pre></div>
+</div>
+</section>
+<section id="preprocessing">
+<h3><a class="toc-backref" href="#id11" role="doc-backlink">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h3>
+<p>In this example, preprocessing includes signal rescaling, the bandpass filtering
+(low and high cut-off frequencies are 4 and 38 Hz) and the standardization using
+the exponential moving mean and variance.
+You can either apply functions provided by
+<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html">mne.Epochs</a>
+or apply your own functions, either to the MNE object or the underlying
+numpy array.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>These prepocessings are now directly applied to the loaded
+data, and not on-the-fly applied as transformations in
+PyTorch-libraries like
+<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+<span class="c1"># Factor to convert from V to uV</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+    <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>
+    <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+                 <span class="c1"># Exponential moving standardization</span>
+                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
+<span class="p">]</span>
+
+<span class="c1"># Preprocess the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4542305600&gt;
+</pre></div>
+</div>
+</section>
+<section id="extraction-of-the-compute-windows">
+<h3><a class="toc-backref" href="#id12" role="doc-backlink">Extraction of the Compute Windows</a><a class="headerlink" href="#extraction-of-the-compute-windows" title="Link to this heading">#</a></h3>
+</section>
+<section id="extraction-of-the-windows">
+<h3><a class="toc-backref" href="#id13" role="doc-backlink">Extraction of the Windows</a><a class="headerlink" href="#extraction-of-the-windows" title="Link to this heading">#</a></h3>
+<p>Extraction of the trials (windows) from the time series is based on the
+events inside the dataset. One event is the demarcation of the stimulus or
+the beginning of the trial. In this example, we want to analyse 0.5 [s] long
+before the corresponding event and the duration of the event itself.
+#Therefore, we set the <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> to -0.5 [s] and the
+<code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> to 0 [s].</p>
+<p>We extract from the dataset the sampling frequency, which is the same for
+all datasets in this case, and we tested it.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">trial_start_offset_seconds</span></code> and <code class="docutils literal notranslate"><span class="pre">trial_stop_offset_seconds</span></code> are
+defined in seconds and need to be converted into samples (multiplication
+with the sampling frequency), relative to the event.
+This variable is dataset dependent.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="split-dataset-into-train-and-valid">
+<h3><a class="toc-backref" href="#id14" role="doc-backlink">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
+<code class="docutils literal notranslate"><span class="pre">0train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">1test</span></code> for evaluation.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="create-model">
+<h2><a class="toc-backref" href="#id15" role="doc-backlink">Create model</a><a class="headerlink" href="#create-model" title="Link to this heading">#</a></h2>
+<p>Now we create the deep learning model! Braindecode comes with some
+predefined convolutional neural network architectures for raw
+time-domain EEG. Here, we use the ShallowFBCSPNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
+learning with convolutional neural networks for EEG decoding and
+visualization</a> <a class="footnote-reference brackets" href="#id8" id="id4" role="doc-noteref"><span class="fn-bracket">[</span>4<span class="fn-bracket">]</span></a>. These models are
+pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
+to use your own model, it just has to be a normal PyTorch
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">partial</span></a>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+
+<span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>  <span class="c1"># random seed to make results reproducible</span>
+<span class="c1"># Set random seed to be able to reproduce results</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># To analyze the impact of the different parameters inside the torch model, we</span>
+<span class="c1"># need to create partial initialisations. This is because the</span>
+<span class="c1"># GridSearchCV of scikit-learn will try to initialize the model with the</span>
+<span class="c1"># parameters we want to tune. If we do not do this, the GridSearchCV will</span>
+<span class="c1"># try to initialize the model with the parameters we want to tune but</span>
+<span class="c1"># without the parameters we do not want to tune. This will result in an</span>
+<span class="c1"># error.</span>
+<a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">partial</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+                <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+                <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="training">
+<h2><a class="toc-backref" href="#id16" role="doc-backlink">Training</a><a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
+<p>Now we train the network! EEGClassifier is a Braindecode object
+responsible for managing the training of neural networks. It inherits
+from <a class="reference external" href="https://skorch.readthedocs.io/en/latest/classifier.html">skorch.NeuralNetClassifier</a>,
+so the training logic is the same as in
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.dataset</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a>
+<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">16</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+
+<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functools.html#functools.partial" title="functools.partial" class="sphx-glr-backref-module-functools sphx-glr-backref-type-py-function"><span class="n">model</span></a><span class="p">,</span>
+    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+    <span class="n">optimizer__lr</span><span class="o">=</span><span class="p">[],</span>  <span class="c1"># This will be handled by GridSearchCV</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/dataset.html#skorch.dataset.ValidSplit" title="skorch.dataset.ValidSplit" class="sphx-glr-backref-module-skorch-dataset sphx-glr-backref-type-py-class"><span class="n">ValidSplit</span></a><span class="p">(</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">),</span>
+    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
+        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
+        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
+    <span class="p">],</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>We use scikit-learn <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a> to tune hyperparameters.
+To be able to do this, we slice the braindecode datasets that by default
+return a 3-tuple to return X and y, respectively.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The KFold object splits the datasets based on their
+length which corresponds to the number of compute windows. In
+this (trialwise) example this is fine to do. In a cropped setting
+this is not advisable since this might split compute windows
+of a single trial into both train and valid set.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
+<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="n">train_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">learning_rates</span></a> <span class="o">=</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.0000625</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">drop_probs</span></a> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
+    <span class="s1">&#39;optimizer__lr&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">learning_rates</span></a><span class="p">,</span>
+    <span class="s1">&#39;module__drop_prob&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">drop_probs</span></a>
+<span class="p">}</span>
+
+<span class="c1"># By setting n_jobs=-1, grid search is performed</span>
+<span class="c1"># with all the processors, in this case the output of the training</span>
+<span class="c1"># process is not printed sequentially</span>
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
+    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
+    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
+    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
+    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">,</span>
+    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><span class="n">train_X</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
+
+<span class="c1"># Extract the results into a DataFrame</span>
+<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 6 candidates, totalling 12 fits
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.5304        2.0774       0.1724            0.1724        3.0998  0.0063  0.5656
+      2            0.6174        1.1537       0.2069            0.2069        2.8704  0.0000  0.5503
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.3565        2.5936       0.3448            0.3448        2.3154  0.0063  0.5648
+      2            0.5130        1.1784       0.3448            0.3448        1.8569  0.0000  0.5300
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2348        1.5185       0.2759            0.2759        3.0333  0.0001  0.5275
+      2            0.2348        1.4272       0.2759            0.2759        2.3804  0.0000  0.5491
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2000        1.6046       0.3103            0.3103        4.8912  0.0001  0.5509
+      2            0.2000        1.3911       0.3103            0.3103        3.7741  0.0000  0.5318
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.5304        1.7828       0.1724            0.1724        2.9789  0.0063  0.5332
+      2            0.5826        1.1250       0.1724            0.1724        2.5773  0.0000  0.5752
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2609        2.5664       0.3793            0.3793        3.1215  0.0063  0.5432
+      2            0.4696        1.4854       0.4483            0.4483        1.7465  0.0000  0.5506
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2261        1.7070       0.3103            0.3103        2.1473  0.0001  0.5476
+      2            0.2435        1.6492       0.3103            0.3103        1.8568  0.0000  0.5280
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2783        1.5837       0.2069            0.2069        2.4035  0.0001  0.5286
+      2            0.2696        1.5464       0.2069            0.2069        2.1272  0.0000  0.5565
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2609        2.1099       0.3103            0.3103        3.2171  0.0063  0.5508
+      2            0.4696        1.8860       0.3448            0.3448        1.9000  0.0000  0.5290
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.4087        2.1420       0.4138            0.4138        1.5093  0.0063  0.5286
+      2            0.5130        1.5210       0.3793            0.3793        1.3900  0.0000  0.5512
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2348        2.1793       0.2759            0.2759        9.8493  0.0001  0.5529
+      2            0.2348        1.9960       0.2759            0.2759        7.3377  0.0000  0.5363
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2000        2.2516       0.3103            0.3103        2.5048  0.0001  0.5304
+      2            0.1913        2.1263       0.3448            0.3448        2.0232  0.0000  0.5541
+Can only infer signal shape of numpy arrays or and Datasets, got &lt;class &#39;skorch.helper.SliceDataset&#39;&gt;.
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+  epoch    train_accuracy    train_loss    valid_acc    valid_accuracy    valid_loss      lr     dur
+-------  ----------------  ------------  -----------  ----------------  ------------  ------  ------
+      1            0.2739        2.2403       0.2586            0.2586        6.1557  0.0063  1.0682
+      2            0.5304        1.7535       0.3448            0.3448        1.9383  0.0000  1.0583
+</pre></div>
+</div>
+</section>
+<section id="plotting-the-results">
+<h2><a class="toc-backref" href="#id17" role="doc-backlink">Plotting the results</a><a class="headerlink" href="#plotting-the-results" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
+
+
+<span class="c1"># Create a pivot table for the heatmap</span>
+<span class="n">pivot_table</span> <span class="o">=</span> <span class="n">search_results</span><span class="o">.</span><span class="n">pivot</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="s1">&#39;param_optimizer__lr&#39;</span><span class="p">,</span>
+                                   <span class="n">columns</span><span class="o">=</span><span class="s1">&#39;param_module__drop_prob&#39;</span><span class="p">,</span>
+                                   <span class="n">values</span><span class="o">=</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">)</span>
+<span class="c1"># Create the heatmap</span>
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
+<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">pivot_table</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s2">&quot;.3f&quot;</span><span class="p">,</span>
+            <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;YlGnBu&quot;</span><span class="p">,</span> <span class="n">cbar</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="matplotlib.pyplot.title" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="s1">&#39;Grid Search Mean Test Scores&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html#matplotlib.pyplot.ylabel" title="matplotlib.pyplot.ylabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span></a><span class="p">(</span><span class="s1">&#39;Learning Rate&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html#matplotlib.pyplot.xlabel" title="matplotlib.pyplot.xlabel" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span></a><span class="p">(</span><span class="s1">&#39;Dropout Probability&#39;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_001.png" srcset="../../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_001.png" alt="Grid Search Mean Test Scores" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py:332: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
+  pivot_table = search_results.pivot(index=&#39;param_optimizer__lr&#39;,
+/home/runner/work/braindecode/braindecode/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.py:332: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)
+  pivot_table = search_results.pivot(index=&#39;param_optimizer__lr&#39;,
+</pre></div>
+</div>
+</section>
+<section id="get-the-best-hyperparameters">
+<h2><a class="toc-backref" href="#id18" role="doc-backlink">Get the best hyperparameters</a><a class="headerlink" href="#get-the-best-hyperparameters" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span>
+    <span class="sa">f</span><span class="s2">&quot;Best hyperparameters were </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> which gave a validation &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">% (training &quot;</span>
+    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%).&quot;</span><span class="p">)</span>
+
+<span class="n">eval_X</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">eval_y</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="collections.abc.Sequence" class="sphx-glr-backref-module-collections-abc sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><span class="n">eval_X</span><span class="p">,</span> <span class="n">eval_y</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%.&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best hyperparameters were {&#39;module__drop_prob&#39;: 0.2, &#39;optimizer__lr&#39;: 0.00625} which gave a validation accuracy of 32.64% (training accuracy of 50.69%).
+Eval accuracy is 34.72%.
+</pre></div>
+</div>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id19" role="doc-backlink">References</a><a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Varun Godbole, George E. Dahl, Justin Gilmer, Christopher J. Shallue,
+Zachary Nado (2022). Deep Learning Tuning Playbook.
+Github <a class="github reference external" href="https://github.com/google-research/tuning_playbook">google-research/tuning_playbook</a></p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Jayaram, Vinay, and Alexandre Barachant.
+“MOABB: trustworthy algorithm benchmarking for BCIs.”
+Journal of neural engineering 15.6 (2018): 066011.</p>
+</aside>
+<aside class="footnote brackets" id="id7" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C.,
+Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G.
+and Nolte, G., 2012. Review of the BCI competition IV.
+Frontiers in neuroscience, 6, p.55.</p>
+</aside>
+<aside class="footnote brackets" id="id8" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">4</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R.T., Springenberg, J.T., Fiederer, L.D.J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W. and Ball, T. (2017),
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Hum. Brain Mapping, 38: 5391-5420. <a class="reference external" href="https://doi.org/10.1002/hbm.23730">https://doi.org/10.1002/hbm.23730</a>.</p>
+</aside>
+</aside>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 28.100 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  164 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/5adeee55d3085fdf6cf644f6c7d12084/plot_hyperparameter_tuning_with_scikit-learn.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_hyperparameter_tuning_with_scikit-learn.py</span></code></a></p>
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+<p><a class="reference download internal" download="" href="../../_downloads/a39e4245738e55e0eb7084c545ed05bc/plot_hyperparameter_tuning_with_scikit-learn.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_hyperparameter_tuning_with_scikit-learn.ipynb</span></code></a></p>
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+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-and-preprocessing-the-dataset">Loading and preprocessing the dataset</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading">Loading</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing">Preprocessing</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extraction-of-the-compute-windows">Extraction of the Compute Windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#extraction-of-the-windows">Extraction of the Windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-valid">Split dataset into train and valid</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#create-model">Create model</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-the-results">Plotting the results</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#get-the-best-hyperparameters">Get the best hyperparameters</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#references">References</a></li>
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+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="convolutional-neural-network-regression-model-on-fake-data">
+<span id="sphx-glr-auto-examples-model-building-plot-regression-py"></span><h1>Convolutional neural network regression model on fake data.<a class="headerlink" href="#convolutional-neural-network-regression-model-on-fake-data" title="Link to this heading">#</a></h1>
+<p>This example shows how to create a CNN regressor from a CNN classifier by removing <cite>softmax</cite>
+function from the classifier’s output layer and how to train it on a fake regression dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
+<span class="c1">#          Sara Sedlar &lt;sara.sedlar@gmail.com&gt;</span>
+<span class="c1"># License: BSD-3</span>
+</pre></div>
+</div>
+<section id="fake-regression-data">
+<h2>Fake regression data<a class="headerlink" href="#fake-regression-data" title="Link to this heading">#</a></h2>
+<p>Function for generation of the fake regression dataset generates <cite>n_fake_recs</cite> recordings,
+each containing sinusoidal signals with Gaussian noise. Each fake recording signal has
+<cite>n_fake_chs</cite> channels, it lasts <cite>fake_duration</cite> [s] and it is sampled with <cite>fake_sfreq</cite> [Hz].
+The recordings are split into train, validation and testing sessions.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">,</span> <a href="../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <span class="n">create_mne_dummy_raw</span>
+</pre></div>
+</div>
+<p>Function for generating fake regression data</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">fake_regression_dataset</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">,</span> <span class="n">n_fake_chs</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                            <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                            <span class="n">fake_data_split</span><span class="o">=</span><span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Generate a fake regression dataset.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    n_fake_recs : int</span>
+<span class="sd">        Number of fake recordings.</span>
+<span class="sd">    n_fake_chs : int</span>
+<span class="sd">        Number of fake EEG channels.</span>
+<span class="sd">    fake_sfreq : float</span>
+<span class="sd">        Fake sampling frequency in Hz.</span>
+<span class="sd">    fake_duration : float</span>
+<span class="sd">        Fake recording duration in seconds.</span>
+<span class="sd">    n_fake_targets : int</span>
+<span class="sd">        Number of targets.</span>
+<span class="sd">    fake_data_split : list</span>
+<span class="sd">        List of train/valid/test subset fractions.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    dataset : BaseConcatDataset object</span>
+<span class="sd">        The generated dataset object.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">datasets</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">int</span><span class="p">(</span><span class="n">fake_data_split</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">n_fake_recs</span><span class="p">):</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;train&quot;</span>
+        <span class="k">elif</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">int</span><span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">fake_data_split</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span> <span class="o">*</span> <span class="n">n_fake_recs</span><span class="p">):</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;valid&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">target_subset</span> <span class="o">=</span> <span class="s2">&quot;test&quot;</span>
+        <span class="n">raw</span><span class="p">,</span> <a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <span class="n">create_mne_dummy_raw</span><span class="p">(</span><span class="n">n_channels</span><span class="o">=</span><span class="n">n_fake_chs</span><span class="p">,</span>
+                                      <span class="n">n_times</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                                      <span class="n">sfreq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">)</span>
+
+        <span class="n">target</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">sin</span></a><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">pi</span></a> <span class="o">*</span> <span class="n">target</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">raw</span><span class="o">.</span><span class="n">times</span><span class="p">)</span>
+            <span class="n">raw</span><span class="o">.</span><span class="n">_data</span> <span class="o">+=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.expand_dims.html#numpy.expand_dims" title="numpy.expand_dims" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span></a><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">fake_description</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">target</span><span class="p">,</span> <span class="n">target_subset</span><span class="p">],</span>
+                                     <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;target&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">])</span>
+        <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <a href="../../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">fake_description</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">))</span>
+
+    <span class="k">return</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="generating-fake-regression-dataset">
+<h2>Generating fake regression dataset<a class="headerlink" href="#generating-fake-regression-dataset" title="Link to this heading">#</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_rec</span></a> <span class="o">=</span> <span class="mi">20</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">=</span> <span class="mi">21</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">=</span> <span class="mi">30</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a> <span class="o">=</span> <span class="mi">1</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <span class="n">fake_regression_dataset</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_rec</span></a><span class="p">,</span>
+                                  <span class="n">n_fake_chs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                                  <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+Creating RawArray with float64 data, n_channels=21, n_times=3000
+    Range : 0 ... 2999 =      0.000 ...    29.990 secs
+Ready.
+</pre></div>
+</div>
+</section>
+<section id="defining-a-cnn-regression-model">
+<h2>Defining a CNN regression model<a class="headerlink" href="#defining-a-cnn-regression-model" title="Link to this heading">#</a></h2>
+<p>Choosing and defining a CNN classifier, <cite>ShallowFBCSPNet</cite> or <cite>Deep4Net</cite>, introduced in <a class="footnote-reference brackets" href="#id4" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>.
+To convert a classifier to a regressor, <cite>softmax</cite> function is removed from its output layer.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="c1"># Choosing a CNN model</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">=</span> <span class="s2">&quot;shallow&quot;</span>  <span class="c1"># &#39;shallow&#39; or &#39;deep&#39;</span>
+
+<span class="c1"># Defining a CNN model</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;shallow&quot;</span><span class="p">,</span> <span class="s2">&quot;Shallow&quot;</span><span class="p">,</span> <span class="s2">&quot;ShallowConvNet&quot;</span><span class="p">]:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span><span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span>
+                            <span class="n">n_classes</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                            <span class="n">input_window_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                            <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
+                            <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
+                            <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,)</span>
+<span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;deep&quot;</span><span class="p">,</span> <span class="s2">&quot;Deep&quot;</span><span class="p">,</span> <span class="s2">&quot;DeepConvNet&quot;</span><span class="p">]:</span>
+    <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a><span class="p">(</span><span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_targets</span></a><span class="p">,</span>
+                     <span class="n">input_window_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a><span class="p">,</span>
+                     <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
+                     <span class="n">stride_before_pool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                     <span class="n">n_filters_2</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span>
+                     <span class="n">n_filters_3</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">4</span><span class="p">,</span>
+                     <span class="n">n_filters_4</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a> <span class="o">*</span> <span class="mi">8</span><span class="p">,</span>
+                     <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                     <span class="n">add_log_softmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="p">)</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a><span class="si">}</span><span class="s1"> unknown&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;in_chans&#39; is depreciated. Use &#39;n_chans&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;n_classes&#39; is depreciated. Use &#39;n_outputs&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+</pre></div>
+</div>
+</section>
+<section id="choosing-between-gpu-and-cpu-processors">
+<h2>Choosing between GPU and CPU processors<a class="headerlink" href="#choosing-between-gpu-and-cpu-processors" title="Link to this heading">#</a></h2>
+<p>By default, model’s training and evaluation take place at GPU if it exists, otherwise on CPU.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Setting a random seed</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="data-windowing">
+<h2>Data windowing<a class="headerlink" href="#data-windowing" title="Link to this heading">#</a></h2>
+<p>Windowing data with a sliding window into the epochs of the size <cite>window_size_samples</cite>.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models.util</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span><span class="p">,</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_sfreq</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fake_duration</span></a> <span class="o">//</span> <span class="mi">3</span>
+<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_fake_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">)[</span>
+    <span class="mi">2</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+                                              <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                                              <span class="n">stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                                              <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
+                                              <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
+                                              <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                              <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Splitting windowed data into train, valid and test subsets.</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function to_dense_prediction_model is deprecated; will be removed in version 1.0. Use EEGModuleMixin.to_dense_prediction_model method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+/home/runner/.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py:86: FutureWarning: Function get_output_shape is deprecated; will be removed in version 1.0. Use EEGModuleMixin.get_output_shape method directly on the model object.
+  warnings.warn(msg, category=FutureWarning)
+/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:610: UserWarning: Meaning of `trial_stop_offset_samples`=0 has changed, use `None` to indicate end of trial/recording. Using `None`.
+  warnings.warn(
+</pre></div>
+</div>
+</section>
+<section id="model-training">
+<h2>Model training<a class="headerlink" href="#model-training" title="Link to this heading">#</a></h2>
+<p>Model is trained by minimizing MSE loss between ground truth and estimated value averaged over
+a period of time using AdamW optimizer <a class="footnote-reference brackets" href="#id5" id="id2" role="doc-noteref"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></a>, <a class="footnote-reference brackets" href="#id6" id="id3" role="doc-noteref"><span class="fn-bracket">[</span>3<span class="fn-bracket">]</span></a>. Learning rate is managed by CosineAnnealingLR
+learning rate scheduler.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.training.losses</span> <span class="kn">import</span> <a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
+<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
+<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <span class="n">predefined_split</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a> <span class="o">=</span> <span class="mf">0.001</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a> <span class="o">=</span> <span class="mf">0.0</span>
+<span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <span class="n">criterion</span><span class="o">=</span><a href="../../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
+                         <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span></a><span class="p">,</span>
+                         <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
+                         <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a><span class="p">,</span>
+                         <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a><span class="p">,</span>
+                         <span class="n">train_split</span><span class="o">=</span><span class="n">predefined_split</span><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
+                         <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                         <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
+                         <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;neg_root_mean_squared_error&quot;</span><span class="p">,</span>
+                                    <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span>
+                                     <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span>
+                                                 <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">))],</span>
+                         <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span> <span class="p">)</span>
+<a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_neg_root_mean_squared_error    valid_loss    valid_neg_root_mean_squared_error      lr     dur
+-------  ------------  -----------------------------------  ------------  -----------------------------------  ------  ------
+      1        7.2505                              -1.1215        0.9371                              -0.9685  0.0010  0.9485
+      2        1.9601                              -1.1443        0.2920                              -0.5329  0.0005  0.8815
+      3        2.3129                              -1.0412        0.2603                              -0.4996  0.0000  0.8871
+</pre></div>
+</div>
+</section>
+<section id="model-evaluation">
+<h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Link to this heading">#</a></h2>
+<p>Plotting training and validation losses and negative root mean square error</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+
+<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Train and valid losses&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;train_loss&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;valid_loss&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Cropped MSE loss&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Train&quot;</span><span class="p">,</span> <span class="s2">&quot;Valid&quot;</span><span class="p">])</span>
+
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Train and valid errors&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;train_neg_root_mean_squared_error&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">regressor</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s2">&quot;valid_neg_root_mean_squared_error&quot;</span><span class="p">])</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Epochs&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Negative RMSE&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Train&quot;</span><span class="p">,</span> <span class="s2">&quot;Valid&quot;</span><span class="p">])</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_regression_001.png" srcset="../../_images/sphx_glr_plot_regression_001.png" alt="Train and valid losses, Train and valid errors" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend object at 0x7f45423fa290&gt;
+</pre></div>
+</div>
+</section>
+<section id="model-testing">
+<h2>Model testing<a class="headerlink" href="#model-testing" title="Link to this heading">#</a></h2>
+<p>Plotting a scatter plot of estimated versus target values and corresponding trend line.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="matplotlib.axes.Axes" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">axes</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ravel.html#numpy.ravel" title="numpy.ravel" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ravel</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/net.html#skorch.net.NeuralNet.predict" title="skorch.net.NeuralNet.predict" class="sphx-glr-backref-module-skorch-net sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">))</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()[</span><span class="s2">&quot;target&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Estimated targets.&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text" title="matplotlib.text.Text" class="sphx-glr-backref-module-matplotlib-text sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Ground truth targets.&quot;</span><span class="p">)</span>
+
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">z</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html#numpy.polyfit" title="numpy.polyfit" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">polyfit</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_estim</span></a><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">p</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class"><span class="n">np</span><span class="o">.</span><span class="n">poly1d</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">z</span></a><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot" title="matplotlib.pyplot.plot" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">plot</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.poly1d.html#numpy.poly1d" title="numpy.poly1d" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">p</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_gt</span></a><span class="p">),</span> <span class="s2">&quot;r--&quot;</span><span class="p">)</span>
+<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<img src="../../_images/sphx_glr_plot_regression_002.png" srcset="../../_images/sphx_glr_plot_regression_002.png" alt="plot regression" class = "sphx-glr-single-img"/></section>
+<section id="references">
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
+<aside class="footnote-list brackets">
+<aside class="footnote brackets" id="id4" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M.,
+Eggensperger, K., Tangermann, M., … &amp; Ball, T. (2017).
+Deep learning with convolutional neural networks for EEG decoding and visualization.
+Human brain mapping, 38(11), 5391-5420.</p>
+</aside>
+<aside class="footnote brackets" id="id5" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Kingma, Diederik P., and Jimmy Ba.
+“Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).</p>
+</aside>
+<aside class="footnote brackets" id="id6" role="note">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Reddi, Sashank J., Satyen Kale, and Sanjiv Kumar.
+“On the convergence of adam and beyond.” arXiv preprint arXiv:1904.09237 (2019).</p>
+</aside>
+</aside>
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+<p><a class="reference download internal" download="" href="../../_downloads/75a73c7d94f3a671fd3dec28f4031ead/plot_regression.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_regression.ipynb</span></code></a></p>
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+<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">Basic model building and training</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="plot_basic_training_epochs.html">Simple training on MNE epochs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Basic Brain Decoding on EEG Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model?</a></li>
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+<li class="toctree-l2 current active"><a class="current reference internal" href="#">Training a Braindecode model in PyTorch</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets_io/index.html">Loading and organizing data</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../datasets_io/plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../advanced_training/index.html">Advanced neural network training strategies</a><input class="toctree-checkbox" id="toctree-checkbox-3" name="toctree-checkbox-3" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-3"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../advanced_training/plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../applied_examples/index.html">Applied examples on real-world datasets</a><input class="toctree-checkbox" id="toctree-checkbox-4" name="toctree-checkbox-4" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-4"><i class="fa-solid fa-chevron-down"></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../applied_examples/plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
+</ul>
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+      
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+  
+  
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+              
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+  
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="training-a-braindecode-model-in-pytorch">
+<span id="sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"></span><h1>Training a Braindecode model in PyTorch<a class="headerlink" href="#training-a-braindecode-model-in-pytorch" title="Link to this heading">#</a></h1>
+<p>This tutorial shows you how to train a Braindecode model with PyTorch. The data
+preparation and model instantiation steps are identical to that of the tutorial
+<a class="reference external" href="./plot_how_train_test_and_tune.html">How to train, test and tune your model</a></p>
+<p>We will use the BCIC IV 2a dataset as a showcase example.</p>
+<p>The methods shown can be applied to any standard supervised trial-based decoding setting.
+This tutorial will include additional parts of code like loading and preprocessing,
+defining a model, and other details which are not exclusive to this page (compare
+<a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Cropped Decoding Tutorial</a>). Therefore we
+will not further elaborate on these parts and you can feel free to skip them.</p>
+<p>The goal of this tutorial is to present braindecode in the PyTorch perceptive.</p>
+<nav class="contents local" id="this-example-covers">
+<p class="topic-title">This example covers:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#why-should-i-care-about-model-evaluation" id="id1">Why should I care about model evaluation?</a></p></li>
+<li><p><a class="reference internal" href="#loading-preprocessing-defining-a-model-etc" id="id2">Loading, preprocessing, defining a model, etc.</a></p>
+<ul>
+<li><p><a class="reference internal" href="#loading-the-dataset-structure" id="id3">Loading the Dataset Structure</a></p></li>
+<li><p><a class="reference internal" href="#preprocessing-the-offline-transformation-of-the-raw-dataset" id="id4">Preprocessing, the offline transformation of the raw dataset</a></p></li>
+<li><p><a class="reference internal" href="#cut-compute-windows" id="id5">Cut Compute Windows</a></p></li>
+<li><p><a class="reference internal" href="#create-pytorch-model" id="id6">Create Pytorch model</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#how-to-train-and-evaluate-your-model" id="id7">How to train and evaluate your model</a></p>
+<ul>
+<li><p><a class="reference internal" href="#split-dataset-into-train-and-test" id="id8">Split dataset into train and test</a></p></li>
+<li><p><a class="reference internal" href="#option-1-pure-pytorch-training-loop" id="id9">Option 1: Pure PyTorch training loop</a></p></li>
+<li><p><a class="reference internal" href="#option-2-train-it-with-pytorch-lightning" id="id10">Option 2: Train it with PyTorch Lightning</a></p></li>
+</ul>
+</li>
+</ul>
+</nav>
+<section id="why-should-i-care-about-model-evaluation">
+<h2><a class="toc-backref" href="#id1" role="doc-backlink">Why should I care about model evaluation?</a><a class="headerlink" href="#why-should-i-care-about-model-evaluation" title="Link to this heading">#</a></h2>
+<p>Short answer: To produce reliable results!</p>
+<p>In machine learning, we usually follow the scheme of splitting the
+data into two parts, training and testing sets. It sounds like a
+simple division, right? But the story does not end here.</p>
+<p>While developing a ML model you usually have to adjust and tune
+hyperparameters of your model or pipeline (e.g., number of layers,
+learning rate, number of epochs). Deep learning models usually have
+many free parameters; they could be considered complex models with
+many degrees of freedom. If you kept using the test dataset to
+evaluate your adjustmentyou would run into data leakage.</p>
+<p>This means that if you use the test set to adjust the hyperparameters
+of your model, the model implicitly learns or memorizes the test set.
+Therefore, the trained model is no longer independent of the test set
+(even though it was never used for training explicitly!).
+If you perform any hyperparameter tuning, you need a third split,
+the so-called validation set.</p>
+<p>This tutorial shows the three basic schemes for training and evaluating
+the model as well as two methods to tune your hyperparameters.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>You might recognize that the accuracy gets better throughout
+the experiments of this tutorial. The reason behind that is that
+we always use the same model with the same parameters in every
+segment to keep the tutorial short and readable. If you do your
+own experiments you always have to reinitialize the model before
+training.</p>
+</div>
+</section>
+<section id="loading-preprocessing-defining-a-model-etc">
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">Loading, preprocessing, defining a model, etc.</a><a class="headerlink" href="#loading-preprocessing-defining-a-model-etc" title="Link to this heading">#</a></h2>
+<section id="loading-the-dataset-structure">
+<h3><a class="toc-backref" href="#id3" role="doc-backlink">Loading the Dataset Structure</a><a class="headerlink" href="#loading-the-dataset-structure" title="Link to this heading">#</a></h3>
+<p>Here, we have a data structure with equal behavior to the Pytorch Dataset.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
+<a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014_001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
+</pre></div>
+</div>
+</section>
+<section id="preprocessing-the-offline-transformation-of-the-raw-dataset">
+<h3><a class="toc-backref" href="#id4" role="doc-backlink">Preprocessing, the offline transformation of the raw dataset</a><a class="headerlink" href="#preprocessing-the-offline-transformation-of-the-raw-dataset" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
+    <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
+<span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.0</span>  <span class="c1"># low cut frequency for filtering</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.0</span>  <span class="c1"># high cut frequency for filtering</span>
+<span class="c1"># Parameters for exponential moving standardization</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
+
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <span class="p">[</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;pick_types&quot;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
+        <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
+    <span class="p">),</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;filter&quot;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
+    <a href="../../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
+        <a href="../../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span>
+        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span>
+    <span class="p">),</span>
+<span class="p">]</span>
+
+<span class="c1"># Transform the data</span>
+<a href="../../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
+  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
+
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f4541374df0&gt;
+</pre></div>
+</div>
+</section>
+<section id="cut-compute-windows">
+<h3><a class="toc-backref" href="#id5" role="doc-backlink">Cut Compute Windows</a><a class="headerlink" href="#cut-compute-windows" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
+<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
+<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
+<span class="c1"># Calculate the trial start offset in samples.</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
+
+<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
+<span class="c1"># trials should be used.</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
+    <a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
+    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
+</pre></div>
+</div>
+</section>
+<section id="create-pytorch-model">
+<h3><a class="toc-backref" href="#id6" role="doc-backlink">Create Pytorch model</a><a class="headerlink" href="#create-pytorch-model" title="Link to this heading">#</a></h3>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
+<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
+<a href="../../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
+
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
+<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">classes</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">))</span>
+<span class="c1"># Extract number of chans and time steps from dataset</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c1"># The ShallowFBCSPNet is a `nn.Sequential` model</span>
+
+<a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
+    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
+    <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="c1"># Display torchinfo table describing the model</span>
+<span class="nb">print</span><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+
+<span class="c1"># Send model to GPU</span>
+<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/models/base.py:23: UserWarning: ShallowFBCSPNet: &#39;input_window_samples&#39; is depreciated. Use &#39;n_times&#39; instead.
+  warnings.warn(
+/home/runner/work/braindecode/braindecode/braindecode/models/base.py:180: UserWarning: LogSoftmax final layer will be removed! Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+  warnings.warn(&quot;LogSoftmax final layer will be removed! &quot; +
+============================================================================================================================================
+Layer (type (var_name):depth-idx)        Input Shape               Output Shape              Param #                   Kernel Shape
+============================================================================================================================================
+ShallowFBCSPNet (ShallowFBCSPNet)        [1, 22, 1125]             [1, 4]                    --                        --
+├─Ensure4d (ensuredims): 1-1             [1, 22, 1125]             [1, 22, 1125, 1]          --                        --
+├─Rearrange (dimshuffle): 1-2            [1, 22, 1125, 1]          [1, 1, 1125, 22]          --                        --
+├─CombinedConv (conv_time_spat): 1-3     [1, 1, 1125, 22]          [1, 40, 1101, 1]          36,240                    --
+├─BatchNorm2d (bnorm): 1-4               [1, 40, 1101, 1]          [1, 40, 1101, 1]          80                        --
+├─Expression (conv_nonlin_exp): 1-5      [1, 40, 1101, 1]          [1, 40, 1101, 1]          --                        --
+├─AvgPool2d (pool): 1-6                  [1, 40, 1101, 1]          [1, 40, 69, 1]            --                        [75, 1]
+├─Expression (pool_nonlin_exp): 1-7      [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Dropout (drop): 1-8                    [1, 40, 69, 1]            [1, 40, 69, 1]            --                        --
+├─Sequential (final_layer): 1-9          [1, 40, 69, 1]            [1, 4]                    --                        --
+│    └─Conv2d (conv_classifier): 2-1     [1, 40, 69, 1]            [1, 4, 1, 1]              11,044                    [69, 1]
+│    └─LogSoftmax (logsoftmax): 2-2      [1, 4, 1, 1]              [1, 4, 1, 1]              --                        --
+│    └─Expression (squeeze): 2-3         [1, 4, 1, 1]              [1, 4]                    --                        --
+============================================================================================================================================
+Total params: 47,364
+Trainable params: 47,364
+Non-trainable params: 0
+Total mult-adds (M): 0.01
+============================================================================================================================================
+Input size (MB): 0.10
+Forward/backward pass size (MB): 0.35
+Params size (MB): 0.04
+Estimated Total Size (MB): 0.50
+============================================================================================================================================
+</pre></div>
+</div>
+</section>
+</section>
+<section id="how-to-train-and-evaluate-your-model">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">How to train and evaluate your model</a><a class="headerlink" href="#how-to-train-and-evaluate-your-model" title="Link to this heading">#</a></h2>
+<section id="split-dataset-into-train-and-test">
+<h3><a class="toc-backref" href="#id8" role="doc-backlink">Split dataset into train and test</a><a class="headerlink" href="#split-dataset-into-train-and-test" title="Link to this heading">#</a></h3>
+<p>We can easily split the dataset using additional info stored in the
+description attribute, in this case the <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We
+select <code class="docutils literal notranslate"><span class="pre">Train</span></code> for training and <code class="docutils literal notranslate"><span class="pre">test</span></code> for testing.
+For other datasets, you might have to choose another column.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>No matter which of the three schemes you use, this initial
+two-fold split into train_set and test_set always remains the same.
+Remember that you are not allowed to use the test_set during any
+stage of training or tuning.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;0train&#39;</span><span class="p">]</span>  <span class="c1"># Session train</span>
+<a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;1test&#39;</span><span class="p">]</span>  <span class="c1"># Session evaluation</span>
+</pre></div>
+</div>
+</section>
+<section id="option-1-pure-pytorch-training-loop">
+<h3><a class="toc-backref" href="#id9" role="doc-backlink">Option 1: Pure PyTorch training loop</a><a class="headerlink" href="#option-1-pure-pytorch-training-loop" title="Link to this heading">#</a></h3>
+<img alt="Pytorch logo" src="https://upload.wikimedia.org/wikipedia/commons/9/96/Pytorch_logo.png" />
+<p><cite>model</cite> is an instance of <cite>torch.nn.Module</cite>, and can as such be trained
+using PyTorch optimization capabilities.
+The following training scheme is simple as the dataset is only
+split into two distinct sets (<code class="docutils literal notranslate"><span class="pre">train_set</span></code> and <code class="docutils literal notranslate"><span class="pre">test_set</span></code>).
+This scheme uses no separate validation split and should only be
+used for the final evaluation of the (previously!) found
+hyperparameters configuration.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>If you make any use of the <code class="docutils literal notranslate"><span class="pre">test_set</span></code> during training
+(e.g. by using EarlyStopping) there will be data leakage
+which will make the reported generalization capability/decoding
+performance of your model less credible.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>The parameter values showcased here for optimizing the network are
+chosen to make this tutorial fast to run and build. Real-world values
+would be higher, especially when it comes to n_epochs.</p>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a>
+<span class="kn">from</span> <span class="nn">torch.optim.lr_scheduler</span> <span class="kn">import</span> <span class="n">LRScheduler</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
+
+<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
+<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">2</span>
+</pre></div>
+</div>
+<p>The following method runs one training epoch over the dataloader for the
+given model. It needs a loss function, optimization algorithm, and
+learning rate updating callback.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+<span class="c1"># Define a method for training one epoch</span>
+
+
+<span class="k">def</span> <span class="nf">train_one_epoch</span><span class="p">(</span>
+        <span class="n">dataloader</span><span class="p">:</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">:</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">:</span> <span class="n">LRScheduler</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span> <span class="n">print_batch_stats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">):</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.train" title="torch.nn.Module.train" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">train</span></a><span class="p">()</span>  <span class="c1"># Set the model to training mode</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="p">,</span> <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+
+    <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
+                        <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="n">print_batch_stats</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">:</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW.zero_grad" title="torch.optim.AdamW.zero_grad" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-method"><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span></a><span class="p">()</span>
+        <span class="n">pred</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="o">.</span><span class="n">step</span><span class="p">()</span>  <span class="c1"># update the model weights</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW.zero_grad" title="torch.optim.AdamW.zero_grad" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-method"><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span></a><span class="p">()</span>
+
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+            <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="si">}</span><span class="s2">/</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Batch </span><span class="si">{</span><span class="n">batch_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Loss: </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <span class="c1"># Update the learning rate</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+
+    <span class="n">correct</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+    <span class="k">return</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">correct</span>
+</pre></div>
+</div>
+<p>Very similarly, the evaluation function loops over the entire dataloader
+and accumulate the metrics, but doesn’t update the model weights.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
+<span class="k">def</span> <span class="nf">test_model</span><span class="p">(</span>
+    <span class="n">dataloader</span><span class="p">:</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">:</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">Module</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <span class="n">print_batch_stats</span><span class="o">=</span><span class="kc">True</span>
+<span class="p">):</span>
+    <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><a href="../../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">)</span>
+    <span class="n">n_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span>
+    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.eval" title="torch.nn.Module.eval" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">eval</span></a><span class="p">()</span>  <span class="c1"># Switch to evaluation mode</span>
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <span class="n">correct</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+
+    <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+        <span class="n">progress_bar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">progress_bar</span> <span class="o">=</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span><span class="p">)</span> <span class="ow">in</span> <span class="n">progress_bar</span><span class="p">:</span>
+        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span> <span class="n">y</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">)</span>
+        <span class="n">pred</span> <span class="o">=</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
+        <span class="n">batch_loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a> <span class="o">+=</span> <span class="n">batch_loss</span>
+        <span class="n">correct</span> <span class="o">+=</span> <span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">type</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype" title="torch.dtype" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">float</span></a><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="n">print_batch_stats</span><span class="p">:</span>
+            <span class="n">progress_bar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Batch </span><span class="si">{</span><span class="n">batch_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
+                <span class="sa">f</span><span class="s2">&quot;Loss: </span><span class="si">{</span><span class="n">batch_loss</span><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a> <span class="o">/=</span> <span class="n">n_batches</span>
+    <span class="n">correct</span> <span class="o">/=</span> <span class="n">size</span>
+
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;Test Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">correct</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%, Test Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <span class="n">correct</span>
+
+
+<span class="c1"># Define the optimization</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters" title="torch.nn.Module.parameters" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">parameters</span></a><span class="p">(),</span>
+                              <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">CosineAnnealingLR</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+                                                       <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+<span class="c1"># Define the loss function</span>
+<span class="c1"># We used the NNLoss function, which expects log probabilities as input</span>
+<span class="c1"># (which is the case for our model output)</span>
+<a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">()</span>
+
+<span class="c1"># train_set and test_set are instances of torch Datasets, and can seamlessly be</span>
+<span class="c1"># wrapped in data loaders.</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span><a href="../../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">)</span>
+
+<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="si">}</span><span class="s2">/</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="si">}</span><span class="s2">: &quot;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_accuracy</span></a> <span class="o">=</span> <span class="n">train_one_epoch</span><span class="p">(</span>
+        <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epoch</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
+    <span class="p">)</span>
+
+    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_accuracy</span></a> <span class="o">=</span> <span class="n">test_model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a><span class="p">,</span> <a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">loss_fn</span></a><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span>
+        <span class="sa">f</span><span class="s2">&quot;Train Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_accuracy</span></a><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Average Train Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="s2">, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Test Accuracy: </span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_accuracy</span></a><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2">%, &quot;</span>
+        <span class="sa">f</span><span class="s2">&quot;Average Test Loss: </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loss</span></a><span class="si">:</span><span class="s2">.6f</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 1/2:
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 1/2, Batch 1/5, Loss: 1.784639:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 1/2, Batch 1/5, Loss: 1.784639:  20%|██        | 1/5 [00:00&lt;00:01,  2.81it/s]
+Epoch 1/2, Batch 2/5, Loss: 1.497504:  20%|██        | 1/5 [00:00&lt;00:01,  2.81it/s]
+Epoch 1/2, Batch 2/5, Loss: 1.497504:  40%|████      | 2/5 [00:00&lt;00:00,  3.24it/s]
+Epoch 1/2, Batch 3/5, Loss: 1.544960:  40%|████      | 2/5 [00:00&lt;00:00,  3.24it/s]
+Epoch 1/2, Batch 3/5, Loss: 1.544960:  60%|██████    | 3/5 [00:00&lt;00:00,  3.38it/s]
+Epoch 1/2, Batch 4/5, Loss: 1.552846:  60%|██████    | 3/5 [00:01&lt;00:00,  3.38it/s]
+Epoch 1/2, Batch 4/5, Loss: 1.552846:  80%|████████  | 4/5 [00:01&lt;00:00,  3.38it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800:  80%|████████  | 4/5 [00:01&lt;00:00,  3.38it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800: 100%|██████████| 5/5 [00:01&lt;00:00,  4.14it/s]
+Epoch 1/2, Batch 5/5, Loss: 1.648800: 100%|██████████| 5/5 [00:01&lt;00:00,  3.69it/s]
+
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 1/5, Loss: 4.067336:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 4.475679:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 4.475679:  40%|████      | 2/5 [00:00&lt;00:00, 14.75it/s]
+Batch 3/5, Loss: 4.224905:  40%|████      | 2/5 [00:00&lt;00:00, 14.75it/s]
+Batch 4/5, Loss: 4.333737:  40%|████      | 2/5 [00:00&lt;00:00, 14.75it/s]
+Batch 4/5, Loss: 4.333737:  80%|████████  | 4/5 [00:00&lt;00:00, 14.74it/s]
+Batch 5/5, Loss: 4.520595:  80%|████████  | 4/5 [00:00&lt;00:00, 14.74it/s]
+Batch 5/5, Loss: 4.520595: 100%|██████████| 5/5 [00:00&lt;00:00, 16.31it/s]
+Test Accuracy: 25.3%, Test Loss: 4.324450
+
+Train Accuracy: 30.90%, Average Train Loss: 1.605750, Test Accuracy: 25.3%, Average Test Loss: 4.324450
+
+Epoch 2/2:
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 2/2, Batch 1/5, Loss: 1.460681:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 2/2, Batch 1/5, Loss: 1.460681:  20%|██        | 1/5 [00:00&lt;00:01,  3.68it/s]
+Epoch 2/2, Batch 2/5, Loss: 1.178580:  20%|██        | 1/5 [00:00&lt;00:01,  3.68it/s]
+Epoch 2/2, Batch 2/5, Loss: 1.178580:  40%|████      | 2/5 [00:00&lt;00:00,  3.50it/s]
+Epoch 2/2, Batch 3/5, Loss: 1.232657:  40%|████      | 2/5 [00:00&lt;00:00,  3.50it/s]
+Epoch 2/2, Batch 3/5, Loss: 1.232657:  60%|██████    | 3/5 [00:00&lt;00:00,  3.22it/s]
+Epoch 2/2, Batch 4/5, Loss: 1.294503:  60%|██████    | 3/5 [00:01&lt;00:00,  3.22it/s]
+Epoch 2/2, Batch 4/5, Loss: 1.294503:  80%|████████  | 4/5 [00:01&lt;00:00,  3.38it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602:  80%|████████  | 4/5 [00:01&lt;00:00,  3.38it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602: 100%|██████████| 5/5 [00:01&lt;00:00,  4.14it/s]
+Epoch 2/2, Batch 5/5, Loss: 1.144602: 100%|██████████| 5/5 [00:01&lt;00:00,  3.77it/s]
+
+  0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 1/5, Loss: 3.412822:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 3.751318:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Batch 2/5, Loss: 3.751318:  40%|████      | 2/5 [00:00&lt;00:00, 14.65it/s]
+Batch 3/5, Loss: 3.565414:  40%|████      | 2/5 [00:00&lt;00:00, 14.65it/s]
+Batch 4/5, Loss: 3.667956:  40%|████      | 2/5 [00:00&lt;00:00, 14.65it/s]
+Batch 4/5, Loss: 3.667956:  80%|████████  | 4/5 [00:00&lt;00:00, 13.85it/s]
+Batch 5/5, Loss: 3.836547:  80%|████████  | 4/5 [00:00&lt;00:00, 13.85it/s]
+Batch 5/5, Loss: 3.836547: 100%|██████████| 5/5 [00:00&lt;00:00, 15.58it/s]
+Test Accuracy: 24.7%, Test Loss: 3.646811
+
+Train Accuracy: 44.10%, Average Train Loss: 1.262205, Test Accuracy: 24.7%, Average Test Loss: 3.646811
+</pre></div>
+</div>
+</section>
+<section id="option-2-train-it-with-pytorch-lightning">
+<h3><a class="toc-backref" href="#id10" role="doc-backlink">Option 2: Train it with PyTorch Lightning</a><a class="headerlink" href="#option-2-train-it-with-pytorch-lightning" title="Link to this heading">#</a></h3>
+<img alt="Pytorch Lightning logo" src="https://upload.wikimedia.org/wikipedia/commons/e/e6/Lightning_Logo_v2.png" />
+<p>Alternatively, lightning provides a nice interface around torch modules
+which integrates the previous logic.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">lightning</span> <span class="k">as</span> <span class="nn">L</span>
+<span class="kn">from</span> <span class="nn">torchmetrics.functional</span> <span class="kn">import</span> <span class="n">accuracy</span>
+
+
+<span class="k">class</span> <span class="nc">LitModule</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">L</span><span class="o">.</span><span class="n">LightningModule</span></a><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">):</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">batch</span>
+        <span class="n">y_hat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="s2">&quot;train_loss&quot;</span><span class="p">,</span> <span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span>
+
+    <span class="k">def</span> <span class="nf">test_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="p">):</span>
+        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">batch</span>
+        <span class="n">y_hat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">acc</span> <span class="o">=</span> <span class="n">accuracy</span><span class="p">(</span><span class="n">y_hat</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
+        <span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;test_acc&quot;</span><span class="p">:</span> <span class="n">acc</span><span class="p">,</span> <span class="s2">&quot;test_loss&quot;</span><span class="p">:</span> <span class="n">loss</span><span class="p">}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log_dict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">metrics</span>
+
+    <span class="k">def</span> <span class="nf">configure_optimizers</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.parameters" title="torch.nn.Module.parameters" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">parameters</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
+                                      <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">)</span>
+        <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">CosineAnnealingLR</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">,</span>
+                                                               <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer</span></a><span class="p">],</span> <span class="p">[</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html#torch.optim.lr_scheduler.CosineAnnealingLR" title="torch.optim.lr_scheduler.CosineAnnealingLR" class="sphx-glr-backref-module-torch-optim-lr_scheduler sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">scheduler</span></a><span class="p">]</span>
+
+
+<span class="c1"># Creating the trainer with max_epochs=2 for demonstration purposes</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">L</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span><span class="n">max_epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
+<span class="c1"># Create and train the LightningModule</span>
+<span class="n">lit_model</span> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">LitModule</span></a><span class="p">(</span><a href="../../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">lit_model</span><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_loader</span></a><span class="p">)</span>
+
+<span class="c1"># After training, you can test the model using the test DataLoader</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">dataloaders</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_loader</span></a><span class="p">)</span>
+</pre></div>
+</div>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>GPU available: False, used: False
+TPU available: False, using: 0 TPU cores
+IPU available: False, using: 0 IPUs
+HPU available: False, using: 0 HPUs
+/home/runner/.local/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
+Missing logger folder: /home/runner/work/braindecode/braindecode/examples/model_building/lightning_logs
+
+  | Name   | Type            | Params
+-------------------------------------------
+0 | module | ShallowFBCSPNet | 47.4 K
+1 | loss   | NLLLoss         | 0
+-------------------------------------------
+47.4 K    Trainable params
+0         Non-trainable params
+47.4 K    Total params
+0.189     Total estimated model params size (MB)
+/home/runner/.local/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The &#39;train_dataloader&#39; does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
+/home/runner/.local/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
+
+Training: |          | 0/? [00:00&lt;?, ?it/s]
+Training:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 0:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Epoch 0:  20%|██        | 1/5 [00:00&lt;00:01,  2.72it/s]
+Epoch 0:  20%|██        | 1/5 [00:00&lt;00:01,  2.72it/s, v_num=0]
+Epoch 0:  40%|████      | 2/5 [00:00&lt;00:00,  3.10it/s, v_num=0]
+Epoch 0:  40%|████      | 2/5 [00:00&lt;00:00,  3.10it/s, v_num=0]
+Epoch 0:  60%|██████    | 3/5 [00:00&lt;00:00,  3.26it/s, v_num=0]
+Epoch 0:  60%|██████    | 3/5 [00:00&lt;00:00,  3.26it/s, v_num=0]
+Epoch 0:  80%|████████  | 4/5 [00:01&lt;00:00,  3.36it/s, v_num=0]
+Epoch 0:  80%|████████  | 4/5 [00:01&lt;00:00,  3.36it/s, v_num=0]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  3.74it/s, v_num=0]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  3.74it/s, v_num=0]
+Epoch 0: 100%|██████████| 5/5 [00:01&lt;00:00,  3.74it/s, v_num=0]
+Epoch 0:   0%|          | 0/5 [00:00&lt;?, ?it/s, v_num=0]
+Epoch 1:   0%|          | 0/5 [00:00&lt;?, ?it/s, v_num=0]
+Epoch 1:  20%|██        | 1/5 [00:00&lt;00:01,  3.48it/s, v_num=0]
+Epoch 1:  20%|██        | 1/5 [00:00&lt;00:01,  3.48it/s, v_num=0]
+Epoch 1:  40%|████      | 2/5 [00:00&lt;00:00,  3.52it/s, v_num=0]
+Epoch 1:  40%|████      | 2/5 [00:00&lt;00:00,  3.52it/s, v_num=0]
+Epoch 1:  60%|██████    | 3/5 [00:00&lt;00:00,  3.57it/s, v_num=0]
+Epoch 1:  60%|██████    | 3/5 [00:00&lt;00:00,  3.57it/s, v_num=0]
+Epoch 1:  80%|████████  | 4/5 [00:01&lt;00:00,  3.56it/s, v_num=0]
+Epoch 1:  80%|████████  | 4/5 [00:01&lt;00:00,  3.55it/s, v_num=0]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  3.93it/s, v_num=0]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  3.93it/s, v_num=0]
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  3.93it/s, v_num=0]`Trainer.fit` stopped: `max_epochs=2` reached.
+
+Epoch 1: 100%|██████████| 5/5 [00:01&lt;00:00,  3.92it/s, v_num=0]
+/home/runner/.local/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/checkpoint_connector.py:145: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `.test(ckpt_path=&#39;best&#39;)` to use the best model or `.test(ckpt_path=&#39;last&#39;)` to use the last model. If you pass a value, this warning will be silenced.
+Restoring states from the checkpoint path at /home/runner/work/braindecode/braindecode/examples/model_building/lightning_logs/version_0/checkpoints/epoch=1-step=10.ckpt
+Loaded model weights from the checkpoint at /home/runner/work/braindecode/braindecode/examples/model_building/lightning_logs/version_0/checkpoints/epoch=1-step=10.ckpt
+/home/runner/.local/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The &#39;test_dataloader&#39; does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.
+
+Testing: |          | 0/? [00:00&lt;?, ?it/s]
+Testing:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Testing DataLoader 0:   0%|          | 0/5 [00:00&lt;?, ?it/s]
+Testing DataLoader 0:  20%|██        | 1/5 [00:00&lt;00:00,  9.95it/s]
+Testing DataLoader 0:  40%|████      | 2/5 [00:00&lt;00:00, 11.29it/s]
+Testing DataLoader 0:  60%|██████    | 3/5 [00:00&lt;00:00, 12.20it/s]
+Testing DataLoader 0:  80%|████████  | 4/5 [00:00&lt;00:00, 12.71it/s]
+Testing DataLoader 0: 100%|██████████| 5/5 [00:00&lt;00:00, 14.18it/s]
+Testing DataLoader 0: 100%|██████████| 5/5 [00:00&lt;00:00, 13.93it/s]
+────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+       Test metric             DataLoader 0
+────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+        test_acc                  0.34375
+        test_loss           1.5098872184753418
+────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
+
+[{&#39;test_acc&#39;: 0.34375, &#39;test_loss&#39;: 1.5098872184753418}]
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 11.348 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  289 MB</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py">
+<div class="sphx-glr-download sphx-glr-download-python docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/66bb851a261b0b302f4dcb9e2f361aa6/plot_train_in_pure_pytorch_and_pytorch_lightning.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.py</span></code></a></p>
+</div>
+<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
+<p><a class="reference download internal" download="" href="../../_downloads/a86d0c5f3a882a069df1683a708d3e25/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb</span></code></a></p>
+</div>
+</div>
+<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
+</section>
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#why-should-i-care-about-model-evaluation">Why should I care about model evaluation?</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-preprocessing-defining-a-model-etc">Loading, preprocessing, defining a model, etc.</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#loading-the-dataset-structure">Loading the Dataset Structure</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#preprocessing-the-offline-transformation-of-the-raw-dataset">Preprocessing, the offline transformation of the raw dataset</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#cut-compute-windows">Cut Compute Windows</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#create-pytorch-model">Create Pytorch model</a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-train-and-evaluate-your-model">How to train and evaluate your model</a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#split-dataset-into-train-and-test">Split dataset into train and test</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-1-pure-pytorch-training-loop">Option 1: Pure PyTorch training loop</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#option-2-train-it-with-pytorch-lightning">Option 2: Train it with PyTorch Lightning</a></li>
+</ul>
+</li>
+</ul>
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diff --git a/stable/auto_examples/model_building/sg_execution_times.html b/stable/auto_examples/model_building/sg_execution_times.html
new file mode 100644
index 00000000..ead7ba9a
--- /dev/null
+++ b/stable/auto_examples/model_building/sg_execution_times.html
@@ -0,0 +1,540 @@
+
+<!DOCTYPE html>
+
+
+<html lang="en" data-theme="light">
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+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
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+    <title>Computation times &#8212; Braindecode 0.8 documentation</title>
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+    <li class="breadcrumb-item active" aria-current="page">Computation times</li>
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+      
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+  
+  
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+</div>
+              
+              
+<div>
+  
+  <section id="computation-times">
+<span id="sphx-glr-auto-examples-model-building-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Link to this heading">#</a></h1>
+<p><strong>02:08.297</strong> total execution time for <strong>auto_examples_model_building</strong> files:</p>
+<table class="table">
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_cropped.py</span></code>)</p></td>
+<td><p>00:36.474</p></td>
+<td><p>743.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_how_train_test_and_tune.py</span></code>)</p></td>
+<td><p>00:32.009</p></td>
+<td><p>280.5 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_hyperparameter_tuning_with_scikit-learn.py</span></code>)</p></td>
+<td><p>00:28.100</p></td>
+<td><p>163.6 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code>)</p></td>
+<td><p>00:12.159</p></td>
+<td><p>502.4 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_train_in_pure_pytorch_and_pytorch_lightning.py</span></code>)</p></td>
+<td><p>00:11.348</p></td>
+<td><p>289.0 MB</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_regression.py</span></code>)</p></td>
+<td><p>00:06.298</p></td>
+<td><p>10.8 MB</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic_training_epochs.py</span></code>)</p></td>
+<td><p>00:01.908</p></td>
+<td><p>41.4 MB</p></td>
+</tr>
+</tbody>
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+</section>
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diff --git a/stable/auto_examples/plot_bcic_iv_2a_moabb_cropped.html b/stable/auto_examples/plot_bcic_iv_2a_moabb_cropped.html
deleted file mode 100644
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-    <ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html"> Basic trialwise decoding</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#"> More data-efficient "cropped decoding"</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_mne_dataset_example.html"> Your own datasets through MNE</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="cropped-decoding-on-bcic-iv-2a-dataset">
-<span id="sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"></span><h1>Cropped Decoding on BCIC IV 2a Dataset<a class="headerlink" href="#cropped-decoding-on-bcic-iv-2a-dataset" title="Permalink to this heading">#</a></h1>
-<p>Building on the <a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Trialwise decoding
-tutorial</a>, we now do more
-data-efficient cropped decoding!</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># In Braindecode, there are two supported configurations created for</span>
-<span class="c1"># training models: trialwise decoding and cropped decoding. We will</span>
-<span class="c1"># explain this visually by comparing trialwise to cropped decoding.</span>
-<span class="c1">#</span>
-<span class="c1"># .. image:: ../_static/trialwise_explanation.png</span>
-<span class="c1"># .. image:: ../_static/cropped_explanation.png</span>
-<span class="c1">#</span>
-<span class="c1"># On the left, you see trialwise decoding:</span>
-<span class="c1">#</span>
-<span class="c1"># 1. A complete trial is pushed through the network.</span>
-<span class="c1"># 2. The network produces a prediction.</span>
-<span class="c1"># 3. The prediction is compared to the target (label) for that trial to</span>
-<span class="c1">#    compute the loss.</span>
-<span class="c1">#</span>
-<span class="c1"># On the right, you see cropped decoding:</span>
-<span class="c1">#</span>
-<span class="c1"># 1. Instead of a complete trial, crops are pushed through the network.</span>
-<span class="c1"># 2. For computational efficiency, multiple neighbouring crops are pushed</span>
-<span class="c1">#    through the network simultaneously (these neighbouring crops are</span>
-<span class="c1">#    called compute windows)</span>
-<span class="c1"># 3. Therefore, the network produces multiple predictions (one per crop in</span>
-<span class="c1">#    the window)</span>
-<span class="c1"># 4. The individual crop predictions are averaged before computing the</span>
-<span class="c1">#    loss function</span>
-<span class="c1">#</span>
-<span class="c1"># .. note::</span>
-<span class="c1">#</span>
-<span class="c1">#     -  The network architecture implicitly defines the crop size (it is the</span>
-<span class="c1">#        receptive field size, i.e., the number of timesteps the network uses</span>
-<span class="c1">#        to make a single prediction)</span>
-<span class="c1">#     -  The window size is a user-defined hyperparameter, called</span>
-<span class="c1">#        ``input_window_samples`` in Braindecode. It mostly affects runtime</span>
-<span class="c1">#        (larger window sizes should be faster). As a rule of thumb, you can</span>
-<span class="c1">#        set it to two times the crop size.</span>
-<span class="c1">#     -  Crop size and window size together define how many predictions the</span>
-<span class="c1">#        network makes per window: ``#window − #crop + 1 = #predictions``</span>
-<span class="c1">#</span>
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># .. note::</span>
-<span class="c1">#</span>
-<span class="c1">#     For cropped decoding, the above training setup is mathematically</span>
-<span class="c1">#     identical to sampling crops in your dataset, pushing them through the</span>
-<span class="c1">#     network and training directly on the individual crops. At the same time,</span>
-<span class="c1">#     the above training setup is much faster as it avoids redundant</span>
-<span class="c1">#     computations by using dilated convolutions, see our paper</span>
-<span class="c1">#     `Deep learning with convolutional neural networks for EEG decoding and visualization &lt;https://arxiv.org/abs/1703.05051&gt;`_. # noqa: E501</span>
-<span class="c1">#     However, the two setups are only mathematically identical in case (1)</span>
-<span class="c1">#     your network does not use any padding or only left padding and</span>
-<span class="c1">#     (2) your loss function leads</span>
-<span class="c1">#     to the same gradients when using the averaged output. The first is true</span>
-<span class="c1">#     for our shallow and deep ConvNet models and the second is true for the</span>
-<span class="c1">#     log-softmax outputs and negative log likelihood loss that is typically</span>
-<span class="c1">#     used for classification in PyTorch.</span>
-<span class="c1">#</span>
-
-<span class="c1"># Loading and preprocessing the dataset</span>
-<span class="c1"># -------------------------------------</span>
-<span class="c1">#</span>
-<span class="c1"># Loading and preprocessing stays the same as in the `Trialwise decoding</span>
-<span class="c1"># tutorial &lt;./plot_bcic_iv_2a_moabb_trial.html&gt;`__.</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50a90f6350&gt;
-</pre></div>
-</div>
-<section id="create-model-and-compute-windowing-parameters">
-<h2>Create model and compute windowing parameters<a class="headerlink" href="#create-model-and-compute-windowing-parameters" title="Permalink to this heading">#</a></h2>
-<p>In contrast to trialwise decoding, we first have to create the model
-before we can cut the dataset into windows. This is because we need to
-know the receptive field of the network to know how large the window
-stride should be.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># We first choose the compute/input window size that will be fed to the</span>
-<span class="c1"># network during training This has to be larger than the networks</span>
-<span class="c1"># receptive field size and can otherwise be chosen for computational</span>
-<span class="c1"># efficiency (see explanations in the beginning of this tutorial). Here we</span>
-<span class="c1"># choose 1000 samples, which are 4 seconds for the 250 Hz sampling rate.</span>
-<span class="c1">#</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
-</pre></div>
-</div>
-<p>Now we create the model. To enable it to be used in cropped decoding
-efficiently, we manually set the length of the final convolution layer
-to some length that makes the receptive field of the ConvNet smaller
-than <code class="docutils literal notranslate"><span class="pre">input_window_samples</span></code> (see <code class="docutils literal notranslate"><span class="pre">final_conv_length=30</span></code> in the model
-definition).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-<span class="c1"># Extract number of chans from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<p>And now we transform model with strides to a model that outputs dense
-prediction, so we can use it to obtain predictions for all
-crops.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span><span class="p">,</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a>
-
-<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>To know the models’ receptive field, we calculate the shape of model
-output for a dummy input.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="cut-the-data-into-windows">
-<h2>Cut the data into windows<a class="headerlink" href="#cut-the-data-into-windows" title="Permalink to this heading">#</a></h2>
-<p>In contrast to trialwise decoding, we have to supply an explicit
-window size and window stride to the <code class="docutils literal notranslate"><span class="pre">create_windows_from_events</span></code>
-function.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
-<span class="c1"># trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-the-dataset">
-<h2>Split the dataset<a class="headerlink" href="#split-the-dataset" title="Permalink to this heading">#</a></h2>
-<p>This code is the same as in trialwise decoding.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_T&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_E&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-<dl class="simple">
-<dt>Training</dt><dd><p>In difference to trialwise decoding, we now should supply
-<code class="docutils literal notranslate"><span class="pre">cropped=True</span></code> to the EEGClassifier, and <code class="docutils literal notranslate"><span class="pre">CroppedLoss</span></code> as the
-criterion, as well as <code class="docutils literal notranslate"><span class="pre">criterion__loss_function</span></code> as the loss function
-applied to the meaned predictions.</p>
-</dd>
-</dl>
-<hr class="docutils" />
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># .. note::</span>
-<span class="c1">#</span>
-<span class="c1">#     In this tutorial, we use some default parameters that we</span>
-<span class="c1">#     have found to work well for motor decoding, however we strongly</span>
-<span class="c1">#     encourage you to perform your own hyperparameter optimization using</span>
-<span class="c1">#     cross validation on your training data.</span>
-<span class="c1">#</span>
-
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.training</span> <span class="kn">import</span> <a href="../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
-
-<span class="c1"># These values we found good for shallow network:</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="c1"># For deep4 they should be:</span>
-<span class="c1"># lr = 1 * 0.01</span>
-<span class="c1"># weight_decay = 0.5 * 0.001</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
-    <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.nll_loss.html#torch.nn.functional.nll_loss" title="torch.nn.functional.nll_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">nll_loss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
-<span class="c1"># in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/skorch/callbacks/base.py:146: SkorchWarning: You are using an callback that overrides on_batch_begin or on_batch_end. As of skorch 0.10, the signature was changed from &#39;on_batch_{begin,end}(self, X, y, ...)&#39; to &#39;on_batch_{begin,end}(self, batch, ...)&#39;. To recover, change the signature accordingly and add &#39;X, y = batch&#39; on the first line of the method body. To suppress this warning, add:
-&#39;import warnings; from skorch.exceptions import SkorchWarning
-warnings.filterwarnings(&#39;ignore&#39;, category=SkorchWarning)&#39;.
-  warnings.warn(warning_msg, SkorchWarning)
-  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr      dur
--------  ----------------  ------------  ----------------  ------------  ------  -------
-      1            0.2500        1.4486            0.2500        5.4385  0.0006  17.6361
-      2            0.2569        1.2315            0.2500        3.6823  0.0005  17.2271
-      3            0.3958        1.1476            0.3611        2.2702  0.0002  17.1282
-      4            0.4340        1.1225            0.4410        1.4375  0.0000  17.1336
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=ShallowFBCSPNet(
-    (ensuredims): Ensure4d()
-    (dimshuffle): Expression(expression=transpose_time_to_spat)
-    (conv_time): Conv2d(1, 40, kernel_size=(25, 1), stride=(1, 1))
-    (conv_spat): Conv2d(40, 40, kernel_size=(1, 22), stride=(1, 1), bias=False)
-    (bnorm): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (conv_nonlin_exp): Expression(expression=square)
-    (pool): AvgPool2d(kernel_size=(75, 1), stride=(1, 1), padding=0)
-    (pool_nonlin_exp): Expression(expression=safe_log)
-    (drop): Dropout(p=0.5, inplace=False)
-    (conv_classifier): Conv2d(40, 4, kernel_size=(30, 1), stride=(1, 1), dilation=(15, 1))
-    (softmax): LogSoftmax(dim=1)
-    (squeeze): Expression(expression=squeeze_final_output)
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="plot-results">
-<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<section id="this-is-again-the-same-code-as-in-trialwise-decoding">
-<h3>This is again the same code as in trialwise decoding.<a class="headerlink" href="#this-is-again-the-same-code-as-in-trialwise-decoding" title="Permalink to this heading">#</a></h3>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Note that we drop further in the classification error and
-loss as in the trialwise decoding tutorial.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
-                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
-
-<span class="c1"># get percent of misclass for better visual comparison to loss</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
-               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<span class="n">ax2</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<span class="c1"># where some data has already been plotted to ax</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_001.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/></section>
-</section>
-<section id="plot-confusion-matrix">
-<h2>Plot Confusion Matrix<a class="headerlink" href="#plot-confusion-matrix" title="Permalink to this heading">#</a></h2>
-<p>Generate a confusion matrix as in <a class="reference external" href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730</a></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
-
-<span class="c1"># generate confusion matrices</span>
-<span class="c1"># get the targets</span>
-<span class="n">y_true</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
-
-<span class="c1"># generating confusion matrix</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
-
-<span class="c1"># add class labels</span>
-<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
-<span class="n">label_dict</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">event_id</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="c1"># sort the labels by values (values are integer class labels)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">label_dict</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-<span class="c1"># plot the basic conf. matrix</span>
-<a href="../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 800x550 with 1 Axes&gt;
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  41.173 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  864 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-cropped-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/995f58ef1b3d29dce64caee24e5bf457/plot_bcic_iv_2a_moabb_cropped.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_2a_moabb_cropped.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/1224801107d6c3b138edf8ee33de0561/plot_bcic_iv_2a_moabb_cropped.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_2a_moabb_cropped.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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-   Create model and compute windowing parameters
-  </a>
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-<li class="toctree-l1 current active"><a class="current reference internal" href="#"> Basic trialwise decoding</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html"> More data-efficient "cropped decoding"</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_mne_dataset_example.html"> Your own datasets through MNE</a></li>
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-              
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="trialwise-decoding-on-bcic-iv-2a-dataset">
-<span id="sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"></span><h1>Trialwise Decoding on BCIC IV 2a Dataset<a class="headerlink" href="#trialwise-decoding-on-bcic-iv-2a-dataset" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows you how to train and test deep learning models with
-Braindecode in a classical EEG setting: you have trials of data with
-labels (e.g., Right Hand, Left Hand, etc.).</p>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<p>First, we load the data. In this tutorial, we use the functionality of
-braindecode to load datasets through
-<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB</a> to load the BCI
-Competition IV 2a data.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To load your own datasets either via mne or from
-preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
-Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
-Tutorial</a>.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<p>Now we apply preprocessing like bandpass filtering to our dataset. You
-can either apply functions provided by
-<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
-<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
-or apply your own functions, either to the MNE object or the underlying
-numpy array.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>These prepocessings are now directly applied to the loaded
-data, and not on-the-fly applied as transformations in
-PyTorch-libraries like
-<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50aaa1ed50&gt;
-</pre></div>
-</div>
-</section>
-<section id="cut-compute-windows">
-<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Permalink to this heading">#</a></h3>
-<p>Now we cut out compute windows, the inputs for the deep networks during
-training. In the case of trialwise decoding, we just have to decide if
-we want to cut out some part before and/or after the trial. For this
-dataset, in our work, it often was beneficial to also cut out 500 ms
-before the trial.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
-<span class="c1"># trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>We can easily split the dataset using additional info stored in the
-description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
-<code class="docutils literal notranslate"><span class="pre">session_T</span></code> for training and <code class="docutils literal notranslate"><span class="pre">session_E</span></code> for validation.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_T&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_E&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
-<p>Now we create the deep learning model! Braindecode comes with some
-predefined convolutional neural network architectures for raw
-time-domain EEG. Here, we use the shallow ConvNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
-learning with convolutional neural networks for EEG decoding and
-visualization</a>. These models are
-pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
-to use your own model, it just has to be a normal PyTorch
-<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-<span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
-<p>Now we train the network! EEGClassifier is a Braindecode object
-responsible for managing the training of neural networks. It inherits
-from skorch.NeuralNetClassifier, so the training logic is the same as in
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
-<p><strong>Note</strong>: In this tutorial, we use some default parameters that we
-have found to work well for motor decoding, however we strongly
-encourage you to perform your own hyperparameter optimization using
-cross validation on your training data.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-<span class="c1"># These values we found good for shallow network:</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-
-<span class="c1"># For deep4 they should be:</span>
-<span class="c1"># lr = 1 * 0.01</span>
-<span class="c1"># weight_decay = 0.5 * 0.001</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
-<span class="c1"># in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
--------  ----------------  ------------  ----------------  ------------  ------  ------
-      1            0.2535        1.6382            0.2500        5.2101  0.0006  6.2506
-      2            0.2708        1.2828            0.2465        5.6990  0.0005  6.1431
-      3            0.2604        1.1267            0.2500        5.0720  0.0002  6.1103
-      4            0.2708        1.1386            0.2500        4.2096  0.0000  6.2784
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=ShallowFBCSPNet(
-    (ensuredims): Ensure4d()
-    (dimshuffle): Expression(expression=transpose_time_to_spat)
-    (conv_time): Conv2d(1, 40, kernel_size=(25, 1), stride=(1, 1))
-    (conv_spat): Conv2d(40, 40, kernel_size=(1, 22), stride=(1, 1), bias=False)
-    (bnorm): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (conv_nonlin_exp): Expression(expression=square)
-    (pool): AvgPool2d(kernel_size=(75, 1), stride=(15, 1), padding=0)
-    (pool_nonlin_exp): Expression(expression=safe_log)
-    (drop): Dropout(p=0.5, inplace=False)
-    (conv_classifier): Conv2d(40, 4, kernel_size=(69, 1), stride=(1, 1))
-    (softmax): LogSoftmax(dim=1)
-    (squeeze): Expression(expression=squeeze_final_output)
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="plot-results">
-<h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>Now we use the history stored by Skorch throughout training to plot
-accuracy and loss curves.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.lines</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Extract loss and accuracy values for plotting from history object</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;train_accuracy&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_accuracy&#39;</span><span class="p">]</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results_columns</span></a><span class="p">,</span>
-                  <span class="n">index</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="p">[:,</span> <span class="s1">&#39;epoch&#39;</span><span class="p">])</span>
-
-<span class="c1"># get percent of misclass for better visual comparison to loss</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">assign</span><span class="p">(</span><span class="n">train_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">train_accuracy</span><span class="p">,</span>
-               <span class="n">valid_misclass</span><span class="o">=</span><span class="mi">100</span> <span class="o">-</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">df</span><span class="o">.</span><span class="n">valid_accuracy</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
-<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Loss&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:blue&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<span class="n">ax2</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html#matplotlib.axes.Axes.twinx" title="matplotlib.axes.Axes.twinx" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">twinx</span></a><span class="p">()</span>  <span class="c1"># instantiate a second axes that shares the same x-axis</span>
-
-<span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span> <span class="p">[</span><span class="s1">&#39;train_misclass&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_misclass&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">,</span> <span class="n">style</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="s1">&#39;:&#39;</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html#matplotlib.axes.Axes.tick_params" title="matplotlib.axes.Axes.tick_params" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span></a><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">labelcolor</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">labelsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s2">&quot;Misclassification Rate [%]&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;tab:red&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html#matplotlib.axes.Axes.set_ylim" title="matplotlib.axes.Axes.set_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylim</span></a><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_ylim.html#matplotlib.axes.Axes.get_ylim" title="matplotlib.axes.Axes.get_ylim" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">get_ylim</span></a><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">85</span><span class="p">)</span>  <span class="c1"># make some room for legend</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s2">&quot;Epoch&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-
-<span class="c1"># where some data has already been plotted to ax</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a> <span class="o">=</span> <span class="p">[]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Train&#39;</span><span class="p">))</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="o">.</span><span class="n">append</span><span class="p">(</span><a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D" class="sphx-glr-backref-module-matplotlib-lines sphx-glr-backref-type-py-class"><span class="n">Line2D</span></a><span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;:&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Valid&#39;</span><span class="p">))</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">,</span> <span class="p">[</span><span class="n">h</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span> <span class="k">for</span> <span class="n">h</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">handles</span></a><span class="p">],</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_001.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/></section>
-<section id="plot-confusion-matrix">
-<h2>Plot Confusion Matrix<a class="headerlink" href="#plot-confusion-matrix" title="Permalink to this heading">#</a></h2>
-<p>Generate a confusion matrix as in <a class="reference external" href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730">https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.23730</a></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.visualization</span> <span class="kn">import</span> <a href="../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a>
-
-<span class="c1"># generate confusion matrices</span>
-<span class="c1"># get the targets</span>
-<span class="n">y_true</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
-
-<span class="c1"># generating confusion matrix</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">)</span>
-
-<span class="c1"># add class labels</span>
-<span class="c1"># label_dict is class_name : str -&gt; i_class : int</span>
-<span class="n">label_dict</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">event_id</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-<span class="c1"># sort the labels by values (values are integer class labels)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">label_dict</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">kv</span><span class="p">:</span> <span class="n">kv</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
-<span class="c1"># plot the basic conf. matrix</span>
-<a href="../generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix" class="sphx-glr-backref-module-braindecode-visualization sphx-glr-backref-type-py-function"><span class="n">plot_confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">confusion_mat</span></a><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">labels</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 800x550 with 1 Axes&gt;
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  41.558 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  772 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-trial-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/748680401b0360239d00624375dd9587/plot_bcic_iv_2a_moabb_trial.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_bcic_iv_2a_moabb_trial.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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-     Preprocessing
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diff --git a/stable/auto_examples/plot_benchmark_preprocessing.html b/stable/auto_examples/plot_benchmark_preprocessing.html
deleted file mode 100644
index 6696cdf6..00000000
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-<!DOCTYPE html>
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-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="benchmarking-preprocessing-with-parallelization-and-serialization">
-<span id="sphx-glr-auto-examples-plot-benchmark-preprocessing-py"></span><h1>Benchmarking preprocessing with parallelization and serialization<a class="headerlink" href="#benchmarking-preprocessing-with-parallelization-and-serialization" title="Permalink to this heading">#</a></h1>
-<p>In this example, we compare the execution time and memory requirements of
-preprocessing data with the parallelization and serialization functionalities
-available in <a class="reference internal" href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a>.</p>
-<p>We compare 4 cases:</p>
-<ol class="arabic simple">
-<li><p>Sequential, no serialization</p></li>
-<li><p>Sequential, with serialization</p></li>
-<li><p>Parallel, no serialization</p></li>
-<li><p>Parallel, with serialization</p></li>
-</ol>
-<p>Case 1 is the simplest approach, in which all recordings in a
-<a class="reference internal" href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> are preprocessed one after the
-other. In this scenario, <a class="reference internal" href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> acts
-inplace, which means memory usage will likely stay stable (depending on the
-preprocessing operations) if recordings have been preloaded. However, two
-potential issues arise when working with large datasets: (1) if recordings have
-not been preloaded before preprocessing, <cite>preprocess()</cite> will need to load them
-and keep them in memory, in which case memory can become a bottleneck, and (2)
-sequential preprocessing can take a considerable amount of time to run when
-working with many recordings.</p>
-<p>A solution to the first issue (memory usage) is to save the preprocessed data
-to a file so it can be cleared from memory before moving on to the next
-recording (case 2). The recordings can then be reloaded with <cite>preload=False</cite>
-once they have all been saved to disk. This enables using the lazy loading
-capabilities of <a class="reference internal" href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> and avoids
-potential memory bottlenecks. The downside is that the writing to disk can take
-some time and of course requires disk space.</p>
-<p>A solution to the second issue (slow preprocessing) is to parallelize the
-preprocessing over multiple cores whenever possible (case 3). This can speed up
-preprocessing significantly. However, this approach will increase memory usage
-because of the way parallelization is implemented internally (with
-<cite>joblib</cite>, copies of (part of) the data must be made when sending arguments to
-parallel processes).</p>
-<p>Finally, case 4 (combining parallelization and serialization) is likely to be
-both fast and memory efficient. As shown in this example, this remains a
-tradeoff though, and the selected configuration should depend on the size of
-the dataset and the specific operations applied to the recordings.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">tempfile</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a>
-
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a>
-<span class="kn">from</span> <span class="nn">memory_profiler</span> <span class="kn">import</span> <span class="n">memory_usage</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>We create a function that goes through the usual three steps of data
-preparation: (1) data loading, (2) continuous data preprocessing,
-(3) windowing and (4) windowed data preprocessing. We use the
-<a class="reference internal" href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> dataset for testing purposes.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">):</span>
-    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">:</span>
-        <span class="n">tmp_dir</span> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.TemporaryDirectory" title="tempfile.TemporaryDirectory" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-class"><span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span></a><span class="p">()</span>
-        <span class="n">save_dir</span> <span class="o">=</span> <span class="n">tmp_dir</span><span class="o">.</span><span class="n">name</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">save_dir</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="c1"># (1) Load the data</span>
-    <span class="n">concat_ds</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
-        <span class="n">subject_ids</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">),</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">)</span>
-    <span class="n">sfreq</span> <span class="o">=</span> <span class="n">concat_ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-
-    <span class="c1"># (2) Preprocess the continuous data</span>
-    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span>
-        <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-        <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
-    <span class="p">]</span>
-    <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">concat_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
-
-    <span class="c1"># (3) Window the data</span>
-    <span class="n">windows_ds</span> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-        <span class="n">concat_ds</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="mi">30</span> <span class="o">*</span> <span class="n">sfreq</span><span class="p">),</span> <span class="kc">True</span><span class="p">,</span>
-        <span class="n">preload</span><span class="o">=</span><span class="n">preload</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
-
-    <span class="c1"># Preprocess the windowed data</span>
-    <span class="n">preprocessors</span> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
-    <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><span class="n">windows_ds</span><span class="p">,</span> <span class="n">preprocessors</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-               <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>Next, we can run our function and measure its run time and peak memory usage
-for each one of our 4 cases above. We call the function multiple times with
-each configuration to get better estimates.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To better characterize the run time vs. memory usage tradeoff for your
-specific configuration (as this will differ based on available hardware,
-data size and preprocessing operations), we recommend adapting this example
-to your use case and running it on your machine.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Number of repetitions</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># Number of recordings to load and preprocess</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>  <span class="c1"># Number of parallel processes</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a> <span class="ow">in</span> <a href="https://docs.python.org/3/library/itertools.html#itertools.product" title="itertools.product" class="sphx-glr-backref-module-itertools sphx-glr-backref-type-py-function"><span class="n">product</span></a><span class="p">(</span>
-        <span class="nb">range</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_repets</span></a><span class="p">),</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="p">],</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">):</span>
-
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">memory_usage</span><span class="p">(</span>
-        <span class="n">proc</span><span class="o">=</span><span class="p">(</span><span class="n">prepare_data</span><span class="p">,</span> <span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <span class="kc">False</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="p">{})))</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/time.html#time.time" title="time.time" class="sphx-glr-backref-module-time sphx-glr-backref-type-py-function"><span class="n">time</span><span class="o">.</span><span class="n">time</span></a><span class="p">()</span> <span class="o">-</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start</span></a>
-
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="o">.</span><span class="n">append</span><span class="p">({</span>
-        <span class="s1">&#39;n_recs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_recs</span></a><span class="p">,</span>
-        <span class="s1">&#39;max_mem&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mem</span></a><span class="p">,</span>
-        <span class="s1">&#39;save&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span>
-        <span class="s1">&#39;n_jobs&#39;</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">,</span>
-        <span class="s1">&#39;time&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">time_taken</span></a>
-    <span class="p">})</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwtuxpqt6 contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:570: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
-  f&quot;datasets!&quot;, UserWarning)
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwtuxpqt6 contains other subdirectories or files [&#39;1&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpwtuxpqt6 contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpn9t88czc contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:570: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
-  f&quot;datasets!&quot;, UserWarning)
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpn9t88czc contains other subdirectories or files [&#39;1&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpn9t88czc contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpkb3dhgyi contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:570: UserWarning: The number of saved datasets (1) does not match the number of existing subdirectories (2). You may now encounter a mix of differently preprocessed datasets!
-  f&quot;datasets!&quot;, UserWarning)
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpkb3dhgyi contains other subdirectories or files [&#39;1&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmpkb3dhgyi contains other subdirectories or files [&#39;0&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-</pre></div>
-</div>
-<p>Finally, we can plot the results:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">results</span></a><span class="p">)</span>
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a> <span class="o">=</span> <span class="p">{</span><span class="kc">True</span><span class="p">:</span> <span class="s1">&#39;tab:orange&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">:</span> <span class="s1">&#39;tab:blue&#39;</span><span class="p">}</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">n</span><span class="p">:</span> <span class="n">m</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_jobs</span></a><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span><span class="p">])}</span>
-<span class="k">for</span> <span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">),</span> <span class="n">sub_df</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;save&#39;</span><span class="p">,</span> <span class="s1">&#39;n_jobs&#39;</span><span class="p">]):</span>
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">],</span> <span class="n">y</span><span class="o">=</span><span class="n">sub_df</span><span class="p">[</span><span class="s1">&#39;max_mem&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">colors</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="p">],</span>
-               <span class="n">marker</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">markers</span></a><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;save=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">save</span></a><span class="si">}</span><span class="s1">, n_jobs=</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int64" title="numpy.int64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">n_jobs</span></a><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Execution time (s)&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Memory usage (MiB)&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html#matplotlib.axes.Axes.set_title" title="matplotlib.axes.Axes.set_title" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_title</span></a><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Loading and preprocessing </span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">all_n_recs</span></a><span class="si">}</span><span class="s1"> recordings from Sleep &#39;</span>
-             <span class="s1">&#39;Physionet&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_benchmark_preprocessing_001.png" srcset="../_images/sphx_glr_plot_benchmark_preprocessing_001.png" alt="Loading and preprocessing 2 recordings from Sleep Physionet" class = "sphx-glr-single-img"/><p>We see that parallel preprocessing without serialization (blue crosses) is
-faster than simple sequential processing (blue circles), however it uses more
-memory.</p>
-<p>Combining parallel preprocessing and serialization (orange crosses) reduces
-memory usage significantly, however it increases run time by a few seconds.
-Depending on available resources (e.g. in limited memory settings), it might
-therefore be more advantageous to use both parallelization and serialization
-together.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  42.156 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  441 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-benchmark-preprocessing-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/817b00270adee98fe11f869f8c64d9cc/plot_benchmark_preprocessing.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_benchmark_preprocessing.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/5ae34d87007c72630b1b1fd61f73ee5d/plot_benchmark_preprocessing.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_benchmark_preprocessing.ipynb</span></code></a></p>
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-    <ul class="current nav bd-sidenav">
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html"> Basic trialwise decoding</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html"> More data-efficient "cropped decoding"</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_mne_dataset_example.html"> Your own datasets through MNE</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#"> Your own datasets through Numpy</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-custom-dataset-example-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="custom-dataset-example">
-<span id="sphx-glr-auto-examples-plot-custom-dataset-example-py"></span><h1>Custom Dataset Example<a class="headerlink" href="#custom-dataset-example" title="Permalink to this heading">#</a></h1>
-<p>This example shows how to convert data X and y as numpy arrays to a braindecode
-compatible data format.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a>
-</pre></div>
-</div>
-<p>To set up the example, we first fetch some data using mne:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
-<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
-
-<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
-<span class="c1"># and then return the paths to the files.</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Load each of the files</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
-         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-</pre></div>
-</div>
-<p>We take the required data, targets and additional information sampling
-frequency and channel names from the loaded data. Note that this data and
-information can originate from any source.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <span class="p">[</span><span class="n">raw</span><span class="o">.</span><span class="n">get_data</span><span class="p">()</span> <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;ch_names&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-<p>Convert to data format compatible with skorch and braindecode:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.create_from_X_y.html#braindecode.datasets.create_from_X_y" title="braindecode.datasets.create_from_X_y" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_X_y</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_names</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>  <span class="c1"># look as dataset description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Creating RawArray with float64 data, n_channels=64, n_times=20000
-    Range : 0 ... 19999 =      0.000 ...   124.994 secs
-Ready.
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-Using data from preloaded Raw for 40 events and 500 original time points ...
-0 bad epochs dropped
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>target</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>5</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>6</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>9</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>10</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>13</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>14</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>You can manipulate the dataset</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">))</span>  <span class="c1"># get the number of samples</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>240
-</pre></div>
-</div>
-<p>You can now index the data</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a> <span class="o">=</span> <span class="mi">0</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x_i</span><span class="o">.</span><span class="n">shape</span></a>  <span class="c1"># the EEG data</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;n_channels=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="si">}</span><span class="s2">  -- n_times=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_times</span></a><span class="si">}</span><span class="s2"> -- y_i=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_i</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;start_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">start_ind</span></a><span class="si">}</span><span class="s2"> -- stop_ind=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">stop_ind</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using data from preloaded Raw for 1 events and 500 original time points ...
-n_channels=64  -- n_times=500 -- y_i=5
-start_ind=0 -- stop_ind=500
-</pre></div>
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-<li class="toctree-l1"><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Data Augmentation on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_data_augmentation_search.html">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Trialwise Decoding on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model</a></li>
-</ul>
-
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-data-augmentation-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="data-augmentation-on-bcic-iv-2a-dataset">
-<span id="sphx-glr-auto-examples-plot-data-augmentation-py"></span><h1>Data Augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#data-augmentation-on-bcic-iv-2a-dataset" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows how to train EEG deep models with data augmentation. It
-follows the trial-wise decoding example and also illustrates the effect of a
-transform on the input signals.</p>
-<div class="contents local topic" id="this-example-covers">
-<p class="topic-title">This example covers:</p>
-<ul class="simple">
-<li><p><a class="reference internal" href="#loading-and-preprocessing-the-dataset" id="id1">Loading and preprocessing the dataset</a></p>
-<ul>
-<li><p><a class="reference internal" href="#loading" id="id2">Loading</a></p></li>
-<li><p><a class="reference internal" href="#preprocessing" id="id3">Preprocessing</a></p></li>
-<li><p><a class="reference internal" href="#extracting-windows" id="id4">Extracting windows</a></p></li>
-<li><p><a class="reference internal" href="#split-dataset-into-train-and-valid" id="id5">Split dataset into train and valid</a></p></li>
-</ul>
-</li>
-<li><p><a class="reference internal" href="#defining-a-transform" id="id6">Defining a Transform</a></p>
-<ul>
-<li><p><a class="reference internal" href="#manipulating-one-session-and-visualizing-the-transformed-data" id="id7">Manipulating one session and visualizing the transformed data</a></p></li>
-</ul>
-</li>
-<li><p><a class="reference internal" href="#training-a-model-with-data-augmentation" id="id8">Training a model with data augmentation</a></p>
-<ul>
-<li><p><a class="reference internal" href="#create-model" id="id9">Create model</a></p></li>
-<li><p><a class="reference internal" href="#create-an-eegclassifier-with-the-desired-augmentation" id="id10">Create an EEGClassifier with the desired augmentation</a></p></li>
-<li><p><a class="reference internal" href="#manually-composing-transforms" id="id11">Manually composing Transforms</a></p></li>
-<li><p><a class="reference internal" href="#setting-the-data-augmentation-at-the-dataset-level" id="id12">Setting the data augmentation at the Dataset level</a></p></li>
-</ul>
-</li>
-</ul>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-</pre></div>
-</div>
-<section id="loading-and-preprocessing-the-dataset">
-<h2><a class="toc-backref" href="#id1">Loading and preprocessing the dataset</a><a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3><a class="toc-backref" href="#id2">Loading</a><a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3><a class="toc-backref" href="#id3">Preprocessing</a><a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50d5678450&gt;
-</pre></div>
-</div>
-</section>
-<section id="extracting-windows">
-<h3><a class="toc-backref" href="#id4">Extracting windows</a><a class="headerlink" href="#extracting-windows" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
-<span class="c1"># define how trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3><a class="toc-backref" href="#id5">Split dataset into train and valid</a><a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_T&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_E&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="defining-a-transform">
-<h2><a class="toc-backref" href="#id6">Defining a Transform</a><a class="headerlink" href="#defining-a-transform" title="Permalink to this heading">#</a></h2>
-<p>Data can be manipulated by transforms, which are callable objects. A
-transform is usually handled by a custom data loader, but can also be called
-directly on input data, as demonstrated below for illutrative purposes.</p>
-<p>First, we need to define a Transform. Here we chose the FrequencyShift, which
-randomly translates all frequencies within a given range.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a>
-
-<a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a> <span class="o">=</span> <a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
-    <span class="n">probability</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span>  <span class="c1"># defines the probability of actually modifying the input</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<section id="manipulating-one-session-and-visualizing-the-transformed-data">
-<h3><a class="toc-backref" href="#id7">Manipulating one session and visualizing the transformed data</a><a class="headerlink" href="#manipulating-one-session-and-visualizing-the-transformed-data" title="Permalink to this heading">#</a></h3>
-<p>Next, let us augment one session to show the resulting frequency shift. The
-data of an mne Epoch is used here to make usage of mne functions.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span>  <span class="c1"># original epochs</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs.get_data" title="mne.Epochs.get_data" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-method"><span class="n">epochs</span><span class="o">.</span><span class="n">get_data</span></a><span class="p">()</span>
-<span class="c1"># This allows to apply the transform with a fixed shift (10 Hz) for</span>
-<span class="c1"># visualization instead of sampling the shift randomly between -2 and 2 Hz</span>
-<a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="p">,</span> <a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a> <span class="o">=</span> <a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transform</span></a><span class="o">.</span><span class="n">operation</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">(),</span> <span class="kc">None</span><span class="p">,</span> <span class="mf">10.</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/augmentation/functional.py:505: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matricesor `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at  /home/conda/feedstock_root/build_artifacts/pytorch-recipe_1664817695526/work/aten/src/ATen/native/TensorShape.cpp:2981.)
-  N_padded, n_channels, 1).T
-</pre></div>
-</div>
-<p>The psd of the transformed session has now been shifted by 10 Hz, as one can
-see on the psd plot.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<span class="k">def</span> <span class="nf">plot_psd</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="p">):</span>
-    <span class="n">psds</span><span class="p">,</span> <span class="n">freqs</span> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.time_frequency.psd_array_multitaper.html#mne.time_frequency.psd_array_multitaper" title="mne.time_frequency.psd_array_multitaper" class="sphx-glr-backref-module-mne-time_frequency sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">time_frequency</span><span class="o">.</span><span class="n">psd_array_multitaper</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-                                                          <span class="n">fmin</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">fmax</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-    <span class="n">psds</span> <span class="o">=</span> <span class="mf">10.</span> <span class="o">*</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">log10</span></a><span class="p">(</span><span class="n">psds</span><span class="p">)</span>
-    <span class="n">psds_mean</span> <span class="o">=</span> <span class="n">psds</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freqs</span><span class="p">,</span> <span class="n">psds_mean</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
-
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">_</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
-<span class="n">plot_psd</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X</span></a><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
-<span class="n">plot_psd</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_tr</span></a><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">ax</span><span class="p">,</span> <span class="s1">&#39;shifted&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span><span class="n">title</span><span class="o">=</span><span class="s1">&#39;Multitaper PSD (gradiometers)&#39;</span><span class="p">,</span> <span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">,</span>
-       <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;Power Spectral Density (dB)&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_data_augmentation_001.png" srcset="../_images/sphx_glr_plot_data_augmentation_001.png" alt="Multitaper PSD (gradiometers)" class = "sphx-glr-single-img"/></section>
-</section>
-<section id="training-a-model-with-data-augmentation">
-<h2><a class="toc-backref" href="#id8">Training a model with data augmentation</a><a class="headerlink" href="#training-a-model-with-data-augmentation" title="Permalink to this heading">#</a></h2>
-<p>Now that we know how to instantiate <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
-to use them to train a model and try to improve its generalization power.
-Let’s first create a model.</p>
-<section id="create-model">
-<h3><a class="toc-backref" href="#id9">Create model</a><a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h3>
-<p>The model to be trained is defined as usual.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-an-eegclassifier-with-the-desired-augmentation">
-<h3><a class="toc-backref" href="#id10">Create an EEGClassifier with the desired augmentation</a><a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Permalink to this heading">#</a></h3>
-<p>In order to train with data augmentation, a custom data loader can be
-for the training. Multiple transforms can be passed to it and will be applied
-sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span> <a href="../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a>
-
-<a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a> <span class="o">=</span> <a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FrequencyShift</span></a><span class="p">(</span>
-    <span class="n">probability</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">max_delta_freq</span><span class="o">=</span><span class="mf">2.</span>  <span class="c1"># the frequency shifts are sampled now between -2 and 2 Hz</span>
-<span class="p">)</span>
-
-<a href="../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a> <span class="o">=</span> <a href="../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SignFlip</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">.1</span><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.augmentation.FrequencyShift.html#braindecode.augmentation.FrequencyShift" title="braindecode.augmentation.FrequencyShift" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">freq_shift</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.augmentation.SignFlip.html#braindecode.augmentation.SignFlip" title="braindecode.augmentation.SignFlip" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sign_flip</span></a>
-<span class="p">]</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<p>The model is now trained as in the trial-wise example. The
-<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
-transforms are passed as arguments.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">iterator_train</span><span class="o">=</span><a href="../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
-    <span class="n">iterator_train__transforms</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>  <span class="c1"># This sets the augmentations to use</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
-<span class="c1"># supplied in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
--------  ----------------  ------------  ----------------  ------------  ------  ------
-      1            0.2535        1.6458            0.2535        5.1248  0.0006  6.4900
-      2            0.2674        1.3297            0.2500        5.7605  0.0005  6.3248
-      3            0.2639        1.1539            0.2500        5.1229  0.0002  6.3496
-      4            0.2708        1.1567            0.2535        4.2554  0.0000  6.3319
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=ShallowFBCSPNet(
-    (ensuredims): Ensure4d()
-    (dimshuffle): Expression(expression=transpose_time_to_spat)
-    (conv_time): Conv2d(1, 40, kernel_size=(25, 1), stride=(1, 1))
-    (conv_spat): Conv2d(40, 40, kernel_size=(1, 22), stride=(1, 1), bias=False)
-    (bnorm): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (conv_nonlin_exp): Expression(expression=square)
-    (pool): AvgPool2d(kernel_size=(75, 1), stride=(15, 1), padding=0)
-    (pool_nonlin_exp): Expression(expression=safe_log)
-    (drop): Dropout(p=0.5, inplace=False)
-    (conv_classifier): Conv2d(40, 4, kernel_size=(69, 1), stride=(1, 1))
-    (softmax): LogSoftmax(dim=1)
-    (squeeze): Expression(expression=squeeze_final_output)
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="manually-composing-transforms">
-<h3><a class="toc-backref" href="#id11">Manually composing Transforms</a><a class="headerlink" href="#manually-composing-transforms" title="Permalink to this heading">#</a></h3>
-<p>It would be equivalent (although more verbose) to pass to <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code> a
-composition of the same transforms:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a>
-
-<a href="../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">composed_transforms</span></a> <span class="o">=</span> <a href="../generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose" title="braindecode.augmentation.Compose" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">Compose</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">)</span>
-</pre></div>
-</div>
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-<section id="setting-the-data-augmentation-at-the-dataset-level">
-<h3><a class="toc-backref" href="#id12">Setting the data augmentation at the Dataset level</a><a class="headerlink" href="#setting-the-data-augmentation-at-the-dataset-level" title="Permalink to this heading">#</a></h3>
-<p>Also note that it is also possible for most of the transforms to pass them
-directly to the WindowsDataset object through the <cite>transform</cite> argument, as
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-<li class="toctree-l1"><a class="reference internal" href="plot_custom_dataset_example.html">Custom Dataset Example</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_tuh_discrete_multitarget.html">Multiple discrete targets with the TUH EEG Corpus</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_benchmark_preprocessing.html">Benchmarking preprocessing with parallelization and serialization</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_dataset_example.html">MOABB Dataset Example</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_regression.html">Regression example on fake data</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html">Hyperparameter tuning with scikit-learn</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_tuh_eeg_corpus.html">Process a big data EEG resource (TUH EEG Corpus)</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_usleep.html">Sleep staging on the Sleep Physionet dataset using U-Sleep network</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_data_augmentation.html">Data Augmentation on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_eldele2021.html">Sleep staging on the Sleep Physionet dataset using Eldele2021</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_sleep_staging_chambon2018.html">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</a></li>
-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Searching the best data augmentation on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html">Trialwise Decoding on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="benchmark_lazy_eager_loading.html">Benchmarking eager and lazy loading</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html">Cropped Decoding on BCIC IV 2a Dataset</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_relative_positioning.html">Self-supervised learning on EEG with relative positioning</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_how_train_test_and_tune.html">How to train, test and tune your model</a></li>
-</ul>
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-</nav>
-    </div>
-  </div>
-  
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-  
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
-    </div>
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-      <main class="bd-main">
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-          <div class="bd-article-container">
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="searching-the-best-data-augmentation-on-bcic-iv-2a-dataset">
-<span id="sphx-glr-auto-examples-plot-data-augmentation-search-py"></span><h1>Searching the best data augmentation on BCIC IV 2a Dataset<a class="headerlink" href="#searching-the-best-data-augmentation-on-bcic-iv-2a-dataset" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows how to search data augmentations using braindecode.
-Indeed, it is known that the best augmentation to use often dependent on the task
-or phenomenon studied. Here we follow the methodology proposed in [1] on the
-openly available BCI IV 2a Dataset.</p>
-<div class="topic">
-<p class="topic-title">Data Augmentation</p>
-<p>Data augmentation could be a step in training deep learning models.
-For decoding brain signals, recent studies have shown that artificially
-generating samples may increase the final performance of a deep learning model <a class="footnote-reference brackets" href="#id3" id="id1">1</a>.
-Other studies have shown that data augmentation can be used to cast
-a self-supervised paradigm, presenting a more diverse
-view of the data, both with pretext tasks and contrastive learning <a class="footnote-reference brackets" href="#id4" id="id2">2</a>.</p>
-</div>
-<p>Both approaches demand an intense comparison to find the best fit with the data.
-This view is supported by Rommel, C., Paillard, J., Moreau, T., &amp; Gramfort, A. (2022),
-who demonstrate the importance of the selection the right transformation and
-strength for each different type of task considered.
-Here, we use the augmentation module present in braindecode in the context of
-trialwise decoding with the BCI IV 2a dataset.</p>
-<section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id3"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id5">2</a>)</span></dt>
-<dd><p>Rommel, C., Paillard, J., Moreau, T., &amp; Gramfort, A. (2022)
-Data augmentation for learning predictive models on EEG:
-a systematic comparison. <a class="reference external" href="https://arxiv.org/abs/2206.14483">https://arxiv.org/abs/2206.14483</a></p>
-</dd>
-<dt class="label" id="id4"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A., &amp; Gramfort, A. (2021).
-Uncovering the structure of clinical EEG signals with self-supervised learning.
-Journal of Neural Engineering, 18(4), 046020.</p>
-</dd>
-</dl>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Bruno Aristimunha &lt;a.bruno@ufabc.edu.br&gt;</span>
-<span class="c1">#          Cédric Rommel &lt;cedric.rommel@inria.fr&gt;</span>
-<span class="c1"># License: BSD (3-clause)</span>
-</pre></div>
-</div>
-</section>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50b201e790&gt;
-</pre></div>
-</div>
-</section>
-<section id="extracting-windows">
-<h3>Extracting windows<a class="headerlink" href="#extracting-windows" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to</span>
-<span class="c1"># define how trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>Following the rules of the BCI competition</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_T&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_E&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="defining-a-list-of-transforms">
-<h2>Defining a list of transforms<a class="headerlink" href="#defining-a-list-of-transforms" title="Permalink to this heading">#</a></h2>
-<p>In this tutorial, we will use three categories of augmentations.
-This categorization has been proposed by <a class="footnote-reference brackets" href="#id3" id="id5">1</a> to explain and aggregate
-the several possibilities of augmentations in EEG, being them: a) Frequency domain
-augmentations, b) Time domain augmentations, and c) Spatial domain augmentations.</p>
-<p>From this same paper, we selected the best augmentations in each type: FTSurrogate,
-SmoothTimeMask, ChannelsDropout, respectively.</p>
-<p>For each augmentation, we adjustable two values from a range for one parameter
-inside the transformation.</p>
-<p>It is important to remember that you can increase the range.
-For that, we need to define three lists of transformations and range for the parameter
-∆φmax in FTSurrogate where ∆φmax ∈ [0, 2π); for ∆t in SmoothTimeMask is ∆t ∈ [0, 2];
-For the method ChannelsDropout, we analyse the parameter p_drop ∈ [0, 1].</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">,</span> <a href="../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">,</span> <a href="../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate" title="braindecode.augmentation.FTSurrogate" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">FTSurrogate</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">phase_noise_magnitude</span><span class="o">=</span><span class="n">phase_freq</span><span class="p">,</span>
-                               <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">phase_freq</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.augmentation.SmoothTimeMask.html#braindecode.augmentation.SmoothTimeMask" title="braindecode.augmentation.SmoothTimeMask" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">SmoothTimeMask</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">mask_len_samples</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">*</span> <span class="n">second</span><span class="p">),</span>
-                                  <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">second</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout" title="braindecode.augmentation.ChannelsDropout" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">ChannelsDropout</span></a><span class="p">(</span><span class="n">probability</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">p_drop</span><span class="o">=</span><span class="n">prob</span><span class="p">,</span>
-                                      <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span> <span class="k">for</span> <span class="n">prob</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.linspace.html#numpy.linspace" title="numpy.linspace" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">linspace</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
-</pre></div>
-</div>
-</section>
-<section id="training-a-model-with-data-augmentation">
-<h2>Training a model with data augmentation<a class="headerlink" href="#training-a-model-with-data-augmentation" title="Permalink to this heading">#</a></h2>
-<p>Now that we know how to instantiate three list of <code class="docutils literal notranslate"><span class="pre">Transforms</span></code>, it is time to learn how
-to use them to train a model and try to search the best for the dataset.
-Let’s first create a model for search a parameter.</p>
-<section id="create-model">
-<h3>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h3>
-<p>The model to be trained is defined as usual.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-an-eegclassifier-with-the-desired-augmentation">
-<h3>Create an EEGClassifier with the desired augmentation<a class="headerlink" href="#create-an-eegclassifier-with-the-desired-augmentation" title="Permalink to this heading">#</a></h3>
-<p>In order to train with data augmentation, a custom data loader can be
-for the training. Multiple transforms can be passed to it and will be applied
-sequentially to the batched data within the <code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> object.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.augmentation</span> <span class="kn">import</span> <a href="../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<p>The model is now trained as in the trial-wise example. The
-<code class="docutils literal notranslate"><span class="pre">AugmentedDataLoader</span></code> is used as the train iterator and the list of
-transforms are passed as arguments.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">iterator_train</span><span class="o">=</span><a href="../generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader" title="braindecode.augmentation.AugmentedDataLoader" class="sphx-glr-backref-module-braindecode-augmentation sphx-glr-backref-type-py-class"><span class="n">AugmentedDataLoader</span></a><span class="p">,</span>  <span class="c1"># This tells EEGClassifier to use a custom DataLoader</span>
-    <span class="n">iterator_train__transforms</span><span class="o">=</span><span class="p">[],</span>  <span class="c1"># This sets is handled by GridSearchCV</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># GridSearchCV will control the split and train/validation over the dataset</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s1">&#39;lr_scheduler&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>To use the skorch framework, it is necessary to transform the windows
-dataset using the module SliceData. Also, it is mandatory to eval the
-generator of the training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_X</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">(</span><span class="nb">list</span><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)))</span>
-</pre></div>
-</div>
-<p>Given the trialwise approach, here we use the KFold approach and
-GridSearchCV.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_freq</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_time</span></a> <span class="o">+</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms_spatial</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;iterator_train__transforms&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">transforms</span></a><span class="p">,</span>
-<span class="p">}</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">clf</span><span class="o">.</span><span class="n">verbose</span></a> <span class="o">=</span> <span class="mi">0</span>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
-    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
-    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
-    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
-    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span><span class="p">)</span>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 6 candidates, totalling 12 fits
-
-GridSearchCV(cv=KFold(n_splits=2, random_state=20200220, shuffle=True),
-             error_score=&#39;raise&#39;,
-             estimator=&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[uninitialized](
-  module=ShallowFBCSPNet(
-    (ensuredims): Ensure4d()
-    (dimshuffle): Expression(expression=transpose_time_to_spat)
-    (conv_time): Conv2d(1, 40, kernel_size=(25, 1), stride=(1, 1))
-    (conv_spat): Conv2d(40, 40, kerne...
-    (drop): Dropout(p=0.5, inplace=False)
-    (conv_classifier): Conv2d(40, 4, kernel_size=(69, 1), stride=(1, 1))
-    (softmax): LogSoftmax(dim=1)
-    (squeeze): Expression(expression=squeeze_final_output)
-  ),
-),
-             param_grid={&#39;iterator_train__transforms&#39;: [FTSurrogate(),
-                                                        FTSurrogate(),
-                                                        SmoothTimeMask(),
-                                                        SmoothTimeMask(),
-                                                        ChannelsDropout(),
-                                                        ChannelsDropout()]},
-             return_train_score=True, scoring=&#39;accuracy&#39;, verbose=1)
-</pre></div>
-</div>
-</section>
-<section id="analysing-the-best-fit">
-<h3>Analysing the best fit<a class="headerlink" href="#analysing-the-best-fit" title="Permalink to this heading">#</a></h3>
-<p>Next, just perform an analysis of the best fit, and the parameters,
-remembering the order that was adjusted.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
-
-<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">best_aug</span></a> <span class="o">=</span> <span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.around.html#numpy.around" title="numpy.around" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">around</span></a><span class="p">(</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a> <span class="o">=</span> <span class="s1">&#39;Best augmentation is saved in best_aug which gave a mean validation accuracy&#39;</span> <span class="o">+</span> \
-                 <span class="s1">&#39;of </span><span class="si">{}</span><span class="s1">% (train accuracy of </span><span class="si">{}</span><span class="s1">%).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">validation_score</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">training_score</span></a><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">report_message</span></a><span class="p">)</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_X</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_y</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_X</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_y</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">%.&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Best augmentation is saved in best_aug which gave a mean validation accuracyof 26.74% (train accuracy of 28.47%).
-Eval accuracy is 26.74%.
-</pre></div>
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-     Split dataset into train and valid
-    </a>
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- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#defining-a-list-of-transforms">
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-<li class="toctree-l1"><a class="reference internal" href="plot_split_dataset.html">Split Dataset Example</a></li>
-<li class="toctree-l1"><a class="reference internal" href="plot_load_save_datasets.html">Load and save dataset example</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-dataset-example-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="moabb-dataset-example">
-<span id="sphx-glr-auto-examples-plot-dataset-example-py"></span><h1>MOABB Dataset Example<a class="headerlink" href="#moabb-dataset-example" title="Permalink to this heading">#</a></h1>
-<p>In this example, we show how to fetch and prepare a MOABB dataset for usage
-with Braindecode.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#          Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#          Simon Brandt &lt;simonbrandt@protonmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> \
-    <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
-</pre></div>
-</div>
-<p>First, we create a dataset based on BCIC IV 2a fetched with MOABB,</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
-<p>ds has a pandas DataFrame with additional description of its internal datasets</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_5</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>We can iterate through ds which yields one time point of a continuous signal x,
-and a target y (which can be None if targets are not defined for the entire
-continuous signal).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="ow">in</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">)</span>
-    <span class="k">break</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(26, 1) None
-</pre></div>
-</div>
-<p>We can apply preprocessing transforms that are defined in mne and work
-in-place, such as resampling, bandpass filtering, or electrode selection.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
-<span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">])</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>250.0
-100.0
-</pre></div>
-</div>
-<p>We can easily split ds based on a criteria applied to the description
-DataFrame:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">({</span><span class="n">subset_name</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span> <span class="k">for</span> <span class="n">subset_name</span><span class="p">,</span> <span class="n">subset</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;session_E&#39;: 232164, &#39;session_T&#39;: 232164}
-</pre></div>
-</div>
-<p>Next, we use a windower to extract events from the dataset based on events:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
-<p>We can iterate through the windows_ds which yields a window x,
-a target y, and window_ind (which itself contains <code class="docutils literal notranslate"><span class="pre">i_window_in_trial</span></code>,
-<code class="docutils literal notranslate"><span class="pre">i_start_in_trial</span></code>, and <code class="docutils literal notranslate"><span class="pre">i_stop_in_trial</span></code>, which are required for
-combining window predictions in the scorer).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a> <span class="ow">in</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a><span class="p">)</span>
-    <span class="k">break</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(23, 400) 3 [0, 300, 700]
-</pre></div>
-</div>
-<p>We visually inspect the windows:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">=</span> <span class="mi">2</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">3.5</span> <span class="o">*</span> <span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="mf">3.5</span><span class="p">),</span>
-                           <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">):</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">T</span></a><span class="p">)</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">4e-5</span><span class="p">,</span> <span class="mf">4e-5</span><span class="p">)</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;label=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a><span class="p">:</span>
-        <span class="k">break</span>
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_dataset_example_001.png" srcset="../_images/sphx_glr_plot_dataset_example_001.png" alt="label=3, label=3, label=0" class = "sphx-glr-single-img"/><p>Alternatively, we can create evenly spaced (“sliding”) windows using a
-different windower.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sliding_windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1200</span><span class="p">,</span> <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sliding_windows_dataset</span></a><span class="p">))</span>
-<span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a> <span class="ow">in</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sliding_windows_dataset</span></a><span class="p">:</span>
-    <span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">shape</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a><span class="p">)</span>
-    <span class="k">break</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sliding_windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:603: UserWarning: Meaning of `trial_stop_offset_samples`=0 has changed, use `None` to indicate end of trial/recording. Using `None`.
-  &#39;Meaning of `trial_stop_offset_samples`=0 has changed, use `None` &#39;
-468
-(23, 1200) -1 [0, 0, 1200]
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_5</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>Transforms can also be applied on windows in the same way as shown
-above on continuous data:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">crop_windows</span><span class="p">(</span><span class="n">windows</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="p">,</span> <span class="n">stop_offset_samples</span><span class="p">):</span>
-    <span class="n">fs</span> <span class="o">=</span> <span class="n">windows</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
-    <span class="n">windows</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><span class="n">tmin</span><span class="o">=</span><span class="n">start_offset_samples</span> <span class="o">/</span> <span class="n">fs</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="n">stop_offset_samples</span> <span class="o">/</span> <span class="n">fs</span><span class="p">,</span>
-                 <span class="n">include_tmax</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs_preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">crop_windows</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                 <span class="n">stop_offset_samples</span><span class="o">=</span><span class="mi">900</span><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;ch_names&quot;</span><span class="p">],</span>
-      <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">times</span><span class="p">))</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">epochs_preprocessors</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;ch_names&quot;</span><span class="p">],</span>
-      <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">times</span><span class="p">))</span>
-
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">=</span> <span class="mi">2</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">3.5</span> <span class="o">*</span> <span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="mf">3.5</span><span class="p">),</span>
-                           <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">,</span> <span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_ind</span></a><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">):</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span><span class="o">.</span><span class="n">T</span></a><span class="p">)</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mf">4e-5</span><span class="p">,</span> <span class="mf">4e-5</span><span class="p">)</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ax_arr</span></a><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;label=</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">max_i</span></a><span class="p">:</span>
-        <span class="k">break</span>
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_dataset_example_002.png" srcset="../_images/sphx_glr_plot_dataset_example_002.png" alt="label=3, label=3, label=0" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;Fz&#39;, &#39;FC3&#39;, &#39;FC1&#39;, &#39;FCz&#39;, &#39;FC2&#39;, &#39;FC4&#39;, &#39;C5&#39;, &#39;C3&#39;, &#39;C1&#39;, &#39;Cz&#39;, &#39;C2&#39;, &#39;C4&#39;, &#39;C6&#39;, &#39;CP3&#39;, &#39;CP1&#39;, &#39;CPz&#39;, &#39;CP2&#39;, &#39;CP4&#39;, &#39;P1&#39;, &#39;Pz&#39;, &#39;P2&#39;, &#39;POz&#39;, &#39;stim&#39;] 400
-[&#39;Fz&#39;, &#39;FC3&#39;, &#39;FC1&#39;, &#39;FCz&#39;, &#39;FC2&#39;, &#39;FC4&#39;, &#39;C5&#39;, &#39;C3&#39;, &#39;C1&#39;, &#39;Cz&#39;, &#39;C2&#39;, &#39;C4&#39;, &#39;C6&#39;, &#39;CP3&#39;, &#39;CP1&#39;, &#39;CPz&#39;, &#39;CP2&#39;, &#39;CP4&#39;, &#39;P1&#39;, &#39;Pz&#39;, &#39;P2&#39;, &#39;POz&#39;] 300
-</pre></div>
-</div>
-<p>Again, we can easily split windows_ds based on some criteria in the
-description DataFrame:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">({</span><span class="n">subset_name</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span> <span class="k">for</span> <span class="n">subset_name</span><span class="p">,</span> <span class="n">subset</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subsets</span></a><span class="o">.</span><span class="n">items</span><span class="p">()})</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;session_E&#39;: 576, &#39;session_T&#39;: 576}
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  8.020 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  371 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-dataset-example-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/c9c12da7d088388ecce38afe9fe69371/plot_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dataset_example.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/176a7695926ccfe061692c4af12b673d/plot_dataset_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_dataset_example.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
-</section>
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diff --git a/stable/auto_examples/plot_how_train_test_and_tune.html b/stable/auto_examples/plot_how_train_test_and_tune.html
deleted file mode 100644
index 9d0e0251..00000000
--- a/stable/auto_examples/plot_how_train_test_and_tune.html
+++ /dev/null
@@ -1,1299 +0,0 @@
-
-<!DOCTYPE html>
-
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
-
-    <title>How to train, test and tune your model &#8212; Braindecode 0.7 documentation</title>
-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
-    document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
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-<section class="sphx-glr-example-title" id="how-to-train-test-and-tune-your-model">
-<span id="sphx-glr-auto-examples-plot-how-train-test-and-tune-py"></span><h1>How to train, test and tune your model<a class="headerlink" href="#how-to-train-test-and-tune-your-model" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows you how to properly train, tune and test your deep learning
-models with Braindecode. We will use the BCIC IV 2a dataset as a showcase example.</p>
-<p>The methods shown can be applied to any standard supervised trial-based decoding setting.
-This tutorial will include additional parts of code like loading and preprocessing,
-defining a model, and other details which are not exclusive to this page (compare
-<a class="reference external" href="./plot_bcic_iv_2a_moabb_trial.html">Cropped Decoding Tutorial</a>). Therefore we
-will not further elaborate on these parts and you can feel free to skip them.</p>
-<p>In general we distinguish between “usual” training and evaluation and hyperparameter search.
-The tutorial is therefore split into two parts, one for the three different training schemes
-and one for the two different hyperparameter tuning methods.</p>
-<section id="why-should-i-care-about-model-evaluation">
-<h2>Why should I care about model evaluation?<a class="headerlink" href="#why-should-i-care-about-model-evaluation" title="Permalink to this heading">#</a></h2>
-<p>Short answer: To produce reliable results!</p>
-<p>In machine learning, we usually follow the scheme of splitting the
-data into two parts, training and testing sets. It sounds like a
-simple division, right? But the story does not end here.</p>
-<p>While developing a ML model you usually have to adjust and tune
-hyperparameters of your model or pipeline (e.g., number of layers,
-learning rate, number of epochs). Deep learning models usually have
-many free parameters; they could be considered complex models with
-many degrees of freedom. If you kept using the test dataset to
-evaluate your adjustmentyou would run into data leakage.</p>
-<p>This means that if you use the test set to adjust the hyperparameters
-of your model, the model implicitly learns or memorizes the test set.
-Therefore, the trained model is no longer independent of the test set
-(even though it was never used for training explicitly!).
-If you perform any hyperparameter tuning, you need a third split,
-the so-called validation set.</p>
-<p>This tutorial shows the three basic schemes for training and evaluating
-the model as well as two methods to tune your hyperparameters.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>You might recognize that the accuracy gets better throughout
-the experiments of this tutorial. The reason behind that is that
-we always use the same model with the same paramters in every
-segment to keep the tutorial short and readable. If you do your
-own experiments you always have to reinitialize the model before
-training.</p>
-</div>
-</section>
-<section id="loading-preprocessing-defining-a-model-etc">
-<h2>Loading, preprocessing, defining a model, etc.<a class="headerlink" href="#loading-preprocessing-defining-a-model-etc" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.0</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.0</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;pick_types&quot;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
-        <span class="k">lambda</span> <span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">factor</span><span class="p">),</span>  <span class="c1"># Convert from V to uV</span>
-        <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span>
-    <span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s2">&quot;filter&quot;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span>
-        <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-        <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">,</span>
-    <span class="p">),</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50b213ba90&gt;
-</pre></div>
-</div>
-</section>
-<section id="cut-compute-windows">
-<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s2">&quot;sfreq&quot;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
-<span class="c1"># trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-model">
-<h3>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-<span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="how-to-train-and-evaluate-your-model">
-<h2>How to train and evaluate your model<a class="headerlink" href="#how-to-train-and-evaluate-your-model" title="Permalink to this heading">#</a></h2>
-<section id="split-dataset-into-train-and-test">
-<h3>Split dataset into train and test<a class="headerlink" href="#split-dataset-into-train-and-test" title="Permalink to this heading">#</a></h3>
-<p>We can easily split the dataset using additional info stored in the
-description attribute, in this case the <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We
-select <code class="docutils literal notranslate"><span class="pre">session_T</span></code> for training and <code class="docutils literal notranslate"><span class="pre">session_E</span></code> for testing.
-For other datasets, you might have to choose another column.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>No matter which of the three schemes you use, this initial
-two-fold split into train_set and test_set always remains the same.
-Remember that you are not allowed to use the test_set during any
-stage of training or tuning.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s2">&quot;session_T&quot;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s2">&quot;session_E&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-<section id="option-1-simple-train-test-split">
-<h3>Option 1: Simple Train-Test Split<a class="headerlink" href="#option-1-simple-train-test-split" title="Permalink to this heading">#</a></h3>
-<p>This is the easiest training scheme to use as the dataset is only
-split into two distinct sets (<code class="docutils literal notranslate"><span class="pre">train_set</span></code> and <code class="docutils literal notranslate"><span class="pre">test_set</span></code>).
-This scheme uses no separate validation split and should only be
-used for the final evaluation of the (previously!) found
-hyperparameters configuration.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>If you make any use of the <code class="docutils literal notranslate"><span class="pre">test_set</span></code> during training
-(e.g. by using EarlyStopping) there will be data leakage
-which will make the reported generalization capability/decoding
-performance of your model less credible.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">0.0625</span> <span class="o">*</span> <span class="mf">0.01</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already supplied</span>
-<span class="c1"># in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-
-<span class="c1"># score the Model after training</span>
-<span class="n">y_test</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.2535        1.6382  0.0006  4.8794
-      2            0.2639        1.2563  0.0005  4.5596
-      3            0.2535        1.1591  0.0002  4.6292
-      4            0.2674        1.1075  0.0000  4.5205
-Test acc: 24.65%
-</pre></div>
-</div>
-</section>
-<section id="let-s-visualize-the-first-option-with-a-util-function">
-<h3>Let’s visualize the first option with a util function.<a class="headerlink" href="#let-s-visualize-the-first-option-with-a-util-function" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a>
-
-
-<span class="k">def</span> <span class="nf">plot_simple_train_test</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create a sample plot for training-testing split.&quot;&quot;&quot;</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">]</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
-        <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">)),</span>
-        <span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">),</span>
-        <span class="n">c</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-        <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
-        <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)),</span>
-        <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span>
-        <span class="n">c</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">)</span>
-        <span class="o">+</span> <span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-        <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span>
-        <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-        <span class="n">yticks</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">],</span>
-        <span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">Split&quot;</span><span class="p">,</span> <span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">Dataset&quot;</span><span class="p">],</span>
-        <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span>
-        <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Test Split&quot;</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span>
-        <span class="p">[</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]),</span>
-        <span class="p">],</span>
-        <span class="p">[</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
-        <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">ax</span>
-
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">plot_simple_train_test</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a>
-<span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_how_train_test_and_tune_001.png" srcset="../_images/sphx_glr_plot_how_train_test_and_tune_001.png" alt="Train-Test Split" class = "sphx-glr-single-img"/></section>
-<section id="option-2-train-val-test-split">
-<h3>Option 2: Train-Val-Test Split<a class="headerlink" href="#option-2-train-val-test-split" title="Permalink to this heading">#</a></h3>
-<p>When evaluating different settings hyperparameters for your model,
-there is still a risk of overfitting on the test set because the
-parameters can be tweaked until the estimator performs optimally.
-For more information visit <a class="reference external" href="https://scikit-learn.org/stable/modules/cross_validation.html">sklearns Cross-Validation Guide</a>.
-This second option splits the original <code class="docutils literal notranslate"><span class="pre">train_set</span></code> into two distinct
-sets, the training set and the validation set to avoid overfitting
-the hyperparameters to the test set.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If your dataset is really small, the validation split can become
-quite small. This may lead to unreliable tuning results. To
-avoid this, either use Option 3 or adjust the split ratio.</p>
-</div>
-<p>To split the <code class="docutils literal notranslate"><span class="pre">train_set</span></code> we will make use of the
-<code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument of <code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code>. If you leave this empty
-(not None!), skorch will make an 80-20 train-validation split.
-If you want to control the split manually you can do that by using
-<code class="docutils literal notranslate"><span class="pre">Subset</span></code> from torch and <code class="docutils literal notranslate"><span class="pre">predefined_split</span></code> from skorch.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
-
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">)</span>
-<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_indices</span></a><span class="p">)</span>
-<a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">Subset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_indices</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>The parameter <code class="docutils literal notranslate"><span class="pre">shuffle</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>. For time-series
-data this should always be the case as shuffling might take
-advantage of correlated samples, which would make the validation
-performance less meaningful.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">),</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-
-<span class="c1"># score the Model after training (optional)</span>
-<span class="n">y_test</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">test_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">()</span><span class="o">.</span><span class="n">target</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin.score" title="sklearn.base.ClassifierMixin.score" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y_test</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test acc: </span><span class="si">{</span><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">test_acc</span></a> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
--------  ----------------  ------------  ----------------  ------------  ------  ------
-      1            0.3478        1.1208            0.2931        3.9705  0.0006  4.0717
-      2            0.4130        0.9749            0.3103        3.3421  0.0005  3.7264
-      3            0.4130        0.9062            0.3103        2.8234  0.0002  3.7242
-      4            0.4174        0.8466            0.3103        2.3347  0.0000  3.6939
-Test acc: 30.21%
-</pre></div>
-</div>
-</section>
-<section id="let-s-visualize-the-second-option-with-a-util-function">
-<h3>Let’s visualize the second option with a util function.<a class="headerlink" href="#let-s-visualize-the-second-option-with-a-util-function" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_train_valid_test</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Create a sample plot for training, validation, testing.&quot;&quot;&quot;</span>
-
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a> <span class="o">=</span> <span class="p">[</span>
-        <span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span>
-        <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">,</span>
-    <span class="p">]</span>
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
-        <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">)),</span>
-        <span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">),</span>
-        <span class="n">c</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-        <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter" title="matplotlib.axes.Axes.scatter" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span></a><span class="p">(</span>
-        <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)),</span>
-        <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">)</span> <span class="o">+</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span>
-        <span class="n">c</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">)</span>
-        <span class="o">+</span> <span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">)</span>
-        <span class="o">+</span> <span class="p">[</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">3</span><span class="p">]]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span>
-        <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-        <span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set.html#matplotlib.axes.Axes.set" title="matplotlib.axes.Axes.set" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set</span></a><span class="p">(</span>
-        <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span>
-        <span class="n">yticks</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">],</span>
-        <span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Train-Test</span><span class="se">\n</span><span class="s2">Split&quot;</span><span class="p">,</span> <span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">Dataset&quot;</span><span class="p">],</span>
-        <span class="n">xlabel</span><span class="o">=</span><span class="s2">&quot;Number of samples.&quot;</span><span class="p">,</span>
-        <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Train-Validation-Test Split&quot;</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span>
-        <span class="p">[</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">3</span><span class="p">]),</span>
-        <span class="p">],</span>
-        <span class="p">[</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
-        <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span>
-    <span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">ax</span>
-
-
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<span class="n">plot_train_valid_test</span><span class="p">(</span>
-    <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
-    <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_subset</span></a><span class="p">,</span>
-    <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="o">=</span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Subset" title="torch.utils.data.Subset" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">val_subset</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_how_train_test_and_tune_002.png" srcset="../_images/sphx_glr_plot_how_train_test_and_tune_002.png" alt="Train-Validation-Test Split" class = "sphx-glr-single-img"/></section>
-<section id="option-3-k-fold-cross-validation">
-<h3>Option 3: k-Fold Cross Validation<a class="headerlink" href="#option-3-k-fold-cross-validation" title="Permalink to this heading">#</a></h3>
-<p>As mentioned above, using only one validation split might not be
-sufficient, as there might be a shift in the data distribution.
-To compensate for this, one can run a k-fold Cross Validation,
-where every sample of the training set is in the validation set once.
-After averaging over the k validation scores afterwards, you get a
-very reliable estimate of how the model would perform on unseen
-data (test set).</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This k-Fold Cross Validation can be used without a separate
-(holdout) test set. If there is no test set available, e.g. in a
-competition, this scheme is highly recommended to get a reliable
-estimate of the generalization performance.</p>
-</div>
-<p>To implement this, we will make use of sklearn function
-<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html">cross_val_score</a> and the <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">KFold</a>. CV splitter.
-The <code class="docutils literal notranslate"><span class="pre">train_split</span></code> argument has to be set to <code class="docutils literal notranslate"><span class="pre">None</span></code>, as sklearn
-will take care of the splitting.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">weight_decay</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s2">&quot;CosineAnnealingLR&quot;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;epochs&quot;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score" title="sklearn.model_selection.cross_val_score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">cross_val_score</span></a><span class="p">(</span>
-    <span class="n">clf</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Validation accuracy: </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean" title="numpy.mean" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span>
-    <span class="sa">f</span><span class="s2">&quot;+-</span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.std.html#numpy.std" title="numpy.std" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">std</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv_results</span></a> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%&quot;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4217        0.9510  0.0006  3.7767
-      2            0.4826        0.8390  0.0005  3.4044
-      3            0.5522        0.7605  0.0002  3.4105
-      4            0.6174        0.8015  0.0000  3.3865
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4435        0.9123  0.0006  3.3694
-      2            0.3696        0.8837  0.0005  3.4226
-      3            0.4391        0.8394  0.0002  3.3729
-      4            0.5087        0.8109  0.0000  3.4060
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.3870        0.9665  0.0006  3.4415
-      2            0.4913        0.9448  0.0005  3.4137
-      3            0.5913        0.7749  0.0002  3.4069
-      4            0.6609        0.7858  0.0000  3.3898
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4372        0.9792  0.0006  3.4271
-      2            0.4675        0.7940  0.0005  3.4213
-      3            0.5022        0.7197  0.0002  3.4006
-      4            0.5584        0.7871  0.0000  3.3757
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4632        0.9445  0.0006  3.4463
-      2            0.5584        0.8156  0.0005  3.4412
-      3            0.6277        0.6882  0.0002  3.4495
-      4            0.6926        0.7000  0.0000  3.4547
-Validation accuracy: 47.85+-11.10%
-</pre></div>
-</div>
-</section>
-<section id="let-s-visualize-the-third-option-with-a-util-function">
-<h3>Let’s visualize the third option with a util function.<a class="headerlink" href="#let-s-visualize-the-third-option-with-a-util-function" title="Permalink to this heading">#</a></h3>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">encode_color</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">br_cmap</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">):</span>
-    <span class="c1"># Util to encoder color</span>
-    <span class="k">if</span> <span class="n">value</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">br_cmap</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">br_cmap</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">plot_k_fold</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">):</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;#3A6190&quot;</span><span class="p">,</span> <span class="s2">&quot;#683E00&quot;</span><span class="p">,</span> <span class="s2">&quot;#2196F3&quot;</span><span class="p">,</span> <span class="s2">&quot;#DDF2FF&quot;</span><span class="p">]</span>
-
-    <span class="n">mosaic</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
-<span class="s2">      aa</span>
-<span class="s2">      BC</span>
-<span class="s2">      &quot;&quot;&quot;</span>
-
-    <span class="n">axes</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="matplotlib.pyplot.figure" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">constrained_layout</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">subplot_mosaic</span><span class="p">(</span>
-        <span class="n">mosaic</span><span class="p">,</span>
-        <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;height_ratios&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;width_ratios&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">3.5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">]},</span>
-    <span class="p">)</span>
-
-    <span class="c1"># Generate the training/testing visualizations for each CV split</span>
-    <span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr</span><span class="p">,</span> <span class="n">tt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)):</span>
-        <span class="c1"># Fill in indices with the training/test groups</span>
-
-        <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-            <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">)),</span>
-            <span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">),</span>
-            <span class="n">c</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-            <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-            <span class="n">lw</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="n">indices</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">))</span>
-        <span class="n">indices</span><span class="p">[</span><span class="n">tt</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">indices</span><span class="p">[</span><span class="n">tr</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="n">color_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="n">encode_color</span><span class="p">,</span> <span class="n">indices</span><span class="p">))</span>
-
-        <span class="c1"># Visualize the results</span>
-        <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-            <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)),</span>
-            <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">),</span>
-            <span class="n">c</span><span class="o">=</span><span class="n">color_indices</span><span class="p">,</span>
-            <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-            <span class="n">lw</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-            <span class="n">vmin</span><span class="o">=-</span><span class="mf">0.2</span><span class="p">,</span>
-            <span class="n">vmax</span><span class="o">=</span><span class="mf">1.2</span><span class="p">,</span>
-        <span class="p">)</span>
-
-        <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
-            <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">)),</span>
-            <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">),</span>
-            <span class="n">c</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-            <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;_&quot;</span><span class="p">,</span>
-            <span class="n">lw</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-        <span class="p">)</span>
-
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-        <span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">],</span>
-        <span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;Original</span><span class="se">\n</span><span class="s2">Data&quot;</span><span class="p">,</span>
-        <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="mf">3.4</span><span class="p">,</span> <span class="mf">3.6</span><span class="p">],</span>
-    <span class="p">)</span>
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
-
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;C&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-        <span class="n">yticks</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span> <span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span> <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span>
-    <span class="p">)</span>
-
-    <span class="c1"># Formatting</span>
-    <span class="n">yticklabels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set</span><span class="p">(</span>
-        <span class="n">yticks</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">+</span> <span class="mf">0.5</span><span class="p">,</span>
-        <span class="n">yticklabels</span><span class="o">=</span><span class="n">yticklabels</span><span class="p">,</span>
-        <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;CV iteration&quot;</span><span class="p">,</span>
-        <span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.2</span><span class="p">],</span>
-        <span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">300</span><span class="p">],</span>
-    <span class="p">)</span>
-
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;B&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">get_label</span><span class="p">()</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
-
-    <span class="n">axes</span><span class="p">[</span><span class="s2">&quot;a&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Training, testing with k-Fold Cross Validation&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
-
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html#matplotlib.pyplot.legend" title="matplotlib.pyplot.legend" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span>
-        <span class="p">[</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">2</span><span class="p">]),</span>
-            <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="matplotlib.patches.Patch" class="sphx-glr-backref-module-matplotlib-patches sphx-glr-backref-type-py-class"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">braindecode_cmap</span></a><span class="p">[</span><span class="mi">3</span><span class="p">]),</span>
-        <span class="p">],</span>
-        <span class="p">[</span><span class="s2">&quot;Original set&quot;</span><span class="p">,</span> <span class="s2">&quot;Training set&quot;</span><span class="p">,</span> <span class="s2">&quot;Validation set&quot;</span><span class="p">,</span> <span class="s2">&quot;Testing set&quot;</span><span class="p">],</span>
-        <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
-    <span class="p">)</span>
-    <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="matplotlib.pyplot.subplots_adjust" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.075</span><span class="p">)</span>
-
-
-<span class="n">plot_k_fold</span><span class="p">(</span>
-    <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span>
-    <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">,</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_how_train_test_and_tune_003.png" srcset="../_images/sphx_glr_plot_how_train_test_and_tune_003.png" alt="Training, testing with k-Fold Cross Validation" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/examples/plot_how_train_test_and_tune.py:553: UserWarning: FixedFormatter should only be used together with FixedLocator
-  ylim=[3.4, 3.6],
-/home/runner/work/braindecode/braindecode/examples/plot_how_train_test_and_tune.py:586: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.
-  plt.subplots_adjust(wspace=0.075)
-</pre></div>
-</div>
-</section>
-</section>
-<section id="how-to-tune-your-hyperparameters">
-<h2>How to tune your hyperparameters<a class="headerlink" href="#how-to-tune-your-hyperparameters" title="Permalink to this heading">#</a></h2>
-<p>One way to do hyperparameter tuning is to run each configuration
-manually (via Option 2 or 3 from above) and compare the validation
-performance afterwards. In the early stages of your developement
-process this might be sufficient to get a rough understanding of
-how your hyperparameter should look like for your model to converge.
-However, this manual tuning process quickly becomes messy as the
-number of hyperparameters you want to (jointly) tune increases.
-Therefore you sould automate this process. We will present two
-different options, analogous to Option 2 and 3 from above.</p>
-<section id="option-1-train-val-test-split">
-<h3>Option 1: Train-Val-Test Split<a class="headerlink" href="#option-1-train-val-test-split" title="Permalink to this heading">#</a></h3>
-<p>We will again make use of the sklearn library to do the hyperparameter
-search. <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a> will perform
-a Grid Search over the parameters specified in <code class="docutils literal notranslate"><span class="pre">param_grid</span></code>.
-We use grid search as a simple example, but you can use <a class="reference external" href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection">any strategy
-you want</a>).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <span class="p">[</span>
-    <span class="nb">tuple</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span><span class="o">.</span><span class="n">indices_</span></a><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
-<span class="p">]</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">&quot;optimizer__lr&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.000625</span><span class="p">],</span>
-<span class="p">}</span>
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
-    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
-    <span class="n">cv</span><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a><span class="p">,</span>
-    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">error_score</span><span class="o">=</span><span class="s2">&quot;raise&quot;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">X_train</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
-<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
-
-<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s2">&quot;rank_test_score&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span>
-    <span class="sa">f</span><span class="s2">&quot;Best hyperparameters were </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> which gave a validation &quot;</span>
-    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">% (training &quot;</span>
-    <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%).&quot;</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 1 folds for each of 2 candidates, totalling 2 fits
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.3391        1.4049  0.0063  3.5999
-      2            0.3174        1.2415  0.0047  3.4335
-      3            0.3696        0.8894  0.0016  3.4155
-      4            0.5087        0.7424  0.0000  3.4152
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4478        0.8664  0.0006  3.4174
-      2            0.4522        0.7537  0.0005  3.4402
-      3            0.4913        0.7082  0.0002  3.4446
-      4            0.5478        0.7106  0.0000  3.4149
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4271        0.9627  0.0006  4.5512
-      2            0.5174        0.8300  0.0005  4.5411
-      3            0.6076        0.7954  0.0002  4.5435
-      4            0.6806        0.7512  0.0000  4.5650
-Best hyperparameters were {&#39;optimizer__lr&#39;: 0.000625} which gave a validation accuracy of 43.10% (training accuracy of 54.78%).
-</pre></div>
-</div>
-</section>
-<section id="option-2-k-fold-cross-validation">
-<h3>Option 2: k-Fold Cross Validation<a class="headerlink" href="#option-2-k-fold-cross-validation" title="Permalink to this heading">#</a></h3>
-<p>To perform a full k-Fold CV just replace <code class="docutils literal notranslate"><span class="pre">train_val_split</span></code> from
-above with the <code class="docutils literal notranslate"><span class="pre">KFold</span></code> cross-validator from sklearn.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_val_split</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-</pre></div>
-</div>
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#split-dataset-into-train-and-test">
-     Split dataset into train and test
-    </a>
-   </li>
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-    <a class="reference internal nav-link" href="#option-1-simple-train-test-split">
-     Option 1: Simple Train-Test Split
-    </a>
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-    <a class="reference internal nav-link" href="#let-s-visualize-the-first-option-with-a-util-function">
-     Let’s visualize the first option with a util function.
-    </a>
-   </li>
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-    <a class="reference internal nav-link" href="#option-2-train-val-test-split">
-     Option 2: Train-Val-Test Split
-    </a>
-   </li>
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-    <a class="reference internal nav-link" href="#let-s-visualize-the-second-option-with-a-util-function">
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="hyperparameter-tuning-with-scikit-learn">
-<span id="sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"></span><h1>Hyperparameter tuning with scikit-learn<a class="headerlink" href="#hyperparameter-tuning-with-scikit-learn" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows you how to tune hyperparameters with scikit-learn
-(GridSearchCV) in the setting of trialwise decoding on dataset
-BCIC IV 2a.</p>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<p>First, we load the data. In this tutorial, we use the functionality of
-braindecode to load datasets through
-<a class="reference external" href="https://github.com/NeuroTechX/moabb">MOABB</a> to load the BCI
-Competition IV 2a data.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To load your own datasets either via mne or from
-preprocessed X/y numpy arrays, see <a class="reference external" href="./plot_mne_dataset_example.html">MNE Dataset
-Tutorial</a> and <a class="reference external" href="./plot_custom_dataset_example.html">Numpy Dataset
-Tutorial</a>.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.moabb</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">])</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<p>Now we apply preprocessing like bandpass filtering to our dataset. You
-can either apply functions provided by
-<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html">mne.Raw</a> or
-<a class="reference external" href="https://mne.tools/0.11/generated/mne.Epochs.html#mne.Epochs">mne.Epochs</a>
-or apply your own functions, either to the MNE object or the underlying
-numpy array.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>These prepocessings are now directly applied to the loaded
-data, and not on-the-fly applied as transformations in
-PyTorch-libraries like
-<a class="reference external" href="https://pytorch.org/docs/stable/torchvision/index.html">torchvision</a>.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.preprocess</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">)</span>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a> <span class="o">=</span> <span class="mf">4.</span>  <span class="c1"># low cut frequency for filtering</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mf">38.</span>  <span class="c1"># high cut frequency for filtering</span>
-<span class="c1"># Parameters for exponential moving standardization</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_types&#39;</span><span class="p">,</span> <span class="n">eeg</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">meg</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stim</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>  <span class="c1"># Keep EEG sensors</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">low_cut_hz</span></a><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">),</span>  <span class="c1"># Bandpass filter</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">exponential_moving_standardize</span></a><span class="p">,</span>  <span class="c1"># Exponential moving standardization</span>
-                 <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor_new</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">init_block_size</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50abef8f10&gt;
-</pre></div>
-</div>
-</section>
-<section id="cut-compute-windows">
-<h3>Cut Compute Windows<a class="headerlink" href="#cut-compute-windows" title="Permalink to this heading">#</a></h3>
-<p>Now we cut out compute windows, the inputs for the deep networks during
-training. In the case of trialwise decoding, we just have to decide if
-we want to cut out some part before and/or after the trial. For this
-dataset, in our work, it often was beneficial to also cut out 500 ms
-before the trial.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing.windowers</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">=</span> <span class="o">-</span><span class="mf">0.5</span>
-<span class="c1"># Extract sampling frequency, check that they are same in all datasets</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-<span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">ds</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">==</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="k">for</span> <span class="n">ds</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">])</span>
-<span class="c1"># Calculate the trial start offset in samples.</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_seconds</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">)</span>
-
-<span class="c1"># Create windows using braindecode function for this. It needs parameters to define how</span>
-<span class="c1"># trials should be used.</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">trial_start_offset_samples</span></a><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>We can easily split the dataset using additional info stored in the
-description attribute, in this case <code class="docutils literal notranslate"><span class="pre">session</span></code> column. We select
-<code class="docutils literal notranslate"><span class="pre">session_T</span></code> for training and <code class="docutils literal notranslate"><span class="pre">session_E</span></code> for evaluation.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s1">&#39;session&#39;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_T&#39;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splitted</span></a><span class="p">[</span><span class="s1">&#39;session_E&#39;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
-<p>Now we create the deep learning model! Braindecode comes with some
-predefined convolutional neural network architectures for raw
-time-domain EEG. Here, we use the shallow ConvNet model from <a class="reference external" href="https://arxiv.org/abs/1703.05051">Deep
-learning with convolutional neural networks for EEG decoding and
-visualization</a>. These models are
-pure <a class="reference external" href="https://pytorch.org">PyTorch</a> deep learning models, therefore
-to use your own model, it just has to be a normal PyTorch
-<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module">nn.Module</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available, if True chooses to use it</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>  <span class="c1"># random seed to make results reproducible</span>
-<span class="c1"># Set random seed to be able to reproduce results</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">4</span>
-<span class="c1"># Extract number of chans and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-<a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">final_conv_length</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
-<p>Now we train the network! EEGClassifier is a Braindecode object
-responsible for managing the training of neural networks. It inherits
-from skorch.NeuralNetClassifier, so the training logic is the same as in
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">16</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">4</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">NLLLoss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><span class="p">[],</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>  <span class="c1"># train /test split is handled by GridSearchCV</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>Use scikit-learn GridSearchCV to tune hyperparameters. To be able
-to do this, we slice the braindecode datasets that by default return
-a 3-tuple to return X and y, respectively.</p>
-<p><strong>Note</strong>: The KFold object splits the datasets based on their
-length which corresponds to the number of compute windows. In
-this (trialwise) example this is fine to do. In a cropped setting
-this is not advisable since this might split compute windows
-of a single trial into both train and valid set.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_X</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">array</span></a><span class="p">([</span><span class="n">y</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)])</span>
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">}</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;optimizer__lr&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.00625</span><span class="p">,</span> <span class="mf">0.000625</span><span class="p">,</span> <span class="mf">0.0000625</span><span class="p">],</span>
-<span class="p">}</span>
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class"><span class="n">GridSearchCV</span></a><span class="p">(</span>
-    <span class="n">estimator</span><span class="o">=</span><span class="n">clf</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">param_grid</span></a><span class="p">,</span>
-    <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="o">=</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cv</span></a><span class="p">,</span>
-    <span class="n">return_train_score</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;accuracy&#39;</span><span class="p">,</span>
-    <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="n">error_score</span><span class="o">=</span><span class="s1">&#39;raise&#39;</span>
-<span class="p">)</span>
-
-<a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.fit" title="sklearn.model_selection.GridSearchCV.fit" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_X</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_y</span></a><span class="p">,</span> <span class="o">**</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fit_params</span></a><span class="p">)</span>
-
-<span class="n">search_results</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">search</span><span class="o">.</span><span class="n">cv_results_</span></a><span class="p">)</span>
-
-<span class="n">best_run</span> <span class="o">=</span> <span class="n">search_results</span><span class="p">[</span><span class="n">search_results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Best hyperparameters were </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">]</span><span class="si">}</span><span class="s2"> which gave a validation &quot;</span>
-      <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">% (training &quot;</span>
-      <span class="sa">f</span><span class="s2">&quot;accuracy of </span><span class="si">{</span><span class="n">best_run</span><span class="p">[</span><span class="s1">&#39;mean_train_score&#39;</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%).&quot;</span><span class="p">)</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_X</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_y</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class"><span class="n">SliceDataset</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_set</span></a><span class="p">,</span> <span class="n">idx</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-<a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a> <span class="o">=</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV.score" title="sklearn.model_selection.GridSearchCV.score" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-method"><span class="n">search</span><span class="o">.</span><span class="n">score</span></a><span class="p">(</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_X</span></a><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.SliceDataset" title="skorch.helper.SliceDataset" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">eval_y</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Eval accuracy is </span><span class="si">{</span><a href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.float64" title="numpy.float64" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-attribute"><span class="n">score</span></a><span class="o">*</span><span class="mi">100</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">%.&quot;</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Fitting 2 folds for each of 3 candidates, totalling 6 fits
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.2569        2.0037  0.0063  2.6691
-      2            0.5417        1.0258  0.0047  2.5314
-      3            0.7014        0.6431  0.0016  2.5267
-      4            0.8472        0.4717  0.0000  2.5397
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.2986        2.0675  0.0063  2.5585
-      2            0.4167        1.2638  0.0047  2.5640
-      3            0.6736        0.7800  0.0016  2.5501
-      4            0.8056        0.6616  0.0000  2.5536
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.3056        1.6027  0.0006  2.5427
-      2            0.2708        1.1093  0.0005  2.5469
-      3            0.3681        0.9526  0.0002  2.5474
-      4            0.5347        0.8761  0.0000  2.5321
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.3125        1.5974  0.0006  2.5371
-      2            0.2917        1.0369  0.0005  2.5315
-      3            0.3333        0.9243  0.0002  2.5335
-      4            0.5625        0.8559  0.0000  2.5437
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.2361        1.7544  0.0001  2.5351
-      2            0.2361        1.5565  0.0000  2.5283
-      3            0.2708        1.5915  0.0000  2.5270
-      4            0.3194        1.5404  0.0000  2.5306
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.2153        1.7057  0.0001  2.5316
-      2            0.2847        1.5504  0.0000  2.5386
-      3            0.2986        1.6305  0.0000  2.5456
-      4            0.3125        1.6165  0.0000  2.5542
-  epoch    train_accuracy    train_loss      lr     dur
--------  ----------------  ------------  ------  ------
-      1            0.4896        2.2402  0.0063  5.1275
-      2            0.5556        1.2775  0.0047  5.1263
-      3            0.8299        0.8836  0.0016  5.1178
-      4            0.8472        0.6766  0.0000  5.1206
-Best hyperparameters were {&#39;optimizer__lr&#39;: 0.00625} which gave a validation accuracy of 39.24% (training accuracy of 82.64%).
-Eval accuracy is 50.69%.
-</pre></div>
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-<li class="toctree-l1"><a class="reference internal" href="plot_split_dataset.html">Split Dataset Example</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="load-and-save-dataset-example">
-<span id="sphx-glr-auto-examples-plot-load-save-datasets-py"></span><h1>Load and save dataset example<a class="headerlink" href="#load-and-save-dataset-example" title="Permalink to this heading">#</a></h1>
-<p>In this example, we show how to load and save braindecode datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">tempfile</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datautil</span> <span class="kn">import</span> <a href="../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-</pre></div>
-</div>
-<p>First, we load some dataset using MOABB.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span>
-    <span class="n">dataset_name</span><span class="o">=</span><span class="s1">&#39;BNCI2014001&#39;</span><span class="p">,</span>
-    <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-</pre></div>
-</div>
-<p>We can apply preprocessing steps to the dataset. It is also possible to skip
-this step and not apply any preprocessing.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
-    <span class="n">concat_ds</span><span class="o">=</span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <span class="n">preprocessors</span><span class="o">=</span><span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">fn</span><span class="o">=</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <span class="n">sfreq</span><span class="o">=</span><span class="mi">10</span><span class="p">)]</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f50b21bca50&gt;
-</pre></div>
-</div>
-<p>We save the dataset to a an existing directory. It will create a ‘.fif’ file
-for every dataset in the concat dataset. Additionally it will create two
-JSON files, the first holding the description of the dataset, the second
-holding the name of the target. If you want to store to the same directory
-several times, for example due to trying different preprocessing, you can
-choose to overwrite the existing files.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># write in a temporary directory</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
-    <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Writing /tmp/tmp_73cii3t/0/0-raw.fif
-Closing /tmp/tmp_73cii3t/0/0-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/1/1-raw.fif
-Closing /tmp/tmp_73cii3t/1/1-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/2/2-raw.fif
-Closing /tmp/tmp_73cii3t/2/2-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/3/3-raw.fif
-Closing /tmp/tmp_73cii3t/3/3-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/4/4-raw.fif
-Closing /tmp/tmp_73cii3t/4/4-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/5/5-raw.fif
-Closing /tmp/tmp_73cii3t/5/5-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/6/6-raw.fif
-Closing /tmp/tmp_73cii3t/6/6-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/7/7-raw.fif
-Closing /tmp/tmp_73cii3t/7/7-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/8/8-raw.fif
-Closing /tmp/tmp_73cii3t/8/8-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/9/9-raw.fif
-Closing /tmp/tmp_73cii3t/9/9-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/10/10-raw.fif
-Closing /tmp/tmp_73cii3t/10/10-raw.fif
-[done]
-Writing /tmp/tmp_73cii3t/11/11-raw.fif
-Closing /tmp/tmp_73cii3t/11/11-raw.fif
-[done]
-</pre></div>
-</div>
-<p>We load the saved dataset from a directory. Signals can be preloaded in
-compliance with mne. Optionally, only specific ‘.fif’ files can be loaded
-by specifying their ids. The target name can be changed, if the dataset
-supports it (TUHAbnormal for example supports ‘pathological’, ‘age’, and
-‘gender’. If you stored a preprocessed version with target ‘pathological’
-it is possible to change the target upon loading).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a> <span class="o">=</span> <a href="../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">ids_to_load</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
-    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Opening raw data file /tmp/tmp_73cii3t/1/1-raw.fif...
-    Range : 0 ... 3868 =      0.000 ...   386.800 secs
-Ready.
-Reading 0 ... 3868  =      0.000 ...   386.800 secs...
-Opening raw data file /tmp/tmp_73cii3t/3/3-raw.fif...
-    Range : 0 ... 3868 =      0.000 ...   386.800 secs
-Ready.
-Reading 0 ... 3868  =      0.000 ...   386.800 secs...
-</pre></div>
-</div>
-<p>The serialization utility also supports WindowsDatasets, so we create
-compute windows next.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <span class="n">concat_ds</span><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset_loaded</span></a><span class="p">,</span>
-    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 40 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 40 original time points ...
-0 bad epochs dropped
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_3</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>Again, we save the dataset to an existing directory. It will create a
-‘-epo.fif’ file for every dataset in the concat dataset. Additionally it
-will create a JSON file holding the description of the dataset. If you
-want to store to the same directory several times, for example due to
-trying different windowing parameters, you can choose to overwrite the
-existing files.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save" title="braindecode.datasets.BaseConcatDataset.save" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">save</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
-    <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Using data from preloaded Raw for 1 events and 40 original time points ...
-Using data from preloaded Raw for 48 events and 40 original time points ...
-Using data from preloaded Raw for 1 events and 40 original time points ...
-Using data from preloaded Raw for 48 events and 40 original time points ...
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:570: UserWarning: The number of saved datasets (2) does not match the number of existing subdirectories (12). You may now encounter a mix of differently preprocessed datasets!
-  f&quot;datasets!&quot;, UserWarning)
-/home/runner/work/braindecode/braindecode/braindecode/datasets/base.py:574: UserWarning: Chosen directory /tmp/tmp_73cii3t contains other subdirectories or files [&#39;5&#39;, &#39;11&#39;, &#39;3&#39;, &#39;7&#39;, &#39;2&#39;, &#39;10&#39;, &#39;4&#39;, &#39;9&#39;, &#39;8&#39;, &#39;6&#39;].
-  warnings.warn(f&#39;Chosen directory {path} contains other &#39;
-</pre></div>
-</div>
-<p>Load the saved dataset from a directory. Signals can be preloaded in
-compliance with mne. Optionally, only specific ‘-epo.fif’ files can be
-loaded by specifying their ids.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a> <span class="o">=</span> <a href="../generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset" title="braindecode.datautil.load_concat_dataset" class="sphx-glr-backref-module-braindecode-datautil sphx-glr-backref-type-py-function"><span class="n">load_concat_dataset</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmpdir</span></a><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">ids_to_load</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset_loaded</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Reading /tmp/tmp_73cii3t/0/0-epo.fif ...
-    Found the data of interest:
-        t =       0.00 ...    3900.00 ms
-        0 CTF compensation matrices available
-Adding metadata with 4 columns
-48 matching events found
-No baseline correction applied
-0 projection items activated
-</pre></div>
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-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  4.616 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  407 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-load-save-datasets-py">
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-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/4855a497085ac8dc60a648bf4a72d647/plot_load_save_datasets.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_load_save_datasets.ipynb</span></code></a></p>
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diff --git a/stable/auto_examples/plot_mne_dataset_example.html b/stable/auto_examples/plot_mne_dataset_example.html
deleted file mode 100644
index 7a21e230..00000000
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-        
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-          <div class="bd-article-container">
-            
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-                
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-            
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-            <article class="bd-article" role="main">
-              
-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="mne-dataset-example">
-<span id="sphx-glr-auto-examples-plot-mne-dataset-example-py"></span><h1>MNE Dataset Example<a class="headerlink" href="#mne-dataset-example" title="Permalink to this heading">#</a></h1>
-<p>This example shows how to convert data from mne.Raws or mne.Epochs to a
-braindecode compatible data format.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>First, fetch some data using mne:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 5, 6, 7, 10, 13, 14 are codes for executed and imagined hands/feet</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a> <span class="o">=</span> <span class="mi">22</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">14</span><span class="p">]</span>
-<span class="c1"># event_codes = [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</span>
-
-<span class="c1"># This will download the files if you don&#39;t have them yet,</span>
-<span class="c1"># and then return the paths to the files.</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a> <span class="o">=</span> <a href="https://mne.tools/stable/generated/mne.datasets.eegbci.load_data.html#mne.datasets.eegbci.load_data" title="mne.datasets.eegbci.load_data" class="sphx-glr-backref-module-mne-datasets-eegbci sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">eegbci</span><span class="o">.</span><span class="n">load_data</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">,</span> <span class="n">update_path</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-<span class="c1"># Load each of the files</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.io.read_raw_edf.html#mne.io.read_raw_edf" title="mne.io.read_raw_edf" class="sphx-glr-backref-module-mne-io sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">read_raw_edf</span></a><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">stim_channel</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">)</span>
-         <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">physionet_paths</span></a><span class="p">]</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R05.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R06.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R09.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R10.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R13.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-Extracting EDF parameters from /home/runner/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S022/S022R14.edf...
-EDF file detected
-Setting channel info structure...
-Creating raw.info structure...
-Reading 0 ... 19999  =      0.000 ...   124.994 secs...
-</pre></div>
-</div>
-<p>Convert Raw objects to a compatible data format:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;event_code&quot;</span><span class="p">:</span> <span class="n">code</span><span class="p">,</span> <span class="s2">&quot;subject&quot;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_id</span></a><span class="p">}</span>
-                <span class="k">for</span> <span class="n">code</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">event_codes</span></a><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.create_from_mne_raw.html#braindecode.datasets.create_from_mne_raw" title="braindecode.datasets.create_from_mne_raw" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_raw</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">,</span>
-    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">descriptions</span></a><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;T0&#39;, &#39;T1&#39;, &#39;T2&#39;]
-Using data from preloaded Raw for 60 events and 500 original time points ...
-0 bad epochs dropped
-</pre></div>
-</div>
-<p>If trials were already cut beforehand and are available as mne.Epochs:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a> <span class="o">=</span> <span class="p">[</span><a href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="mne.Epochs" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-class"><span class="n">mne</span><span class="o">.</span><span class="n">Epochs</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">baseline</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-                  <span class="k">for</span> <span class="n">raw</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">parts</span></a><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.create_from_mne_epochs.html#braindecode.datasets.create_from_mne_epochs" title="braindecode.datasets.create_from_mne_epochs" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-function"><span class="n">create_from_mne_epochs</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">list_of_epochs</span></a><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">)</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Not setting metadata
-1 matching events found
-No baseline correction applied
-0 projection items activated
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-Creating RawArray with float64 data, n_channels=64, n_times=81
-    Range : 0 ... 80 =      0.000 ...     0.500 secs
-Ready.
-Adding metadata with 4 columns
-2 matching events found
-No baseline correction applied
-0 projection items activated
-Using data from preloaded Raw for 2 events and 50 original time points ...
-0 bad epochs dropped
-</pre></div>
-</div>
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-      <th>5</th>
-      <td>None</td>
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-</table>
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-<br />
-<br /><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.910 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  79 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-mne-dataset-example-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/865f9cabb97769a6672829cdd4848751/plot_mne_dataset_example.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mne_dataset_example.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/e42b5bd97033be02521040b0d5ee21c3/plot_mne_dataset_example.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_mne_dataset_example.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-regression-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
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-<section class="sphx-glr-example-title" id="regression-example-on-fake-data">
-<span id="sphx-glr-auto-examples-plot-regression-py"></span><h1>Regression example on fake data<a class="headerlink" href="#regression-example-on-fake-data" title="Permalink to this heading">#</a></h1>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/windowers.py:603: UserWarning: Meaning of `trial_stop_offset_samples`=0 has changed, use `None` to indicate end of trial/recording. Using `None`.
-  &#39;Meaning of `trial_stop_offset_samples`=0 has changed, use `None` &#39;
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/skorch/callbacks/base.py:146: SkorchWarning: You are using an callback that overrides on_batch_begin or on_batch_end. As of skorch 0.10, the signature was changed from &#39;on_batch_{begin,end}(self, X, y, ...)&#39; to &#39;on_batch_{begin,end}(self, batch, ...)&#39;. To recover, change the signature accordingly and add &#39;X, y = batch&#39; on the first line of the method body. To suppress this warning, add:
-&#39;import warnings; from skorch.exceptions import SkorchWarning
-warnings.filterwarnings(&#39;ignore&#39;, category=SkorchWarning)&#39;.
-  warnings.warn(warning_msg, SkorchWarning)
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
-  &quot;https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate&quot;, UserWarning)
-  epoch    train_neg_root_mean_squared_error    valid_loss    valid_neg_root_mean_squared_error      lr     dur
--------  -----------------------------------  ------------  -----------------------------------  ------  ------
-      1                             -30.8722      103.8379                             -10.1901  0.0006  0.1510
-      2                             -30.8722      103.8379                             -10.1901  0.0003  0.1266
-      3                             -30.8722      103.8379                             -10.1901  0.0000  0.0546
-</pre></div>
-</div>
-<div class="line-block">
-<div class="line"><br /></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD-3</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a>
-<span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.training.losses</span> <span class="kn">import</span> <a href="../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models.util</span> <span class="kn">import</span> <span class="n">to_dense_prediction_model</span><span class="p">,</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">,</span> <span class="n">create_mne_dummy_raw</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">=</span> <span class="s2">&quot;shallow&quot;</span>  <span class="c1"># &#39;shallow&#39; or &#39;deep&#39;</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a> <span class="o">=</span> <span class="mi">20200220</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a> <span class="o">=</span> <span class="mi">6000</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">64</span>
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">=</span> <span class="mi">21</span>
-<span class="c1"># set to how many targets you want to regress (age -&gt; 1, [x, y, z] -&gt; 3)</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">1</span>
-
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">seed</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<span class="c1"># initialize a model, transform to dense and move to gpu</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">==</span> <span class="s2">&quot;shallow&quot;</span><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet" title="braindecode.models.ShallowFBCSPNet" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">ShallowFBCSPNet</span></a><span class="p">(</span>
-        <span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-        <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-        <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">40</span><span class="p">,</span>
-        <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">35</span><span class="p">,</span>
-    <span class="p">)</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a> <span class="o">=</span> <span class="mf">0.000625</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a> <span class="o">=</span> <span class="mi">0</span>
-<span class="k">elif</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a> <span class="o">==</span> <span class="s2">&quot;deep&quot;</span><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">Deep4Net</span></a><span class="p">(</span>
-        <span class="n">in_chans</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-        <span class="n">n_filters_time</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-        <span class="n">n_filters_spat</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
-        <span class="n">stride_before_pool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-        <span class="n">n_filters_2</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">*</span> <span class="mi">2</span><span class="p">),</span>
-        <span class="n">n_filters_3</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">)),</span>
-        <span class="n">n_filters_4</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="mf">3.0</span><span class="p">)),</span>
-        <span class="n">final_conv_length</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="p">)</span>
-    <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a> <span class="o">=</span> <span class="mf">0.01</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a> <span class="o">=</span> <span class="mf">0.0005</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model_name</span></a><span class="si">}</span><span class="s1"> unknown&#39;</span><span class="p">)</span>
-
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">()</span>
-<span class="k">for</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.named_children" title="torch.nn.Module.named_children" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">named_children</span></a><span class="p">():</span>
-    <span class="k">if</span> <span class="s2">&quot;softmax&quot;</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">:</span>
-        <span class="k">continue</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.add_module" title="torch.nn.Module.add_module" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">new_model</span><span class="o">.</span><span class="n">add_module</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">name</span></a><span class="p">,</span> <span class="n">module_</span><span class="p">)</span>
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">new_model</span></a>
-
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-
-<span class="n">to_dense_prediction_model</span><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape" title="braindecode.models.get_output_shape" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-function"><span class="n">get_output_shape</span></a><span class="p">(</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
-
-
-<span class="k">def</span> <span class="nf">fake_regression_dataset</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">,</span> <span class="n">n_fake_chs</span><span class="p">,</span> <span class="n">fake_sfreq</span><span class="p">,</span> <span class="n">fake_duration_s</span><span class="p">):</span>
-    <span class="n">datasets</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_fake_recs</span><span class="p">):</span>
-        <span class="n">train_or_eval</span> <span class="o">=</span> <span class="s2">&quot;eval&quot;</span> <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;train&quot;</span>
-        <span class="n">raw</span><span class="p">,</span> <span class="n">save_fname</span> <span class="o">=</span> <span class="n">create_mne_dummy_raw</span><span class="p">(</span>
-            <span class="n">n_channels</span><span class="o">=</span><span class="n">n_fake_chs</span><span class="p">,</span> <span class="n">n_times</span><span class="o">=</span><span class="n">fake_duration_s</span> <span class="o">*</span> <span class="n">fake_sfreq</span><span class="p">,</span>
-            <span class="n">sfreq</span><span class="o">=</span><span class="n">fake_sfreq</span><span class="p">,</span> <span class="n">savedir</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="n">target</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html#numpy.random.randint" title="numpy.random.randint" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
-        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">fake_descrition</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span>
-            <span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">target</span><span class="p">,</span> <span class="n">train_or_eval</span><span class="p">],</span>
-            <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;target&quot;</span><span class="p">,</span> <span class="s2">&quot;session&quot;</span><span class="p">])</span>
-        <span class="n">base_ds</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseDataset</span></a><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">fake_descrition</span><span class="p">,</span> <span class="n">target_name</span><span class="o">=</span><span class="s2">&quot;target&quot;</span><span class="p">)</span>
-        <span class="n">datasets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">base_ds</span><span class="p">)</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">BaseConcatDataset</span></a><span class="p">(</span><span class="n">datasets</span><span class="p">)</span>
-    <span class="k">return</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a>
-
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <span class="n">fake_regression_dataset</span><span class="p">(</span>
-    <span class="n">n_fake_recs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_fake_chs</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">fake_sfreq</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">fake_duration_s</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_window_samples</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_preds_per_input</span></a><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">drop_bad_windows</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;session&quot;</span><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;eval&quot;</span><span class="p">]</span>
-
-<span class="n">regressor</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor" title="skorch.regressor.NeuralNetRegressor" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-class"><span class="n">EEGRegressor</span></a><span class="p">(</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">cropped</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="../generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss" title="braindecode.training.CroppedLoss" class="sphx-glr-backref-module-braindecode-training sphx-glr-backref-type-py-class"><span class="n">CroppedLoss</span></a><span class="p">,</span>
-    <span class="n">criterion__loss_function</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss" class="sphx-glr-backref-module-torch-nn-functional sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">mse_loss</span></a><span class="p">,</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW" title="torch.optim.AdamW" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_lr</span></a><span class="p">,</span>
-    <span class="n">optimizer__weight_decay</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">optimizer_weight_decay</span></a><span class="p">,</span>
-    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">&quot;neg_root_mean_squared_error&quot;</span><span class="p">,</span>
-        <span class="c1"># seems n_epochs -1 leads to desired behavior of lr=0 after end of training?</span>
-        <span class="p">(</span><span class="s2">&quot;lr_scheduler&quot;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.LRScheduler" title="skorch.callbacks.LRScheduler" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">LRScheduler</span></a><span class="p">(</span><span class="s1">&#39;CosineAnnealingLR&#39;</span><span class="p">,</span> <span class="n">T_max</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">-</span> <span class="mi">1</span><span class="p">)),</span>
-    <span class="p">],</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/regressor.html#skorch.regressor.NeuralNetRegressor.fit" title="skorch.regressor.NeuralNetRegressor.fit" class="sphx-glr-backref-module-skorch-regressor sphx-glr-backref-type-py-method"><span class="n">regressor</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.504 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  20 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-regression-py">
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-<p><a class="reference download internal" download="" href="../_downloads/d8042a52b5ef94f5d88f95acaeb05755/plot_regression.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_regression.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/9c64c2c15907a661b1fe2d7dd209a434/plot_regression.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_regression.ipynb</span></code></a></p>
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-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network">
-<span id="sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Chambon2018 network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows how to train and test a sleep staging neural network with
-Braindecode. We adapt the time distributed approach of <a class="footnote-reference brackets" href="#id4" id="id1">1</a> to learn on
-sequences of EEG windows using the openly accessible Sleep Physionet dataset
-<a class="footnote-reference brackets" href="#id5" id="id2">2</a> <a class="footnote-reference brackets" href="#id6" id="id3">3</a>.</p>
-<section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id4"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id7">2</a>,<a href="#id8">3</a>,<a href="#id9">4</a>,<a href="#id10">5</a>,<a href="#id11">6</a>,<a href="#id12">7</a>)</span></dt>
-<dd><p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
-(2018)A Deep Learning Architecture for Temporal Sleep Stage
-Classification Using Multivariate and Multimodal Time Series.
-IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
-(758-769)</p>
-</dd>
-<dt class="label" id="id5"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-a sleep-dependent neuronal feedback loop: the slow-wave
-microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
-</dd>
-<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
-<dd><p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-Research Resource for Complex Physiologic Signals.
-Circulation 101(23):e215-e220</p>
-</dd>
-</dl>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Hubert Banville &lt;hubert.jbanville@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-</pre></div>
-</div>
-</section>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<p>First, we load the data using the
-<a class="reference internal" href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
-two recordings from two different individuals: we will use the first one to
-train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
-sleep staging example).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
-a lowpass filter. We omit the downsampling step of <a class="footnote-reference brackets" href="#id4" id="id7">1</a> as the Sleep
-Physionet data is already sampled at a lower 100 Hz.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function scale is deprecated; will be removed in 0.8.0. Use numpy.multiply inside a lambda function instead.
-  warnings.warn(msg, category=FutureWarning)
-
-&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f50b1f6ff10&gt;
-</pre></div>
-</div>
-</section>
-<section id="extract-windows">
-<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Permalink to this heading">#</a></h3>
-<p>We extract 30-s windows to be used in the classification task.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
-    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
-<span class="p">}</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="window-preprocessing">
-<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Permalink to this heading">#</a></h3>
-<p>We also preprocess the windows by applying channel-wise z-score normalization
-in each window.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">scale</span></a> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
-
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50a90784d0&gt;
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>We split the dataset into training and validation set taking
-every other subject as train or valid.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="create-sequence-samplers">
-<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Permalink to this heading">#</a></h2>
-<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id4" id="id8">1</a>, we need to provide our
-neural network with sequences of windows, such that the embeddings of
-multiple consecutive windows can be concatenated and provided to a final
-classifier. We can achieve this by defining Sampler objects that return
-sequences of window indices.
-To simplify the example, we train the whole model end-to-end on sequences,
-rather than using the two-step approach of <a class="footnote-reference brackets" href="#id4" id="id9">1</a> (i.e. training the feature
-extractor on single windows, then freezing its weights and training the
-classifier).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
-
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-
-<span class="c1"># Print number of examples per class</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  372
-Validation examples:  383
-</pre></div>
-</div>
-<p>We also implement a transform to extract the label of the center window of a
-sequence to use it as target.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Use label of center window in the sequence</span>
-<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
-        <span class="k">return</span> <span class="n">x</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
-
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
-</pre></div>
-</div>
-<p>Finally, since some sleep stages appear a lot more often than others (e.g.
-most of the night is spent in the N2 stage), the classes are imbalanced. To
-avoid overfitting on the more frequent classes, we compute weights that we
-will provide to the loss function when training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
-<p>We can now create the deep learning model. In this tutorial, we use the sleep
-staging architecture introduced in <a class="footnote-reference brackets" href="#id4" id="id10">1</a>, which is a four-layer convolutional
-neural network. We use the time distributed version of the model, where the
-feature vectors of a sequence of windows are concatenated and passed to a
-linear layer for classification.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">,</span> <a href="../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
-<span class="c1"># Extract number of channels and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-
-<a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerChambon2018</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <span class="n">input_size_s</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span>
-<span class="p">)</span>
-
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../generated/braindecode.models.SleepStagerChambon2018.html#braindecode.models.SleepStagerChambon2018" title="braindecode.models.SleepStagerChambon2018" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
-<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
-braindecode object that is responsible for managing the training of neural
-networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
-training logic is the same as in
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id4" id="id11">1</a>, as these hyperparameters were
-optimized on a different dataset (MASS SS3) and with a different number of
-recordings. Generally speaking, it is recommended to perform
-hyperparameter optimization if reusing this code on a different dataset or
-with more recordings.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">10</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
-    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
-    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
-    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
-    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
-<span class="c1"># supplied in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_bal_acc    valid_loss     dur
--------  ---------------  ------------  ---------------  ------------  ------
-      1           0.1084        1.6514           0.2113        1.6141  2.0795
-      2           0.2583        1.6061           0.2476        1.6083  1.8426
-      3           0.2252        1.5895           0.2000        1.6149  1.8180
-      4           0.2016        1.5501           0.2000        1.6202  1.8267
-      5           0.2118        1.4884           0.2000        1.6860  1.8134
-      6           0.2134        1.4005           0.2000        1.7568  1.8013
-      7           0.2263        1.3324           0.2000        1.8240  1.8210
-      8           0.2920        1.2849           0.2000        1.9320  1.8259
-      9           0.3287        1.2328           0.2396        1.9407  1.8407
-     10           0.4305        1.1433           0.3595        1.7398  1.8753
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=Sequential(
-    (0): TimeDistributed(
-      (module): SleepStagerChambon2018(
-        (spatial_conv): Conv2d(1, 2, kernel_size=(2, 1), stride=(1, 1))
-        (feature_extractor): Sequential(
-          (0): Conv2d(1, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
-          (1): Identity()
-          (2): ReLU()
-          (3): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
-          (4): Conv2d(8, 8, kernel_size=(1, 50), stride=(1, 1), padding=(0, 25))
-          (5): Identity()
-          (6): ReLU()
-          (7): MaxPool2d(kernel_size=(1, 13), stride=(1, 13), padding=0, dilation=1, ceil_mode=False)
-        )
-      )
-    )
-    (1): Sequential(
-      (0): Flatten(start_dim=1, end_dim=-1)
-      (1): Dropout(p=0.5, inplace=False)
-      (2): Linear(in_features=816, out_features=5, bias=True)
-    )
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="plot-results">
-<h2>Plot results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>We use the history stored by Skorch during training to plot the performance of
-the model throughout training. Specifically, we plot the loss and the balanced
-balanced accuracy for the training and validation sets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
-<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" srcset="../_images/sphx_glr_plot_sleep_staging_chambon2018_001.png" alt="plot sleep staging chambon2018" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[  0   0  23  35   3]
- [  0   0  15   4   2]
- [  0   0 144  46   4]
- [  0   0  45  29   1]
- [  0   0  29   3   0]]
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-              precision    recall  f1-score   support
-
-           0       0.00      0.00      0.00        61
-           1       0.00      0.00      0.00        21
-           2       0.56      0.74      0.64       194
-           3       0.25      0.39      0.30        75
-           4       0.00      0.00      0.00        32
-
-    accuracy                           0.45       383
-   macro avg       0.16      0.23      0.19       383
-weighted avg       0.33      0.45      0.38       383
-</pre></div>
-</div>
-<p>Our model was able to learn despite the low amount of data that was available
-(only two recordings in this example) and reached a balanced accuracy of
-about 36% in a 5-class classification task (chance-level = 20%) on held-out
-data.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To further improve performance, more recordings should be included in the
-training set, and hyperparameters should be selected accordingly.
-Increasing the sequence length was also shown in <a class="footnote-reference brackets" href="#id4" id="id12">1</a> to help improve
-performance, especially when few EEG channels are available.</p>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  28.682 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  9 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-sleep-staging-chambon2018-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/fc5680253dfb92d25d5d415bcdc10089/plot_sleep_staging_chambon2018.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sleep_staging_chambon2018.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/9d9ad03ba61ec2571831dd8a39013a5d/plot_sleep_staging_chambon2018.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sleep_staging_chambon2018.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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deleted file mode 100644
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021">
-<span id="sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"></span><h1>Sleep staging on the Sleep Physionet dataset using Eldele2021<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021" title="Permalink to this heading">#</a></h1>
-<p>This tutorial shows how to train and test a sleep staging neural network with
-Braindecode. We use the attention-based model from <a class="footnote-reference brackets" href="#id5" id="id1">1</a> with the time distributed approach of <a class="footnote-reference brackets" href="#id6" id="id2">2</a>
-to learn on sequences of EEG windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id7" id="id3">3</a> <a class="footnote-reference brackets" href="#id8" id="id4">4</a>.</p>
-<section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id5"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id11">2</a>)</span></dt>
-<dd><p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
-Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
-Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
-</dd>
-<dt class="label" id="id6"><span class="brackets">2</span><span class="fn-backref">(<a href="#id2">1</a>,<a href="#id9">2</a>,<a href="#id10">3</a>)</span></dt>
-<dd><p>Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
-(2018)A Deep Learning Architecture for Temporal Sleep Stage
-Classification Using Multivariate and Multimodal Time Series.
-IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
-(758-769)</p>
-</dd>
-<dt class="label" id="id7"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
-<dd><p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-a sleep-dependent neuronal feedback loop: the slow-wave
-microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
-</dd>
-<dt class="label" id="id8"><span class="brackets"><a class="fn-backref" href="#id4">4</a></span></dt>
-<dd><p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-Research Resource for Complex Physiologic Signals.
-Circulation 101(23):e215-e220</p>
-</dd>
-</dl>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Divyesh Narayanan &lt;divyesh.narayanan@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-</pre></div>
-</div>
-</section>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<p>First, we load the data using the
-<a class="reference internal" href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
-two recordings from two different individuals: we will use the first one to
-train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
-sleep staging example).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
-<a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<p>Next, we preprocess the raw data. We convert the data to microvolts and apply
-a lowpass filter.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
-<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a> <span class="o">=</span> <span class="mi">30</span>
-<span class="c1"># Factor to convert from V to uV</span>
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a> <span class="o">=</span> <span class="mf">1e6</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="k">lambda</span> <span class="n">data</span><span class="p">:</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">multiply</span></a><span class="p">(</span><span class="n">data</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">factor</span></a><span class="p">)),</span>  <span class="c1"># Convert from V to uV</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;filter&#39;</span><span class="p">,</span> <span class="n">l_freq</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">h_freq</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">high_cut_hz</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
-  warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
-
-&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f50ab3c0e10&gt;
-</pre></div>
-</div>
-</section>
-<section id="extract-windows">
-<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Permalink to this heading">#</a></h3>
-<p>We extract 30-s windows to be used in the classification task.
-The Eldele2021 model takes a single channel as input. Here, the Fpz-Cz channel is used as it
-was found to give better performance than using the Pz-Oz channel</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
-    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span>
-<span class="p">}</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">picks</span><span class="o">=</span><span class="s2">&quot;Fpz-Cz&quot;</span><span class="p">,</span>  <span class="c1"># the other option is Pz-Oz,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="window-preprocessing">
-<h3>Window preprocessing<a class="headerlink" href="#window-preprocessing" title="Permalink to this heading">#</a></h3>
-<p>We also preprocess the windows by applying channel-wise z-score normalization
-in each window.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">scale</span> <span class="k">as</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a>
-
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html#sklearn.preprocessing.scale" title="sklearn.preprocessing.scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">standard_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50a90fa190&gt;
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>We split the dataset into training and validation set taking
-every other subject as train or valid.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="create-sequence-samplers">
-<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Permalink to this heading">#</a></h2>
-<p>Following the time distributed approach of <a class="footnote-reference brackets" href="#id6" id="id9">2</a>, we need to provide our
-neural network with sequences of windows, such that the embeddings of
-multiple consecutive windows can be concatenated and provided to a final
-classifier. We can achieve this by defining Sampler objects that return
-sequences of window indices.
-To simplify the example, we train the whole model end-to-end on sequences,
-rather than using the two-step approach of <a class="footnote-reference brackets" href="#id6" id="id10">2</a> (i.e. training the feature
-extractor on single windows, then freezing its weights and training the
-classifier).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Maximally overlapping sequences</span>
-
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-
-<span class="c1"># Print number of examples per class</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Validation examples: &#39;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Training examples:  133
-Validation examples:  133
-</pre></div>
-</div>
-<p>We also implement a transform to extract the label of the center window of a
-sequence to use it as target.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<span class="c1"># Use label of center window in the sequence</span>
-<span class="k">def</span> <span class="nf">get_center_label</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <a href="https://docs.python.org/3/library/numbers.html#numbers.Integral" title="numbers.Integral" class="sphx-glr-backref-module-numbers sphx-glr-backref-type-py-class"><span class="n">Integral</span></a><span class="p">):</span>
-        <span class="k">return</span> <span class="n">x</span>
-    <span class="k">return</span> <span class="n">x</span><span class="p">[</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ufunc.html#numpy.ufunc" title="numpy.ufunc" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">ceil</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)]</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span>
-
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="o">.</span><span class="n">target_transform</span> <span class="o">=</span> <span class="n">get_center_label</span>
-</pre></div>
-</div>
-<p>Finally, since some sleep stages appear a lot more often than others (e.g.
-most of the night is spent in the N2 stage), the classes are imbalanced. To
-avoid overfitting on the more frequent classes, we compute weights that we
-will provide to the loss function when training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
-<p>We can now create the deep learning model. In this tutorial, we use the sleep
-staging architecture introduced in <a class="footnote-reference brackets" href="#id5" id="id11">1</a>, which is an attention-based
-neural network. We use the time distributed version of the model, where the
-feature vectors of a sequence of windows are concatenated and passed to a
-linear layer for classification.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">,</span> <a href="../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<span class="c1"># Set random seed to be able to reproduce results</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
-<span class="c1"># Extract number of channels and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_channels</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-
-<a href="../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">SleepStagerEldele2021</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <span class="n">input_size_s</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">return_feats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-<a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.TimeDistributed.html#braindecode.models.TimeDistributed" title="braindecode.models.TimeDistributed" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">TimeDistributed</span></a><span class="p">(</span><a href="../generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span></a><span class="p">),</span>  <span class="c1"># apply model on each 30-s window</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span></a><span class="p">(</span>  <span class="c1"># apply linear layer on concatenated feature vectors</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html#torch.nn.Flatten" title="torch.nn.Flatten" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span></a><span class="p">(</span><span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span></a><span class="p">(</span><span class="mf">0.5</span><span class="p">),</span>
-        <a href="https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">feat_extractor</span><span class="o">.</span><span class="n">len_last_layer</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">)</span>
-    <span class="p">)</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
-<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
-braindecode object that is responsible for managing the training of neural
-networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
-training logic is the same as in
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
-
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="s1">&#39;balanced_accuracy&#39;</span><span class="p">,</span> <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
-    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential" title="torch.nn.Sequential" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
-    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
-    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
-    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
-<span class="c1"># supplied in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_bal_acc    train_loss    valid_bal_acc    valid_loss     dur
--------  ---------------  ------------  ---------------  ------------  ------
-      1           0.0522        4.4700           0.2000        4.9062  2.2202
-      2           0.4802        1.6978           0.1991        2.7382  1.7995
-      3           0.3317        1.8455           0.2000        3.4288  1.6948
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=Sequential(
-    (0): TimeDistributed(
-      (module): SleepStagerEldele2021(
-        (feature_extractor): Sequential(
-          (0): _MRCNN(
-            (GELU): GELU(approximate=none)
-            (features1): Sequential(
-              (0): Conv1d(1, 64, kernel_size=(50,), stride=(6,), padding=(24,), bias=False)
-              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (2): GELU(approximate=none)
-              (3): MaxPool1d(kernel_size=8, stride=2, padding=4, dilation=1, ceil_mode=False)
-              (4): Dropout(p=0.5, inplace=False)
-              (5): Conv1d(64, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
-              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (7): GELU(approximate=none)
-              (8): Conv1d(128, 128, kernel_size=(8,), stride=(1,), padding=(4,), bias=False)
-              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (10): GELU(approximate=none)
-              (11): MaxPool1d(kernel_size=4, stride=4, padding=2, dilation=1, ceil_mode=False)
-            )
-            (features2): Sequential(
-              (0): Conv1d(1, 64, kernel_size=(400,), stride=(50,), padding=(200,), bias=False)
-              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (2): GELU(approximate=none)
-              (3): MaxPool1d(kernel_size=4, stride=2, padding=2, dilation=1, ceil_mode=False)
-              (4): Dropout(p=0.5, inplace=False)
-              (5): Conv1d(64, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
-              (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (7): GELU(approximate=none)
-              (8): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,), bias=False)
-              (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-              (10): GELU(approximate=none)
-              (11): MaxPool1d(kernel_size=2, stride=2, padding=1, dilation=1, ceil_mode=False)
-            )
-            (dropout): Dropout(p=0.5, inplace=False)
-            (AFR): Sequential(
-              (0): _SEBasicBlock(
-                (conv1): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
-                (bn1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (relu): ReLU(inplace=True)
-                (conv2): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
-                (bn2): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                (se): _SELayer(
-                  (avg_pool): AdaptiveAvgPool1d(output_size=1)
-                  (fc): Sequential(
-                    (0): Linear(in_features=30, out_features=1, bias=False)
-                    (1): ReLU(inplace=True)
-                    (2): Linear(in_features=1, out_features=30, bias=False)
-                    (3): Sigmoid()
-                  )
-                )
-                (downsample): Sequential(
-                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,), bias=False)
-                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                )
-                (features): Sequential(
-                  (0): Conv1d(128, 30, kernel_size=(1,), stride=(1,))
-                  (1): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (2): ReLU(inplace=True)
-                  (3): Conv1d(30, 30, kernel_size=(1,), stride=(1,))
-                  (4): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-                  (5): _SELayer(
-                    (avg_pool): AdaptiveAvgPool1d(output_size=1)
-                    (fc): Sequential(
-                      (0): Linear(in_features=30, out_features=1, bias=False)
-                      (1): ReLU(inplace=True)
-                      (2): Linear(in_features=1, out_features=30, bias=False)
-                      (3): Sigmoid()
-                    )
-                  )
-                )
-              )
-            )
-          )
-          (1): _TCE(
-            (layers): ModuleList(
-              (0): _EncoderLayer(
-                (self_attn): _MultiHeadedAttention(
-                  (convs): ModuleList(
-                    (0): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                    (1): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                    (2): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                  )
-                  (linear): Linear(in_features=80, out_features=80, bias=True)
-                  (dropout): Dropout(p=0.1, inplace=False)
-                )
-                (feed_forward): _PositionwiseFeedForward(
-                  (w_1): Linear(in_features=80, out_features=120, bias=True)
-                  (w_2): Linear(in_features=120, out_features=80, bias=True)
-                  (dropout): Dropout(p=0.1, inplace=False)
-                )
-                (sublayer_output): ModuleList(
-                  (0): _SublayerOutput(
-                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
-                    (dropout): Dropout(p=0.1, inplace=False)
-                  )
-                  (1): _SublayerOutput(
-                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
-                    (dropout): Dropout(p=0.1, inplace=False)
-                  )
-                )
-                (conv): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-              )
-              (1): _EncoderLayer(
-                (self_attn): _MultiHeadedAttention(
-                  (convs): ModuleList(
-                    (0): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                    (1): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                    (2): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-                  )
-                  (linear): Linear(in_features=80, out_features=80, bias=True)
-                  (dropout): Dropout(p=0.1, inplace=False)
-                )
-                (feed_forward): _PositionwiseFeedForward(
-                  (w_1): Linear(in_features=80, out_features=120, bias=True)
-                  (w_2): Linear(in_features=120, out_features=80, bias=True)
-                  (dropout): Dropout(p=0.1, inplace=False)
-                )
-                (sublayer_output): ModuleList(
-                  (0): _SublayerOutput(
-                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
-                    (dropout): Dropout(p=0.1, inplace=False)
-                  )
-                  (1): _SublayerOutput(
-                    (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
-                    (dropout): Dropout(p=0.1, inplace=False)
-                  )
-                )
-                (conv): _CausalConv1d(30, 30, kernel_size=(7,), stride=(1,), padding=(6,))
-              )
-            )
-            (norm): LayerNorm((80,), eps=1e-06, elementwise_affine=True)
-          )
-        )
-      )
-    )
-    (1): Sequential(
-      (0): Flatten(start_dim=1, end_dim=-1)
-      (1): Dropout(p=0.5, inplace=False)
-      (2): Linear(in_features=7200, out_features=5, bias=True)
-    )
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="plot-results">
-<h2>Plot results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>We use the history stored by Skorch during training to plot the performance of
-the model throughout training. Specifically, we plot the loss and the balanced
-balanced accuracy for the training and validation sets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
-<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" srcset="../_images/sphx_glr_plot_sleep_staging_eldele2021_001.png" alt="plot sleep staging eldele2021" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[[</span><span class="n">i</span><span class="p">]][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))]</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="p">))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 0  0  0 60]
- [ 0  0  0  6]
- [ 0  0  0 48]
- [ 0  0  0 19]]
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, msg_start, len(result))
-              precision    recall  f1-score   support
-
-           0       0.00      0.00      0.00        60
-           1       0.00      0.00      0.00         6
-           2       0.00      0.00      0.00        48
-           3       0.14      1.00      0.25        19
-
-    accuracy                           0.14       133
-   macro avg       0.04      0.25      0.06       133
-weighted avg       0.02      0.14      0.04       133
-</pre></div>
-</div>
-<p>The model was able to learn despite the low amount of data that was available
-(only two recordings in this example) and reached a balanced accuracy of
-about 43% in a 5-class classification task (chance-level = 20%) on held-out
-data over 10 epochs.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>To further improve performance, the number of epochs should be increased.
-It has been reduced here for faster run-time in document generation. In
-testing, 10 epochs provided reasonable performance with around 89% balanced
-accuracy on training data and around 43% on held out validation data.
-Increasing the number of training recordings and optimizing the hyperparameters
-will also help increase performance</p>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  11.833 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  16 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-sleep-staging-eldele2021-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/763a361b3d3c09298e3afb84a82726b5/plot_sleep_staging_eldele2021.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sleep_staging_eldele2021.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/7132d1f42727a7844635064390fdd264/plot_sleep_staging_eldele2021.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sleep_staging_eldele2021.ipynb</span></code></a></p>
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-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
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diff --git a/stable/auto_examples/plot_sleep_staging_usleep.html b/stable/auto_examples/plot_sleep_staging_usleep.html
deleted file mode 100644
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network">
-<span id="sphx-glr-auto-examples-plot-sleep-staging-usleep-py"></span><h1>Sleep staging on the Sleep Physionet dataset using U-Sleep network<a class="headerlink" href="#sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network" title="Permalink to this heading">#</a></h1>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Please take a look at the simpler sleep staging example
-<span class="xref std std-ref">here</span>
-before going through this example. The current example uses a more complex
-architecture and a sequence-to-sequence (seq2seq) approach.</p>
-</div>
-<p>This tutorial shows how to train and test a sleep staging neural network with
-Braindecode. We adapt the U-Sleep approach of <a class="footnote-reference brackets" href="#id4" id="id1">1</a> to learn on sequences of EEG
-windows using the openly accessible Sleep Physionet dataset <a class="footnote-reference brackets" href="#id5" id="id2">2</a> <a class="footnote-reference brackets" href="#id6" id="id3">3</a>.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>The example is written to have a very short excecution time.
-This number of epochs is here too small and very few recordings are used.
-To obtain competitive results you need to use more data and more epochs.</p>
-</div>
-<section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
-<dl class="footnote brackets">
-<dt class="label" id="id4"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id7">2</a>,<a href="#id8">3</a>,<a href="#id9">4</a>,<a href="#id10">5</a>,<a href="#id11">6</a>,<a href="#id12">7</a>)</span></dt>
-<dd><p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
-U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
-<a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
-</dd>
-<dt class="label" id="id5"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-a sleep-dependent neuronal feedback loop: the slow-wave
-microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).</p>
-</dd>
-<dt class="label" id="id6"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
-<dd><p>Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-Research Resource for Complex Physiologic Signals.
-Circulation 101(23):e215-e220</p>
-</dd>
-</dl>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Theo Gnassounou &lt;theo.gnassounou@inria.fr&gt;</span>
-<span class="c1">#          Omar Chehab &lt;l-emir-omar.chehab@inria.fr&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-</pre></div>
-</div>
-</section>
-<section id="loading-and-preprocessing-the-dataset">
-<h2>Loading and preprocessing the dataset<a class="headerlink" href="#loading-and-preprocessing-the-dataset" title="Permalink to this heading">#</a></h2>
-<section id="loading">
-<h3>Loading<a class="headerlink" href="#loading" title="Permalink to this heading">#</a></h3>
-<p>First, we load the data using the
-<a class="reference internal" href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.sleep_physionet.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.sleep_physionet.SleepPhysionet</span></code></a> class. We load
-two recordings from two different individuals: we will use the first one to
-train our network and the second one to evaluate performance (as in the <a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">MNE</a>
-sleep staging example).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">30</span> <span class="o">*</span> <span class="mi">400</span><span class="p">)</span>  <span class="c1"># we only keep 400 windows of 30s to speed example</span>
-<a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">SleepPhysionet</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">,</span> <span class="n">recording_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">crop_wake_mins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#tuple" title="builtins.tuple" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">crop</span></a><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="preprocessing">
-<h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to this heading">#</a></h3>
-<p>Next, we preprocess the raw data. We scale each channel recording-wise to
-have zero median and unit interquartile range. We don’t upsample to 128 Hz as
-done in <a class="footnote-reference brackets" href="#id4" id="id7">1</a> so that we keep the example as light as possible. No filtering
-is described in <a class="footnote-reference brackets" href="#id4" id="id8">1</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a>
-<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.robust_scale.html#sklearn.preprocessing.robust_scale" title="sklearn.preprocessing.robust_scale" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-function"><span class="n">robust_scale</span></a><span class="p">,</span> <span class="n">channel_wise</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span>
-
-<span class="c1"># Transform the data</span>
-<a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.sleep_physionet.SleepPhysionet object at 0x7f50caa0cd10&gt;
-</pre></div>
-</div>
-</section>
-<section id="extract-windows">
-<h3>Extract windows<a class="headerlink" href="#extract-windows" title="Permalink to this heading">#</a></h3>
-<p>We extract 30-s windows to be used in the classification task.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-
-
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a> <span class="o">=</span> <span class="p">{</span>  <span class="c1"># We merge stages 3 and 4 following AASM standards.</span>
-    <span class="s1">&#39;Sleep stage W&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 1&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 2&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 3&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage 4&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
-    <span class="s1">&#39;Sleep stage R&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
-<span class="p">}</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">=</span> <span class="mi">30</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_s</span></a> <span class="o">*</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a>
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows_dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span>
-    <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">mapping</span></a><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="split-dataset-into-train-and-valid">
-<h3>Split dataset into train and valid<a class="headerlink" href="#split-dataset-into-train-and-valid" title="Permalink to this heading">#</a></h3>
-<p>We split the dataset into training and validation set taking
-every other subject as train or valid.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">valid</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">subject_ids</span></a><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows_dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">split_ids</span></a><span class="p">)</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;valid&quot;</span><span class="p">]</span>
-</pre></div>
-</div>
-</section>
-</section>
-<section id="create-sequence-samplers">
-<h2>Create sequence samplers<a class="headerlink" href="#create-sequence-samplers" title="Permalink to this heading">#</a></h2>
-<p>Following the sequence-to-sequence approach of <a class="footnote-reference brackets" href="#id4" id="id9">1</a>, we need to provide our
-neural network with sequences of windows. We can achieve this by defining
-Sampler objects that return sequences of windows.
-Non-overlapping sequences of 35 windows are used in <a class="footnote-reference brackets" href="#id4" id="id10">1</a>, however to limit
-the memory requirements for this example we use shorter sequences of 3
-windows.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.samplers</span> <span class="kn">import</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Sequences of 3 consecutive windows; originally 35 in paper</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># Non-overlapping sequences</span>
-
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">train_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-<a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a> <span class="o">=</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class"><span class="n">SequenceSampler</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata" title="braindecode.datasets.BaseConcatDataset.get_metadata" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">valid_set</span><span class="o">.</span><span class="n">get_metadata</span></a><span class="p">(),</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_windows_stride</span></a><span class="p">)</span>
-
-<span class="c1"># Print number of examples per class</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">))</span>
-<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>133
-133
-</pre></div>
-</div>
-<p>Finally, since some sleep stages appear a lot more often than others (e.g.
-most of the night is spent in the N2 stage), the classes are imbalanced. To
-avoid overfitting to the more frequent classes, we compute weights that we
-will provide to the loss function when training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">compute_class_weight</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">]</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a> <span class="o">=</span> <span class="n">compute_class_weight</span><span class="p">(</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.unique.html#numpy.unique" title="numpy.unique" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">unique</span></a><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">),</span> <span class="n">y</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_train</span></a><span class="p">)</span>
-</pre></div>
-</div>
-</section>
-<section id="create-model">
-<h2>Create model<a class="headerlink" href="#create-model" title="Permalink to this heading">#</a></h2>
-<p>We can now create the deep learning model. In this tutorial, we use the
-U-Sleep architecture introduced in <a class="footnote-reference brackets" href="#id4" id="id11">1</a>, which is fully convolutional
-neural network.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">braindecode.util</span> <span class="kn">import</span> <a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.models</span> <span class="kn">import</span> <a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span>  <span class="c1"># check if GPU is available</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <a href="https://pytorch.org/docs/stable/generated/torch.cuda.is_available.html#torch.cuda.is_available" title="torch.cuda.is_available" class="sphx-glr-backref-module-torch-cuda sphx-glr-backref-type-py-function"><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span></a><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">benchmark</span></a> <span class="o">=</span> <span class="kc">True</span>
-<span class="c1"># Set random seed to be able to roughly reproduce results</span>
-<span class="c1"># Note that with cudnn benchmark set to True, GPU indeterminism</span>
-<span class="c1"># may still make results substantially different between runs.</span>
-<span class="c1"># To obtain more consistent results at the cost of increased computation time,</span>
-<span class="c1"># you can set `cudnn_benchmark=False` in `set_random_seeds`</span>
-<span class="c1"># or remove `torch.backends.cudnn.benchmark = True`</span>
-<a href="../generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds" title="braindecode.util.set_random_seeds" class="sphx-glr-backref-module-braindecode-util sphx-glr-backref-type-py-function"><span class="n">set_random_seeds</span></a><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">31</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">)</span>
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a> <span class="o">=</span> <span class="mi">5</span>
-<span class="c1"># Extract number of channels and time steps from dataset</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span>
-
-<a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class"><span class="n">USleep</span></a><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">in_chans</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">depth</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
-    <span class="n">with_skip_connection</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_classes</span></a><span class="p">,</span>
-    <span class="n">input_size_s</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">input_size_samples</span></a> <span class="o">/</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">,</span>
-    <span class="n">apply_softmax</span><span class="o">=</span><span class="kc">False</span>
-<span class="p">)</span>
-
-<span class="c1"># Send model to GPU</span>
-<span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#bool" title="builtins.bool" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">cuda</span></a><span class="p">:</span>
-    <a href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.cuda" title="torch.nn.Module.cuda" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-method"><span class="n">model</span><span class="o">.</span><span class="n">cuda</span></a><span class="p">()</span>
-</pre></div>
-</div>
-</section>
-<section id="training">
-<h2>Training<a class="headerlink" href="#training" title="Permalink to this heading">#</a></h2>
-<p>We can now train our network. <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> is a
-braindecode object that is responsible for managing the training of neural
-networks. It inherits from <code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.NeuralNetClassifier</span></code>, so the
-training logic is the same as in
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/">Skorch</a>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>We use different hyperparameters from <a class="footnote-reference brackets" href="#id4" id="id12">1</a>, as these hyperparameters were
-optimized on different datasets and with a different number of recordings.
-Generally speaking, it is recommended to perform hyperparameter
-optimization if reusing this code on a different dataset or with more
-recordings.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">skorch.helper</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a>
-<span class="kn">from</span> <span class="nn">skorch.callbacks</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a>
-<span class="kn">from</span> <span class="nn">braindecode</span> <span class="kn">import</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a>
-
-<a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a> <span class="o">=</span> <span class="mf">1e-3</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a> <span class="o">=</span> <span class="mi">32</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a> <span class="o">=</span> <span class="mi">3</span>  <span class="c1"># we use few epochs for speed and but more than one for plotting</span>
-
-<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a>
-
-
-<span class="k">def</span> <span class="nf">balanced_accuracy_multi</span><span class="p">(</span><a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="k">return</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">balanced_accuracy_score</span></a><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-
-
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
-    <span class="n">on_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class"><span class="n">EpochScoring</span></a><span class="p">(</span>
-    <span class="n">scoring</span><span class="o">=</span><span class="n">balanced_accuracy_multi</span><span class="p">,</span>
-    <span class="n">on_train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span>
-    <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a> <span class="o">=</span> <span class="p">[</span>
-    <span class="p">(</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_bal_acc</span></a><span class="p">),</span>
-    <span class="p">(</span><span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">,</span> <a href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.EpochScoring" title="skorch.callbacks.EpochScoring" class="sphx-glr-backref-module-skorch-callbacks sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_bal_acc</span></a><span class="p">)</span>
-<span class="p">]</span>
-
-<span class="n">clf</span> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier" title="skorch.classifier.NeuralNetClassifier" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-class"><span class="n">EEGClassifier</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep" class="sphx-glr-backref-module-braindecode-models sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">model</span></a><span class="p">,</span>
-    <span class="n">criterion</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss" class="sphx-glr-backref-module-torch-nn sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span></a><span class="p">,</span>
-    <span class="n">criterion__weight</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">class_weights</span></a><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">),</span>
-    <span class="n">optimizer</span><span class="o">=</span><a href="https://pytorch.org/docs/stable/generated/torch.optim.Adam.html#torch.optim.Adam" title="torch.optim.Adam" class="sphx-glr-backref-module-torch-optim sphx-glr-backref-type-py-class"><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></a><span class="p">,</span>
-    <span class="n">iterator_train__shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">iterator_train__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_sampler</span></a><span class="p">,</span>
-    <span class="n">iterator_valid__sampler</span><span class="o">=</span><a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">,</span>
-    <span class="n">train_split</span><span class="o">=</span><a href="https://skorch.readthedocs.io/en/stable/helper.html#skorch.helper.predefined_split" title="skorch.helper.predefined_split" class="sphx-glr-backref-module-skorch-helper sphx-glr-backref-type-py-function"><span class="n">predefined_split</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">),</span>  <span class="c1"># using valid_set for validation</span>
-    <span class="n">optimizer__lr</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lr</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_size</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">callbacks</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">device</span></a><span class="p">,</span>
-<span class="p">)</span>
-<span class="c1"># Model training for a specified number of epochs. `y` is None as it is already</span>
-<span class="c1"># supplied in the dataset.</span>
-<a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.fit" title="skorch.classifier.NeuralNetClassifier.fit" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">fit</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">train_set</span></a><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">n_epochs</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/usr/share/miniconda/envs/braindecode/lib/python3.7/site-packages/torch/nn/modules/conv.py:304: UserWarning: Using padding=&#39;same&#39; with even kernel lengths and odd dilation may require a zero-padded copy of the input be created (Triggered internally at  /home/conda/feedstock_root/build_artifacts/pytorch-recipe_1664817695526/work/aten/src/ATen/native/Convolution.cpp:882.)
-  self.padding, self.dilation, self.groups)
-  epoch    train_bal_acc    train_loss    valid_bal_acc    valid_loss     dur
--------  ---------------  ------------  ---------------  ------------  ------
-      1           0.2341        1.5822           0.1546        1.5791  2.9393
-      2           0.2053        1.5601           0.1591        1.5832  2.5449
-      3           0.2713        1.5431           0.2033        1.5853  2.5110
-
-&lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
-  module_=USleep(
-    (encoder): Sequential(
-      (0): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(2, 6, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (1): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(6, 9, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (2): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(9, 11, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (3): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(11, 15, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(15, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (4): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(15, 20, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (5): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(20, 28, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(28, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (6): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(28, 40, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (7): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(40, 55, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(55, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (8): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(55, 77, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(77, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (9): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(77, 108, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (10): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(108, 152, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-      (11): _EncoderBlock(
-        (block_prepool): Sequential(
-          (0): Conv1d(152, 214, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(214, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (pad): ConstantPad1d(padding=(1, 1), value=0)
-        (maxpool): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
-      )
-    )
-    (bottom): Sequential(
-      (0): Conv1d(214, 302, kernel_size=(7,), stride=(1,), padding=(3,))
-      (1): ELU(alpha=1.0)
-      (2): BatchNorm1d(302, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    )
-    (decoder): Sequential(
-      (0): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(302, 214, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(214, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(428, 214, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(214, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (1): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(214, 152, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(304, 152, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (2): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(152, 108, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(216, 108, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (3): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(108, 77, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(77, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(154, 77, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(77, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (4): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(77, 55, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(55, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(110, 55, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(55, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (5): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(55, 40, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(80, 40, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (6): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(40, 28, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(28, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(56, 28, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(28, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (7): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(28, 20, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(40, 20, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (8): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(20, 15, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(15, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(30, 15, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(15, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (9): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(15, 11, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(22, 11, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (10): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(11, 9, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(18, 9, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-      (11): _DecoderBlock(
-        (block_preskip): Sequential(
-          (0): Upsample(scale_factor=2.0, mode=nearest)
-          (1): Conv1d(9, 6, kernel_size=(2,), stride=(1,), padding=same)
-          (2): ELU(alpha=1.0)
-          (3): BatchNorm1d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-        (block_postskip): Sequential(
-          (0): Conv1d(12, 6, kernel_size=(7,), stride=(1,), padding=same)
-          (1): ELU(alpha=1.0)
-          (2): BatchNorm1d(6, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-        )
-      )
-    )
-    (clf): Sequential(
-      (0): Conv1d(6, 6, kernel_size=(1,), stride=(1,))
-      (1): Tanh()
-      (2): AvgPool1d(kernel_size=(3000,), stride=(3000,), padding=(0,))
-      (3): Conv1d(6, 5, kernel_size=(1,), stride=(1,))
-      (4): ELU(alpha=1.0)
-      (5): Conv1d(5, 5, kernel_size=(1,), stride=(1,))
-      (6): Identity()
-    )
-  ),
-)
-</pre></div>
-</div>
-</section>
-<section id="plot-results">
-<h2>Plot results<a class="headerlink" href="#plot-results" title="Permalink to this heading">#</a></h2>
-<p>We use the history stored by Skorch during training to plot the performance of
-the model throughout training. Specifically, we plot the loss and the balanced
-balanced accuracy for the training and validation sets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-
-<span class="c1"># Extract loss and balanced accuracy values for plotting from history object</span>
-<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">history</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
-<span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Epoch&quot;</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">7</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_loss&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_loss&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
-<span class="n">df</span><span class="p">[[</span><span class="s1">&#39;train_bal_acc&#39;</span><span class="p">,</span> <span class="s1">&#39;valid_bal_acc&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Loss&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Balanced accuracy&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax1</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax2</span><span class="o">.</span><span class="n">legend</span></a><span class="p">([</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Valid&#39;</span><span class="p">])</span>
-<a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.tight_layout" title="matplotlib.figure.Figure.tight_layout" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-method"><span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_sleep_staging_usleep_001.png" srcset="../_images/sphx_glr_plot_sleep_staging_usleep_001.png" alt="plot sleep staging usleep" class = "sphx-glr-single-img"/><p>Finally, we also display the confusion matrix and classification report:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">,</span> <a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a>
-
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <a href="../generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler" class="sphx-glr-backref-module-braindecode-samplers sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_sampler</span></a><span class="p">])</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a> <span class="o">=</span> <a href="https://skorch.readthedocs.io/en/stable/classifier.html#skorch.classifier.NeuralNetClassifier.predict" title="skorch.classifier.NeuralNetClassifier.predict" class="sphx-glr-backref-module-skorch-classifier sphx-glr-backref-type-py-method"><span class="n">clf</span><span class="o">.</span><span class="n">predict</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">valid_set</span></a><span class="p">)</span>
-
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">confusion_matrix</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report" class="sphx-glr-backref-module-sklearn-metrics sphx-glr-backref-type-py-function"><span class="n">classification_report</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_true</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y_pred</span></a><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[[ 9 20  7 23  5]
- [ 8  9  1  4  0]
- [75 29 31 58  4]
- [25  9 21 26  3]
- [15  4  9  4  0]]
-              precision    recall  f1-score   support
-
-           0       0.07      0.14      0.09        64
-           1       0.13      0.41      0.19        22
-           2       0.45      0.16      0.23       197
-           3       0.23      0.31      0.26        84
-           4       0.00      0.00      0.00        32
-
-    accuracy                           0.19       399
-   macro avg       0.17      0.20      0.16       399
-weighted avg       0.29      0.19      0.20       399
-</pre></div>
-</div>
-<p>Our model was able to learn, as shown by the decreasing training and
-validation loss values, despite the low amount of data that was available
-(only two recordings in this example). To further improve performance, more
-recordings should be included in the training set, the model should be
-trained for more epochs and hyperparameters should be optimized.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  12.305 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  314 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-sleep-staging-usleep-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/692af8474f3e890e34bcf84a5b5b929c/plot_sleep_staging_usleep.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sleep_staging_usleep.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/93aa63aa0ed0b3ee87caaf393a756a8c/plot_sleep_staging_usleep.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_sleep_staging_usleep.ipynb</span></code></a></p>
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-<li class="toctree-l1 current active"><a class="current reference internal" href="#">Split Dataset Example</a></li>
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-split-dataset-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="split-dataset-example">
-<span id="sphx-glr-auto-examples-plot-split-dataset-py"></span><h1>Split Dataset Example<a class="headerlink" href="#split-dataset-example" title="Permalink to this heading">#</a></h1>
-<p>In this example, we show multiple ways of how to split datasets.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Authors: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a>
-</pre></div>
-</div>
-<p>First, we create a dataset based on BCIC IV 2a fetched with MOABB,</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">MOABBDataset</span></a><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s2">&quot;BNCI2014001&quot;</span><span class="p">,</span> <span class="n">subject_ids</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-48 events found
-Event IDs: [1 2 3 4]
-</pre></div>
-</div>
-<p>ds has a pandas DataFrame with additional description of its internal datasets</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_5</td>
-    </tr>
-    <tr>
-      <th>6</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>7</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>8</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>9</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>10</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>11</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>We can split the dataset based on the info in the description, for example
-based on different runs. The returned dictionary will have string keys
-corresponding to unique entries in the description DataFrame column</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;run_4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;run_0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21c2d10&gt;, &#39;run_1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21400d0&gt;, &#39;run_2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1f75590&gt;, &#39;run_3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1f00390&gt;, &#39;run_4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1f00510&gt;, &#39;run_5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1fb3ed0&gt;}
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>We can also split the dataset based on a list of integers corresponding to
-rows in the description. In this case, the returned dictionary will have
-‘0’ as the only key</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;0&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b216ef50&gt;}
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_T</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>If we want multiple splits based on indices, we can also specify a list of
-list of integers. In this case, the dictionary will have string keys
-representing the id of the dataset split in the order of the given list of
-integers</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1fb3ed0&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50d5718c50&gt;, &#39;2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50d57183d0&gt;}
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>If we want to split based on a list of indices but you want to specify
-the keys in the output dictionary you can pass a dict as:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">dataset</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span>
-    <span class="p">{</span><span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="s2">&quot;valid&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="s2">&quot;test&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]}</span>
-<span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a><span class="p">[</span><span class="s2">&quot;test&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21f3310&gt;, &#39;valid&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50d5718b50&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50d5718890&gt;}
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>subject</th>
-      <th>session</th>
-      <th>run</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_0</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_1</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_2</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_3</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_4</td>
-    </tr>
-    <tr>
-      <th>5</th>
-      <td>1</td>
-      <td>session_E</td>
-      <td>run_5</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>Similarly, we can split datasets after creating windows</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">windows</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_windows_from_events</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.MOABBDataset.html#braindecode.datasets.MOABBDataset" title="braindecode.datasets.MOABBDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="n">trial_start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">trial_stop_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="s2">&quot;run&quot;</span><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-Used Annotations descriptions: [&#39;feet&#39;, &#39;left_hand&#39;, &#39;right_hand&#39;, &#39;tongue&#39;]
-Using data from preloaded Raw for 48 events and 1000 original time points ...
-0 bad epochs dropped
-
-{&#39;run_0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b20628d0&gt;, &#39;run_1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21c2d10&gt;, &#39;run_2&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50d572ee50&gt;, &#39;run_3&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b201e7d0&gt;, &#39;run_4&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b201ea90&gt;, &#39;run_5&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1f75590&gt;}
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21808d0&gt;}
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;0&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21400d0&gt;, &#39;1&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b1f43cd0&gt;}
-</pre></div>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.split" title="braindecode.datasets.BaseConcatDataset.split" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">windows</span><span class="o">.</span><span class="n">split</span></a><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">test</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]))</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">splits</span></a>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{&#39;train&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21f3f90&gt;, &#39;test&#39;: &lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f50b21c2d10&gt;}
-</pre></div>
-</div>
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-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-split-dataset-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/b6d17638dc8505e659bc7a9abe0c7335/plot_split_dataset.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_split_dataset.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/b38e40c9ee338673865922575e84fd7f/plot_split_dataset.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_split_dataset.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
-</section>
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-  <a class='left-prev' id="prev-link" href="index.html" title="previous page">
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-          <p class="prev-next-subtitle">previous</p>
-          <p class="prev-next-title">Examples</p>
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-      <p class="prev-next-title">Load and save dataset example</p>
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diff --git a/stable/auto_examples/plot_tuh_discrete_multitarget.html b/stable/auto_examples/plot_tuh_discrete_multitarget.html
deleted file mode 100644
index 846d5b83..00000000
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-  <div class="sphx-glr-download-link-note admonition note">
-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="multiple-discrete-targets-with-the-tuh-eeg-corpus">
-<span id="sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"></span><h1>Multiple discrete targets with the TUH EEG Corpus<a class="headerlink" href="#multiple-discrete-targets-with-the-tuh-eeg-corpus" title="Permalink to this heading">#</a></h1>
-<p>In this example, we showcase usage of multiple discrete targets per recording
-with the TUH EEG Corpus.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">mne</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a>
-
-<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages everytime a window is extracted</span>
-</pre></div>
-</div>
-<p>If you want to try this code with the actual data, please delete the next
-section. We are required to mock some dataset functionality, since the data
-is not available at creation time of this example.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
-</pre></div>
-</div>
-<p>We start by creating a TUH dataset. Instead of just a str, we give it
-multiple strings as target names. Each of the strings has to exist as a
-column in the description DataFrame.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
-<span class="n">tuh</span> <span class="o">=</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
-    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">target_name</span><span class="o">=</span><span class="p">(</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;gender&#39;</span><span class="p">),</span>  <span class="c1"># use both age and gender as decoding target</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-<span class="p">)</span>
-<span class="n">tuh</span><span class="o">.</span><span class="n">description</span>
-</pre></div>
-</div>
-<div class="output_subarea output_html rendered_html output_result">
-<div>
-<style scoped>
-    .dataframe tbody tr th:only-of-type {
-        vertical-align: middle;
-    }
-
-    .dataframe tbody tr th {
-        vertical-align: top;
-    }
-
-    .dataframe thead th {
-        text-align: right;
-    }
-</style>
-<table border="1" class="dataframe">
-  <thead>
-    <tr style="text-align: right;">
-      <th></th>
-      <th>path</th>
-      <th>version</th>
-      <th>year</th>
-      <th>month</th>
-      <th>day</th>
-      <th>subject</th>
-      <th>session</th>
-      <th>segment</th>
-      <th>age</th>
-      <th>gender</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <th>0</th>
-      <td>tuh_eeg/v1.1.0/edf/02_tcp_le/000/00000058/s001...</td>
-      <td>v1.1.0</td>
-      <td>2003</td>
-      <td>2</td>
-      <td>5</td>
-      <td>58</td>
-      <td>1</td>
-      <td>0</td>
-      <td>0</td>
-      <td>M</td>
-    </tr>
-    <tr>
-      <th>1</th>
-      <td>tuh_eeg/v1.1.0/edf/01_tcp_ar/099/00009932/s004...</td>
-      <td>v1.1.0</td>
-      <td>2014</td>
-      <td>9</td>
-      <td>30</td>
-      <td>9932</td>
-      <td>4</td>
-      <td>13</td>
-      <td>53</td>
-      <td>F</td>
-    </tr>
-    <tr>
-      <th>2</th>
-      <td>tuh_eeg/v1.1.0/edf/03_tcp_ar_a/123/00012331/s0...</td>
-      <td>v1.1.0</td>
-      <td>2014</td>
-      <td>12</td>
-      <td>14</td>
-      <td>12331</td>
-      <td>3</td>
-      <td>2</td>
-      <td>39</td>
-      <td>M</td>
-    </tr>
-    <tr>
-      <th>3</th>
-      <td>tuh_eeg/v1.1.0/edf/01_tcp_ar/000/00000000/s001...</td>
-      <td>v1.1.0</td>
-      <td>2015</td>
-      <td>12</td>
-      <td>30</td>
-      <td>0</td>
-      <td>1</td>
-      <td>0</td>
-      <td>37</td>
-      <td>M</td>
-    </tr>
-    <tr>
-      <th>4</th>
-      <td>tuh_eeg/v1.2.0/edf/03_tcp_ar_a/149/00014928/s0...</td>
-      <td>v1.2.0</td>
-      <td>2016</td>
-      <td>1</td>
-      <td>15</td>
-      <td>14928</td>
-      <td>4</td>
-      <td>7</td>
-      <td>83</td>
-      <td>F</td>
-    </tr>
-  </tbody>
-</table>
-</div>
-</div>
-<br />
-<br /><p>Iterating through the dataset gives x as ndarray(n_channels x 1) as well as
-the target as [age of the subject, gender of the subject]. Let’s look at the last example
-as it has more interesting age/gender labels (compare to the last row of the dataframe above).</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a> <span class="o">=</span> <span class="n">tuh</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x:&#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y:&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x: [[-1.39513281]
- [ 0.87884217]
- [ 0.76573971]
- [-1.07025866]
- [ 2.08539375]
- [ 0.04078953]
- [-0.49139174]
- [-0.00571471]
- [ 0.43150701]
- [ 0.6832762 ]
- [ 0.07593693]
- [-0.23194062]
- [-1.60383273]
- [ 1.10370984]
- [-1.33047682]
- [-0.56567374]
- [-0.52561288]
- [ 0.38533583]
- [-0.13483532]
- [-1.0106733 ]
- [ 0.61984129]]
-y: [83, &#39;F&#39;]
-</pre></div>
-</div>
-<p>We will skip preprocessing steps for now, since it is not the aim of this
-example. Instead, we will directly create compute windows. We specify a
-mapping from genders ‘M’ and ‘F’ to integers, since this is required for
-decoding.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <span class="n">tuh</span><span class="p">,</span>
-    <span class="n">start_offset_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-    <span class="n">stop_offset_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">window_size_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-    <span class="n">window_stride_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">mapping</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;M&#39;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;F&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">},</span>  <span class="c1"># map non-digit targets</span>
-<span class="p">)</span>
-<span class="c1"># store the number of windows required for loading later on</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.set_description" title="braindecode.datasets.BaseConcatDataset.set_description" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-method"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">set_description</span></a><span class="p">({</span>
-    <span class="s2">&quot;n_windows&quot;</span><span class="p">:</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span><span class="o">.</span><span class="n">datasets</span></a><span class="p">]})</span>
-</pre></div>
-</div>
-<p>Iterating through the dataset gives x as ndarray(n_channels x 1000), y as
-[age, gender], and ind. Let’s look at the last example again.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;x:&#39;</span><span class="p">,</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;y:&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ind:&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>x: [[ 1.8291361   0.13107584  0.09874357 ...  1.5261606  -1.7010179
-  -1.3951328 ]
- [-1.0897747   0.53174806  0.40103233 ... -2.2451496  -0.872565
-   0.8788422 ]
- [-0.3393165  -0.14407331  1.3640033  ...  0.11461935  0.11734861
-   0.7657397 ]
- ...
- [-0.2753506  -1.1553683   0.13125211 ... -0.37722126 -0.5284417
-  -0.13483532]
- [-0.3746723   0.05251044  1.1372249  ... -0.9949117   0.50443316
-  -1.0106733 ]
- [-1.0833622   1.2574992  -1.4423852  ...  1.876381    1.0440995
-   0.6198413 ]]
-y: [83, 1]
-ind: [3, 2600, 3600]
-</pre></div>
-</div>
-<p>We give the dataset to a pytorch DataLoader, such that it can be used for
-model training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a> <span class="o">=</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class"><span class="n">DataLoader</span></a><span class="p">(</span>
-    <span class="n">dataset</span><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">,</span>
-    <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>Iterating through the DataLoader gives batch_X as tensor(4 x n_channels x
-1000), batch_y as [tensor([4 x age of subject]), tensor([4 x gender of
-subject])], and batch_ind. We will iterate to the end to look at the last example
-again.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a> <span class="ow">in</span> <a href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader" class="sphx-glr-backref-module-torch-utils-data sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dl</span></a><span class="p">:</span>
-    <span class="k">pass</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;batch_X:&#39;</span><span class="p">,</span> <a href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="torch.Tensor" class="sphx-glr-backref-module-torch sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_X</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;batch_y:&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_y</span></a><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;batch_ind:&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">batch_ind</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>batch_X: tensor([[[ 5.5363e-02,  8.2126e-01, -8.4776e-01,  ...,  9.8022e-01,
-          -7.0758e-01, -1.3419e+00],
-         [ 5.3741e-01,  2.8508e-01,  1.7888e+00,  ...,  1.8968e+00,
-          -2.7722e-01,  1.2931e+00],
-         [-2.6328e-01,  2.8525e+00,  1.6408e+00,  ..., -1.7391e-01,
-          -1.3787e-01,  1.3067e+00],
-         ...,
-         [ 7.6871e-01,  9.6170e-02, -1.9727e+00,  ..., -1.3167e+00,
-           1.2657e-01, -5.7562e-01],
-         [-7.7614e-01, -1.3191e-01,  1.0751e+00,  ...,  6.8062e-01,
-          -1.4114e+00, -5.6931e-01],
-         [-1.1234e+00, -9.2222e-01, -8.1196e-01,  ...,  2.9172e-01,
-          -1.1871e+00,  1.1650e+00]],
-
-        [[ 5.3379e-01,  4.7839e-01, -1.5889e+00,  ..., -9.3951e-01,
-           6.9861e-01,  1.2567e-01],
-         [-7.3573e-01,  1.1935e+00,  6.5482e-01,  ...,  1.7572e+00,
-          -1.4189e+00, -1.2484e-01],
-         [ 3.1653e-01, -1.4104e+00, -1.0475e-01,  ...,  1.0421e+00,
-           9.9583e-01,  6.4541e-01],
-         ...,
-         [-7.6099e-01,  1.1348e+00,  1.2041e+00,  ..., -9.5639e-01,
-          -3.9937e-01, -2.1994e+00],
-         [ 1.0830e-03, -4.0075e-01, -1.2254e+00,  ...,  2.0254e+00,
-          -1.2927e-01,  9.5079e-02],
-         [-1.9702e-01,  1.1700e+00, -7.7355e-01,  ...,  6.4929e-01,
-           2.8302e+00,  4.4761e-01]],
-
-        [[ 4.4920e-01,  2.9287e-01, -4.4798e-01,  ...,  1.4734e-01,
-          -1.9941e+00, -3.7620e-01],
-         [ 1.3327e+00,  1.9766e-01, -4.9474e-01,  ..., -2.8459e-01,
-           2.9799e-01,  7.7121e-01],
-         [ 1.2965e-01,  7.2586e-01,  3.1546e-01,  ..., -5.4598e-01,
-          -1.4853e-01,  7.8705e-01],
-         ...,
-         [-4.2984e-01, -7.6921e-01,  6.0587e-01,  ..., -5.4981e-01,
-           1.7690e-01,  6.0530e-01],
-         [ 4.0353e-01,  5.1134e-01,  4.1883e-01,  ...,  6.0788e-01,
-          -1.6549e+00, -6.7648e-01],
-         [-5.7833e-01, -4.9057e-01,  8.2945e-01,  ...,  2.6360e-01,
-           1.0847e-01,  5.0733e-01]],
-
-        [[ 1.8291e+00,  1.3108e-01,  9.8744e-02,  ...,  1.5262e+00,
-          -1.7010e+00, -1.3951e+00],
-         [-1.0898e+00,  5.3175e-01,  4.0103e-01,  ..., -2.2451e+00,
-          -8.7256e-01,  8.7884e-01],
-         [-3.3932e-01, -1.4407e-01,  1.3640e+00,  ...,  1.1462e-01,
-           1.1735e-01,  7.6574e-01],
-         ...,
-         [-2.7535e-01, -1.1554e+00,  1.3125e-01,  ..., -3.7722e-01,
-          -5.2844e-01, -1.3484e-01],
-         [-3.7467e-01,  5.2510e-02,  1.1372e+00,  ..., -9.9491e-01,
-           5.0443e-01, -1.0107e+00],
-         [-1.0834e+00,  1.2575e+00, -1.4424e+00,  ...,  1.8764e+00,
-           1.0441e+00,  6.1984e-01]]])
-batch_y: [tensor([83, 83, 83, 83]), tensor([1, 1, 1, 1])]
-batch_ind: [tensor([0, 1, 2, 3]), tensor([   0, 1000, 2000, 2600]), tensor([1000, 2000, 3000, 3600])]
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.293 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  18 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-tuh-discrete-multitarget-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/7072ea9a517ea63ad1a0cf3ca2153f10/plot_tuh_discrete_multitarget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tuh_discrete_multitarget.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/698f5f9879e8de86ce2b89d52213c004/plot_tuh_discrete_multitarget.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tuh_discrete_multitarget.ipynb</span></code></a></p>
-</div>
-</div>
-<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
-</section>
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diff --git a/stable/auto_examples/plot_tuh_eeg_corpus.html b/stable/auto_examples/plot_tuh_eeg_corpus.html
deleted file mode 100644
index e279496f..00000000
--- a/stable/auto_examples/plot_tuh_eeg_corpus.html
+++ /dev/null
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-<!DOCTYPE html>
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-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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-  <script>
-    document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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-<p class="admonition-title">Note</p>
-<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">here</span></a>
-to download the full example code</p>
-</div>
-<section class="sphx-glr-example-title" id="process-a-big-data-eeg-resource-tuh-eeg-corpus">
-<span id="sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"></span><h1>Process a big data EEG resource (TUH EEG Corpus)<a class="headerlink" href="#process-a-big-data-eeg-resource-tuh-eeg-corpus" title="Permalink to this heading">#</a></h1>
-<p>In this example, we showcase usage of the Temple University Hospital EEG Corpus
-(<a class="reference external" href="https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg">https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg</a>)
-including simple preprocessing steps as well as cutting of compute windows.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Lukas Gemein &lt;l.gemein@gmail.com&gt;</span>
-<span class="c1">#</span>
-<span class="c1"># License: BSD (3-clause)</span>
-
-<span class="kn">import</span> <span class="nn">tempfile</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">mne</span>
-
-<span class="kn">from</span> <span class="nn">braindecode.datasets</span> <span class="kn">import</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>
-<span class="kn">from</span> <span class="nn">braindecode.preprocessing</span> <span class="kn">import</span> <span class="p">(</span>
-    <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">,</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">,</span> <span class="n">scale</span> <span class="k">as</span> <a href="../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">multiply</span></a><span class="p">)</span>
-
-
-<span class="n">plt</span><span class="o">.</span><span class="n">style</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="s1">&#39;seaborn&#39;</span><span class="p">)</span>
-<a href="https://mne.tools/stable/generated/mne.set_log_level.html#mne.set_log_level" title="mne.set_log_level" class="sphx-glr-backref-module-mne sphx-glr-backref-type-py-function"><span class="n">mne</span><span class="o">.</span><span class="n">set_log_level</span></a><span class="p">(</span><span class="s1">&#39;ERROR&#39;</span><span class="p">)</span>  <span class="c1"># avoid messages everytime a window is extracted</span>
-</pre></div>
-</div>
-<p>If you want to try this code with the actual data, please delete the next
-section. We are required to mock some dataset functionality, since the data
-is not available at creation time of this example.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">braindecode.datasets.tuh</span> <span class="kn">import</span> <span class="n">_TUHMock</span> <span class="k">as</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a>  <span class="c1"># noqa F811</span>
-</pre></div>
-</div>
-<p>We start by creating a TUH dataset. First, the class generates a description
-of the recordings in <cite>TUH_PATH</cite> (which is later accessible as
-<cite>tuh.description</cite>) without actually touching the files. This will parse
-information from file paths such as patient id, recording data, etc and should
-be really fast. Afterwards, the files are sorted chronologically by year,
-month, day, patient id, recording session and segment.
-In the following, a subset of the description corresponding to <cite>recording_ids</cite>
-is used.
-Afterwards, the files will be iterated a second time, slower than before.
-The files are now actually touched. Additional information about subjects
-like age and gender are parsed directly from the EDF file header. If existent,
-the physician report is added to the description. Furthermore, the recordings
-are read with <cite>mne.io.read_raw_edf</cite> with <cite>preload=False</cite>. Finally, we will get
-a <cite>BaseConcatDataset</cite> of <cite>BaseDatasets</cite> each holding a single
-<cite>nme.io.Raw</cite> which is fully compatible with other braindecode functionalities.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a> <span class="o">=</span> <span class="s1">&#39;please insert actual path to data here&#39;</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># specify the number of jobs for loading and windowing</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="p">(</span>
-    <span class="n">path</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TUH_PATH</span></a><span class="p">,</span>
-    <span class="n">recording_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">target_name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-    <span class="n">preload</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">add_physician_reports</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span> <span class="k">if</span> <a href="../generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.TUH" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class"><span class="n">TUH</span></a><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;_TUHMock&#39;</span> <span class="k">else</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>  <span class="c1"># Mock dataset can&#39;t</span>
-    <span class="c1"># be loaded in parallel</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>We can easily create descriptive statistics using the description <cite>DataFrame</cite>,
-for example an age histogram split by gender of patients.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="matplotlib.figure.Figure" class="sphx-glr-backref-module-matplotlib-figure sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">fig</span></a><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">genders</span></a> <span class="o">=</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">gender</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-<a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a> <span class="o">=</span> <span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">age</span><span class="p">[</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="o">.</span><span class="n">description</span><span class="o">.</span><span class="n">gender</span> <span class="o">==</span> <span class="n">g</span><span class="p">]</span> <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">genders</span></a><span class="p">]</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.hist.html#matplotlib.axes.Axes.hist" title="matplotlib.axes.Axes.hist" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span>
-    <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span>
-    <span class="n">stacked</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-    <span class="n">bins</span><span class="o">=</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.arange.html#numpy.arange" title="numpy.arange" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">),</span>
-    <span class="n">alpha</span><span class="o">=</span><span class="mf">.5</span><span class="p">,</span>
-<span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html#matplotlib.axes.Axes.legend" title="matplotlib.axes.Axes.legend" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">legend</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">genders</span></a><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html#matplotlib.axes.Axes.set_xlabel" title="matplotlib.axes.Axes.set_xlabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span></a><span class="p">(</span><span class="s1">&#39;Age [years]&#39;</span><span class="p">)</span>
-<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html#matplotlib.axes.Axes.set_ylabel" title="matplotlib.axes.Axes.set_ylabel" class="sphx-glr-backref-module-matplotlib-axes sphx-glr-backref-type-py-method"><span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span></a><span class="p">(</span><span class="s1">&#39;Count&#39;</span><span class="p">)</span>
-</pre></div>
-</div>
-<img src="../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" srcset="../_images/sphx_glr_plot_tuh_eeg_corpus_001.png" alt="plot tuh eeg corpus" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Text(152.97222222222223, 0.5, &#39;Count&#39;)
-</pre></div>
-</div>
-<p>Next, we will perform some preprocessing steps. First, we will do some
-selection of available recordings based on the duration. We will select those
-recordings, that have at least five minutes duration. Data is not loaded here.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">select_by_duration</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/functions.html#float" title="builtins.float" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">inf</span></a>
-    <span class="c1"># determine length of the recordings and select based on tmin and tmax</span>
-    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">d_i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
-        <span class="n">duration</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">/</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span>
-        <span class="k">if</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">&lt;=</span> <span class="n">duration</span> <span class="o">&lt;=</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">:</span>
-            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">d_i</span><span class="p">)</span>
-    <span class="n">splits</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)</span>
-    <span class="n">split</span> <span class="o">=</span> <span class="n">splits</span><span class="p">[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">split</span>
-
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">5</span> <span class="o">*</span> <span class="mi">60</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="kc">None</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_duration</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>Next, we will discard all recordings that have an incomplete channel
-configuration (wrt the channels that we are interested in, i.e. the 21
-channels of the international 10-20-placement). The dataset is subdivided into
-recordings with ‘le’ and ‘ar’ reference which we will have to consider. Data
-is not loaded here.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
-    <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;A2&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;FP1&#39;</span><span class="p">,</span> <span class="s1">&#39;FP2&#39;</span><span class="p">,</span> <span class="s1">&#39;F3&#39;</span><span class="p">,</span> <span class="s1">&#39;F4&#39;</span><span class="p">,</span> <span class="s1">&#39;C3&#39;</span><span class="p">,</span> <span class="s1">&#39;C4&#39;</span><span class="p">,</span> <span class="s1">&#39;P3&#39;</span><span class="p">,</span> <span class="s1">&#39;P4&#39;</span><span class="p">,</span> <span class="s1">&#39;O1&#39;</span><span class="p">,</span> <span class="s1">&#39;O2&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;F7&#39;</span><span class="p">,</span> <span class="s1">&#39;F8&#39;</span><span class="p">,</span> <span class="s1">&#39;T3&#39;</span><span class="p">,</span> <span class="s1">&#39;T4&#39;</span><span class="p">,</span> <span class="s1">&#39;T5&#39;</span><span class="p">,</span> <span class="s1">&#39;T6&#39;</span><span class="p">,</span> <span class="s1">&#39;FZ&#39;</span><span class="p">,</span> <span class="s1">&#39;CZ&#39;</span><span class="p">,</span> <span class="s1">&#39;PZ&#39;</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
-    <span class="s1">&#39;EEG A1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-REF&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG FP1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-REF&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG C4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-REF&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG F7-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-REF&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG T6-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-REF&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-REF&#39;</span><span class="p">])</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([</span>
-    <span class="s1">&#39;EEG A1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG A2-LE&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG FP1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FP2-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG C3-LE&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG C4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG P4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O1-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG O2-LE&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG F7-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG F8-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T3-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T4-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG T5-LE&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;EEG T6-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG FZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG CZ-LE&#39;</span><span class="p">,</span> <span class="s1">&#39;EEG PZ-LE&#39;</span><span class="p">])</span>
-<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">)</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="n">ch_name</span><span class="p">:</span> <span class="n">short_ch_name</span> <span class="k">for</span> <span class="n">ch_name</span><span class="p">,</span> <span class="n">short_ch_name</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_names</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">)}</span>
-<a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ar&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ar_ch_mapping</span></a><span class="p">,</span> <span class="s1">&#39;le&#39;</span><span class="p">:</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">le_ch_mapping</span></a><span class="p">}</span>
-
-
-<span class="k">def</span> <span class="nf">select_by_channels</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">):</span>
-    <span class="n">split_ids</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">datasets</span><span class="p">):</span>
-        <span class="n">ref</span> <span class="o">=</span> <span class="s1">&#39;ar&#39;</span> <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;-REF&#39;</span><span class="p">)</span> <span class="k">else</span> <span class="s1">&#39;le&#39;</span>
-        <span class="c1"># these are the channels we are looking for</span>
-        <span class="n">seta</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">[</span><span class="n">ref</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="c1"># these are the channels of the recoding</span>
-        <span class="n">setb</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">)</span>
-        <span class="c1"># if recording contains all channels we are looking for, include it</span>
-        <span class="k">if</span> <span class="n">seta</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">setb</span><span class="p">):</span>
-            <span class="n">split_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">ds</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">split_ids</span><span class="p">)[</span><span class="s1">&#39;0&#39;</span><span class="p">]</span>
-
-
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a> <span class="o">=</span> <span class="n">select_by_channels</span><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">)</span>
-</pre></div>
-</div>
-<p>Next, we will chain several preprocessing steps that are realized through
-<cite>mne</cite>. Data will be loaded by the first preprocessor that has a mention of it
-in brackets:</p>
-<ol class="arabic simple">
-<li><p>crop the recordings to a region of interest</p></li>
-<li><p>re-reference all recordings to ‘ar’ (requires load)</p></li>
-<li><p>rename channels to short channel names</p></li>
-<li><p>pick channels of interest</p></li>
-<li><p>scale signals to micro volts (requires load)</p></li>
-<li><p>clip outlier values to +/- 800 micro volts (requires load)</p></li>
-<li><p>resample recordings to a common frequency (requires load)</p></li>
-</ol>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">custom_rename_channels</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <span class="n">mapping</span><span class="p">):</span>
-    <span class="c1"># rename channels which are dependent on referencing:</span>
-    <span class="c1"># le: EEG 01-LE, ar: EEG 01-REF</span>
-    <span class="c1"># mne fails if the mapping contains channels as keys that are not present</span>
-    <span class="c1"># in the raw</span>
-    <span class="n">reference</span> <span class="o">=</span> <span class="n">raw</span><span class="o">.</span><span class="n">ch_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;-&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="n">reference</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;le&#39;</span><span class="p">,</span> <span class="s1">&#39;ref&#39;</span><span class="p">],</span> <span class="s1">&#39;unexpected referencing&#39;</span>
-    <span class="n">reference</span> <span class="o">=</span> <span class="s1">&#39;le&#39;</span> <span class="k">if</span> <span class="n">reference</span> <span class="o">==</span> <span class="s1">&#39;le&#39;</span> <span class="k">else</span> <span class="s1">&#39;ar&#39;</span>
-    <span class="n">raw</span><span class="o">.</span><span class="n">rename_channels</span><span class="p">(</span><span class="n">mapping</span><span class="p">[</span><span class="n">reference</span><span class="p">])</span>
-
-
-<span class="k">def</span> <span class="nf">custom_crop</span><span class="p">(</span><span class="n">raw</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-    <span class="c1"># crop recordings to tmin – tmax. can be incomplete if recording</span>
-    <span class="c1"># has lower duration than tmax</span>
-    <span class="c1"># by default mne fails if tmax is bigger than duration</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="nb">min</span><span class="p">((</span><span class="n">raw</span><span class="o">.</span><span class="n">n_times</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">raw</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">],</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">)</span>
-    <span class="n">raw</span><span class="o">.</span><span class="n">crop</span><span class="p">(</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="n">include_tmax</span><span class="p">)</span>
-
-
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a> <span class="o">=</span> <span class="mi">1</span> <span class="o">*</span> <span class="mi">60</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">60</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a> <span class="o">=</span> <span class="mi">100</span>
-
-<a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a> <span class="o">=</span> <span class="p">[</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_crop</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmin</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tmax</span></a><span class="p">,</span> <span class="n">include_tmax</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;set_eeg_reference&#39;</span><span class="p">,</span> <span class="n">ref_channels</span><span class="o">=</span><span class="s1">&#39;average&#39;</span><span class="p">,</span> <span class="n">ch_type</span><span class="o">=</span><span class="s1">&#39;eeg&#39;</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="n">custom_rename_channels</span><span class="p">,</span> <span class="n">mapping</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ch_mapping</span></a><span class="p">,</span>
-                 <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;pick_channels&#39;</span><span class="p">,</span> <span class="n">ch_names</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">short_ch_names</span></a><span class="p">,</span> <span class="n">ordered</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="../generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">multiply</span></a><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">1e6</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.clip.html#numpy.clip" title="numpy.clip" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">clip</span></a><span class="p">,</span> <span class="n">a_min</span><span class="o">=-</span><span class="mi">800</span><span class="p">,</span> <span class="n">a_max</span><span class="o">=</span><span class="mi">800</span><span class="p">,</span> <span class="n">apply_on_array</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;resample&#39;</span><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">sfreq</span></a><span class="p">),</span>
-<span class="p">]</span>
-</pre></div>
-</div>
-<p>Next, we apply the preprocessors on the selected recordings in parallel.
-We additionally use the serialization functionality of
-<a class="reference internal" href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> to limit memory usage during
-preprocessing (as each file must be loaded into memory for some of the
-preprocessing steps to work). This also makes it possible to use the lazy
-loading capabilities of <a class="reference internal" href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a>, as
-the preprocessed data is automatically reloaded with <code class="docutils literal notranslate"><span class="pre">preload=False</span></code>.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>Here we use <code class="docutils literal notranslate"><span class="pre">n_jobs=2</span></code> as the machines the documentation is build on
-only have two cores. This number should be modified based on the machine
-that is available for preprocessing.</p>
-</div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a> <span class="o">=</span> <a href="https://docs.python.org/3/library/tempfile.html#tempfile.mkdtemp" title="tempfile.mkdtemp" class="sphx-glr-backref-module-tempfile sphx-glr-backref-type-py-function"><span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span></a><span class="p">()</span>  <span class="c1"># please insert actual output directory here</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span>
-    <span class="n">concat_ds</span><span class="o">=</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">preprocessors</span></a><span class="p">,</span>
-    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
-    <span class="n">save_dir</span><span class="o">=</span><a href="https://docs.python.org/3/library/stdtypes.html#str" title="builtins.str" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OUT_PATH</span></a>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>We can finally generate compute windows. The resulting dataset is now ready
-to be used for model training.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a> <span class="o">=</span> <span class="mi">1000</span>
-<span class="c1"># generate compute windows here and store them to disk</span>
-<a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a> <span class="o">=</span> <a href="../generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">create_fixed_length_windows</span></a><span class="p">(</span>
-    <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_preproc</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_size_samples</span></a><span class="p">,</span>
-    <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">window_stride_samples</span></a><span class="p">,</span>
-    <span class="n">drop_last_window</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">n_jobs</span><span class="o">=</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">N_JOBS</span></a><span class="p">,</span>
-<span class="p">)</span>
-
-<span class="k">for</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">x</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">y</span></a><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#list" title="builtins.list" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ind</span></a> <span class="ow">in</span> <a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">tuh_windows</span></a><span class="p">:</span>
-    <span class="k">break</span>
-</pre></div>
-</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.234 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  9 MB</p>
-<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-tuh-eeg-corpus-py">
-<div class="sphx-glr-download sphx-glr-download-python docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/e0deebe7e74ab8d42ee491e92f30e023/plot_tuh_eeg_corpus.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tuh_eeg_corpus.py</span></code></a></p>
-</div>
-<div class="sphx-glr-download sphx-glr-download-jupyter docutils container">
-<p><a class="reference download internal" download="" href="../_downloads/d351841512f246981cc35aadb0422301/plot_tuh_eeg_corpus.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_tuh_eeg_corpus.ipynb</span></code></a></p>
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diff --git a/stable/auto_examples/sg_execution_times.html b/stable/auto_examples/sg_execution_times.html
deleted file mode 100644
index 0842d4d8..00000000
--- a/stable/auto_examples/sg_execution_times.html
+++ /dev/null
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-
-<!DOCTYPE html>
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-<html lang="en">
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-  <section id="computation-times">
-<span id="sphx-glr-auto-examples-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
-<p><strong>22:25.452</strong> total execution time for <strong>auto_examples</strong> files:</p>
-<table class="table">
-<colgroup>
-<col style="width: 86%" />
-<col style="width: 7%" />
-<col style="width: 7%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code>)</p></td>
-<td><p>05:43.141</p></td>
-<td><p>1883.1 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_how_train_test_and_tune.py</span></code>)</p></td>
-<td><p>03:41.659</p></td>
-<td><p>767.8 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation_search.py</span></code>)</p></td>
-<td><p>03:29.500</p></td>
-<td><p>729.4 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_hyperparameter_tuning_with_scikit-learn.py</span></code>)</p></td>
-<td><p>02:08.708</p></td>
-<td><p>506.4 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_benchmark_preprocessing.py</span></code>)</p></td>
-<td><p>01:42.156</p></td>
-<td><p>441.0 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_cropped.py</span></code>)</p></td>
-<td><p>01:41.173</p></td>
-<td><p>864.1 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_relative_positioning.py</span></code>)</p></td>
-<td><p>00:54.959</p></td>
-<td><p>149.0 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code>)</p></td>
-<td><p>00:41.558</p></td>
-<td><p>771.6 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_data_augmentation.py</span></code>)</p></td>
-<td><p>00:40.723</p></td>
-<td><p>1110.8 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_chambon2018.py</span></code>)</p></td>
-<td><p>00:28.682</p></td>
-<td><p>9.1 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_bcic_iv_4_ecog_cropped.py</span></code>)</p></td>
-<td><p>00:25.332</p></td>
-<td><p>1059.0 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_usleep.py</span></code>)</p></td>
-<td><p>00:12.305</p></td>
-<td><p>314.2 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sleep_staging_eldele2021.py</span></code>)</p></td>
-<td><p>00:11.833</p></td>
-<td><p>16.1 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dataset_example.py</span></code>)</p></td>
-<td><p>00:08.020</p></td>
-<td><p>370.9 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_load_save_datasets.py</span></code>)</p></td>
-<td><p>00:04.616</p></td>
-<td><p>406.8 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_split_dataset.html#sphx-glr-auto-examples-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_split_dataset.py</span></code>)</p></td>
-<td><p>00:03.982</p></td>
-<td><p>333.4 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_eeg_corpus.py</span></code>)</p></td>
-<td><p>00:02.234</p></td>
-<td><p>8.6 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_regression.py</span></code>)</p></td>
-<td><p>00:01.504</p></td>
-<td><p>20.3 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tuh_discrete_multitarget.py</span></code>)</p></td>
-<td><p>00:01.293</p></td>
-<td><p>17.7 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_custom_dataset_example.html#sphx-glr-auto-examples-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_dataset_example.py</span></code>)</p></td>
-<td><p>00:01.164</p></td>
-<td><p>96.3 MB</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_mne_dataset_example.html#sphx-glr-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mne_dataset_example.py</span></code>)</p></td>
-<td><p>00:00.910</p></td>
-<td><p>79.4 MB</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a> (<code class="docutils literal notranslate"><span class="pre">benchmark_lazy_eager_loading.py</span></code>)</p></td>
-<td><p>00:00.000</p></td>
-<td><p>0.0 MB</p></td>
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+              
               
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+  
   <section id="how-to-cite-braindecode">
-<span id="cite"></span><h1>How to cite Braindecode<a class="headerlink" href="#how-to-cite-braindecode" title="Permalink to this heading">#</a></h1>
+<span id="cite"></span><h1>How to cite Braindecode<a class="headerlink" href="#how-to-cite-braindecode" title="Link to this heading">#</a></h1>
 <section id="citing-the-braindecode">
-<h2>Citing the Braindecode<a class="headerlink" href="#citing-the-braindecode" title="Permalink to this heading">#</a></h2>
+<h2>Citing the Braindecode<a class="headerlink" href="#citing-the-braindecode" title="Link to this heading">#</a></h2>
 <p>If you are using the library in a scientific context, to cite the braindecode software,
 you can use the canonical journal article reference</p>
-<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="w"> </span><span class="p">{</span><span class="nl">HBM:HBM23730</span><span class="p">,</span><span class="w"></span>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="w"> </span><span class="p">{</span><span class="nl">HBM:HBM23730</span><span class="p">,</span>
 <span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,</span>
 <span class="s">  Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and</span>
-<span class="s">  Hutter, Frank and Burgard, Wolfram and Ball, Tonio}</span><span class="p">,</span><span class="w"></span>
-<span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Deep learning with convolutional neural networks for EEG decoding and visualization}</span><span class="p">,</span><span class="w"></span>
-<span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Human Brain Mapping}</span><span class="p">,</span><span class="w"></span>
-<span class="na">issn</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1097-0193}</span><span class="p">,</span><span class="w"></span>
-<span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{http://dx.doi.org/10.1002/hbm.23730}</span><span class="p">,</span><span class="w"></span>
-<span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.1002/hbm.23730}</span><span class="p">,</span><span class="w"></span>
-<span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{aug}</span><span class="p">,</span><span class="w"></span>
-<span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2017}</span><span class="p">,</span><span class="w"></span>
+<span class="s">  Hutter, Frank and Burgard, Wolfram and Ball, Tonio}</span><span class="p">,</span>
+<span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Deep learning with convolutional neural networks for EEG decoding and visualization}</span><span class="p">,</span>
+<span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Human Brain Mapping}</span><span class="p">,</span>
+<span class="na">issn</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{1097-0193}</span><span class="p">,</span>
+<span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{http://dx.doi.org/10.1002/hbm.23730}</span><span class="p">,</span>
+<span class="na">doi</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{10.1002/hbm.23730}</span><span class="p">,</span>
+<span class="na">month</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{aug}</span><span class="p">,</span>
+<span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2017}</span><span class="p">,</span>
 <span class="na">keywords</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,</span>
-<span class="s">  brain–computer interface, model interpretability, brain mapping}</span><span class="p">,</span><span class="w"></span>
-<span class="p">}</span><span class="w"></span>
+<span class="s">  brain–computer interface, model interpretability, brain mapping}</span><span class="p">,</span>
+<span class="p">}</span>
 </pre></div>
 </div>
 </section>
 <section id="citing-the-mne">
-<h2>Citing the MNE<a class="headerlink" href="#citing-the-mne" title="Permalink to this heading">#</a></h2>
+<h2>Citing the MNE<a class="headerlink" href="#citing-the-mne" title="Link to this heading">#</a></h2>
 <p>Braindecode is built on top of the <a class="reference external" href="https://mne.tools">MNE-Python</a> software.
 you can use the DOIs provided by <a class="reference external" href="https://doi.org/10.5281/zenodo.592483">Zenodo</a>.
 Additionally, we also ask that when citing the MNE-Python package,
 you cite the canonical journal article reference below:</p>
-<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="p">{</span><span class="nl">10.3389/fnins.2013.00267</span><span class="p">,</span><span class="w"></span>
+<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@article</span><span class="p">{</span><span class="nl">10.3389/fnins.2013.00267</span><span class="p">,</span>
 <span class="na">author</span><span class="p">=</span><span class="s">{Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann,</span>
 <span class="s">        Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas,</span>
-<span class="s">        Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti}</span><span class="p">,</span><span class="w"></span>
-<span class="na">title</span><span class="p">=</span><span class="s">{{MEG and EEG data analysis with MNE-Python}}</span><span class="p">,</span><span class="w"></span>
-<span class="na">journal</span><span class="p">=</span><span class="s">{Frontiers in Neuroscience}</span><span class="p">,</span><span class="w"></span>
-<span class="na">volume</span><span class="p">=</span><span class="s">{7}</span><span class="p">,</span><span class="w"></span>
-<span class="na">pages</span><span class="p">=</span><span class="s">{267}</span><span class="p">,</span><span class="w"></span>
-<span class="na">year</span><span class="p">=</span><span class="s">{2013}</span><span class="p">,</span><span class="w"></span>
-<span class="na">url</span><span class="p">=</span><span class="s">{https://www.frontiersin.org/article/10.3389/fnins.2013.00267}</span><span class="p">,</span><span class="w"></span>
-<span class="na">doi</span><span class="p">=</span><span class="s">{10.3389/fnins.2013.00267}</span><span class="p">,</span><span class="w"></span>
-<span class="na">issn</span><span class="p">=</span><span class="s">{1662-453X}</span><span class="p">,</span><span class="w"></span>
-<span class="p">}</span><span class="w"></span>
+<span class="s">        Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti}</span><span class="p">,</span>
+<span class="na">title</span><span class="p">=</span><span class="s">{{MEG and EEG data analysis with MNE-Python}}</span><span class="p">,</span>
+<span class="na">journal</span><span class="p">=</span><span class="s">{Frontiers in Neuroscience}</span><span class="p">,</span>
+<span class="na">volume</span><span class="p">=</span><span class="s">{7}</span><span class="p">,</span>
+<span class="na">pages</span><span class="p">=</span><span class="s">{267}</span><span class="p">,</span>
+<span class="na">year</span><span class="p">=</span><span class="s">{2013}</span><span class="p">,</span>
+<span class="na">url</span><span class="p">=</span><span class="s">{https://www.frontiersin.org/article/10.3389/fnins.2013.00267}</span><span class="p">,</span>
+<span class="na">doi</span><span class="p">=</span><span class="s">{10.3389/fnins.2013.00267}</span><span class="p">,</span>
+<span class="na">issn</span><span class="p">=</span><span class="s">{1662-453X}</span><span class="p">,</span>
+<span class="p">}</span>
 </pre></div>
 </div>
 </section>
 <section id="citing-other-algorithms">
-<h2>Citing other algorithms<a class="headerlink" href="#citing-other-algorithms" title="Permalink to this heading">#</a></h2>
+<h2>Citing other algorithms<a class="headerlink" href="#citing-other-algorithms" title="Link to this heading">#</a></h2>
 <p>Depending on your research topic, it may also be appropriate to cite related
 method papers, some of which are listed in the documentation strings of the
 relevant functions or methods. We recommend always check the documentation
@@ -417,108 +503,95 @@ <h2>Citing other algorithms<a class="headerlink" href="#citing-other-algorithms"
 </section>
 
 
-            </article>
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- <li class="toc-h2 nav-item toc-entry">
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-   Citing the MNE
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-   Citing other algorithms
-  </a>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-the-braindecode">Citing the Braindecode</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-the-mne">Citing the MNE</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citing-other-algorithms">Citing other algorithms</a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,169 +530,177 @@
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   <section id="braindecode-augmentation-augmenteddataloader">
-<h1>braindecode.augmentation.AugmentedDataLoader<a class="headerlink" href="#braindecode-augmentation-augmenteddataloader" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.AugmentedDataLoader<a class="headerlink" href="#braindecode-augmentation-augmenteddataloader" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.AugmentedDataLoader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#AugmentedDataLoader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">AugmentedDataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L186-L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.AugmentedDataLoader" title="Link to this definition">#</a></dt>
 <dd><p>A base dataloader class customized to applying augmentation Transforms.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.BaseDataset.html#braindecode.datasets.BaseDataset" title="braindecode.datasets.BaseDataset"><em>BaseDataset</em></a>) – The dataset containing the signals.</p></li>
-<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><a class="reference internal" href="braindecode.augmentation.Transform.html#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><em>Transform</em></a><em>, </em><em>optional</em>) – Transform or sequence of Transform to be applied to each batch.</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.12)"><em>torch.device</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Device on which to transform the data. Defaults to None.</p></li>
-<li><p><strong>**kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>, </em><em>optional</em>) – keyword arguments to pass to standard DataLoader class.</p></li>
+<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference internal" href="braindecode.augmentation.Transform.html#braindecode.augmentation.Transform" title="braindecode.augmentation.Transform"><em>Transform</em></a><em>, </em><em>optional</em>) – Transform or sequence of Transform to be applied to each batch.</p></li>
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Device on which to transform the data. Defaults to None.</p></li>
+<li><p><strong>**kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – keyword arguments to pass to standard DataLoader class.</p></li>
 </ul>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-augmenteddataloader">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.AugmentedDataLoader</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-augmenteddataloader" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.AugmentedDataLoader</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-augmenteddataloader" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
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-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,83 +530,111 @@
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   <section id="braindecode-augmentation-bandstopfilter">
-<h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindecode-augmentation-bandstopfilter" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindecode-augmentation-bandstopfilter" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">BandstopFilter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#BandstopFilter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">BandstopFilter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L516-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter" title="Link to this definition">#</a></dt>
 <dd><p>Apply a band-stop filter with desired bandwidth at a randomly selected
 frequency position between 0 and <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r4ef33a8aa142-1" id="id1">[1]</a> and <a class="reference internal" href="#r4ef33a8aa142-2" id="id2">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
 frequencies.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</p></li>
-<li><p><strong>max_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Maximal admissible frequency. The low cut frequency will be sampled so
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Sampling frequency of the signals to be filtered. Defaults to 100 Hz.</p></li>
+<li><p><strong>max_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Maximal admissible frequency. The low cut frequency will be sampled so
 that the corresponding high cut frequency + transition (=1Hz) are below
 <code class="docutils literal notranslate"><span class="pre">max_freq</span></code>. If omitted or <cite>None</cite>, will default to the Nyquist
 frequency (<code class="docutils literal notranslate"><span class="pre">sfreq</span> <span class="pre">/</span> <span class="pre">2</span></code>).</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r4ef33a8aa142-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r4ef33a8aa142-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r4ef33a8aa142-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r4ef33a8aa142-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#BandstopFilter.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L586-L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -561,25 +661,25 @@ <h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindec
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.BandstopFilter.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.BandstopFilter.operation" title="Link to this definition">#</a></dt>
 <dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
 position.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r4e2c3378db18-1" id="id3">[1]</a> and <a class="reference internal" href="#r4e2c3378db18-2" id="id4">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
-<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
 frequencies.</p></li>
 <li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
 frequency band to filter out for each sample in the batch. Frequencies
@@ -587,7 +687,7 @@ <h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindec
 <code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -596,18 +696,20 @@ <h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindec
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r4e2c3378db18-1"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r4e2c3378db18-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r4e2c3378db18-2"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r4e2c3378db18-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -615,122 +717,97 @@ <h1>braindecode.augmentation.BandstopFilter<a class="headerlink" href="#braindec
 <div style='clear:both'></div></section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     BandstopFilter
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       BandstopFilter.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       BandstopFilter.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter"><code class="docutils literal notranslate"><span class="pre">BandstopFilter</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.BandstopFilter.operation"><code class="docutils literal notranslate"><span class="pre">BandstopFilter.operation()</span></code></a></li>
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index 15b70e66..628706cf 100644
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+    <div class="navbar-header-items__end">
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+        <div class="navbar-item navbar-persistent--container">
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+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        <div class="navbar-item">
+<script>
+document.write(`
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+document.write(`
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
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+      Choose version  <!-- this text may get changed later by javascript -->
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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+</script></div>
       
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+ document.write(`
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+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
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+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
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+                    <li class="nav-item">
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+                    <li class="nav-item current active">
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+
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+document.write(`
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+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
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+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
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+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,73 +530,100 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-channelsdropout">
-<h1>braindecode.augmentation.ChannelsDropout<a class="headerlink" href="#braindecode-augmentation-channelsdropout" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.ChannelsDropout<a class="headerlink" href="#braindecode-augmentation-channelsdropout" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L154-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout" title="Link to this definition">#</a></dt>
 <dd><p>Randomly set channels to flat signal.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#ra3d0be2d719b-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>proba_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of dropping each channel.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>proba_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of dropping each channel.
 Defaults to 0.2.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument and to sample channels to erase. Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="ra3d0be2d719b-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="ra3d0be2d719b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsDropout.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L191-L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -541,28 +640,28 @@ <h1>braindecode.augmentation.ChannelsDropout<a class="headerlink" href="#brainde
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsDropout.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsDropout.operation" title="Link to this definition">#</a></dt>
 <dd><p>Randomly set channels to flat signal.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r40820df5a429-1" id="id2">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -571,152 +670,119 @@ <h1>braindecode.augmentation.ChannelsDropout<a class="headerlink" href="#brainde
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r40820df5a429-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r40820df5a429-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-channelsdropout">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-channelsdropout" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-channelsdropout" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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-      <span class="pre">
-       ChannelsDropout.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       ChannelsDropout.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-channelsdropout">
-   Examples using
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     braindecode.augmentation.ChannelsDropout
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsDropout.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsDropout.operation()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-channelsdropout">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.ChannelsShuffle.html b/stable/generated/braindecode.augmentation.ChannelsShuffle.html
index 79977457..b62c2640 100644
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+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,47 +530,73 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
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   <section id="braindecode-augmentation-channelsshuffle">
-<h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#braindecode-augmentation-channelsshuffle" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#braindecode-augmentation-channelsshuffle" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L218-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle" title="Link to this definition">#</a></dt>
 <dd><p>Randomly shuffle channels in EEG data matrix.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r021aa678995d-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of including the channel
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em><em>, </em><em>optional</em>) – Float between 0 and 1 setting the probability of including the channel
 in the set of permuted channels. Defaults to 0.2.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument, to sample which channels to shuffle and to carry the shuffle.
 Defaults to None.</p></li>
@@ -506,26 +604,27 @@ <h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#brainde
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r021aa678995d-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r021aa678995d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsShuffle.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L256-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -542,30 +641,30 @@ <h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#brainde
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsShuffle.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsShuffle.operation" title="Link to this definition">#</a></dt>
 <dd><p>Randomly shuffle channels in EEG data matrix.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r97a313e9d2d3-1" id="id2">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
 in the set of permutted channels.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to sample which channels to shuffle and to carry the shuffle.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -574,13 +673,14 @@ <h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#brainde
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r97a313e9d2d3-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r97a313e9d2d3-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -588,122 +688,97 @@ <h1>braindecode.augmentation.ChannelsShuffle<a class="headerlink" href="#brainde
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     ChannelsShuffle
-    </span>
-   </code>
-  </a>
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       ChannelsShuffle.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       ChannelsShuffle.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsShuffle.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsShuffle.operation()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.ChannelsSymmetry.html b/stable/generated/braindecode.augmentation.ChannelsSymmetry.html
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,75 +530,102 @@
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   <section id="braindecode-augmentation-channelssymmetry">
-<h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braindecode-augmentation-channelssymmetry" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braindecode-augmentation-channelssymmetry" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsSymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsSymmetry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">ChannelsSymmetry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L351-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry" title="Link to this definition">#</a></dt>
 <dd><p>Permute EEG channels inverting left and right-side sensors.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r5999d4af6ac6-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – Ordered list of strings containing the names (in 10-20
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Ordered list of strings containing the names (in 10-20
 nomenclature) of the EEG channels that will be transformed. The
 first name should correspond the data in the first row of X, the
 second name in the second row and so on.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument. Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r5999d4af6ac6-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+<div role="list" class="citation-list">
+<div class="citation" id="r5999d4af6ac6-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
 (2018). HAMLET: interpretable human and machine co-learning technique.
 arXiv preprint arXiv:1803.09702.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#ChannelsSymmetry.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L410-L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -537,26 +636,26 @@ <h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braind
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.ChannelsSymmetry.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.ChannelsSymmetry.operation" title="Link to this definition">#</a></dt>
 <dd><p>Permute EEG channels according to fixed permutation matrix.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#rae865eae5a1e-1" id="id2">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -565,13 +664,14 @@ <h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braind
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rae865eae5a1e-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
-<dd><p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+<div role="list" class="citation-list">
+<div class="citation" id="rae865eae5a1e-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
 (2018). HAMLET: interpretable human and machine co-learning technique.
 arXiv preprint arXiv:1803.09702.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -579,122 +679,97 @@ <h1>braindecode.augmentation.ChannelsSymmetry<a class="headerlink" href="#braind
 <div style='clear:both'></div></section>
 
 
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     <ul class="visible nav section-nav flex-column">
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     ChannelsSymmetry
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       ChannelsSymmetry.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       ChannelsSymmetry.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.ChannelsSymmetry.operation"><code class="docutils literal notranslate"><span class="pre">ChannelsSymmetry.operation()</span></code></a></li>
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-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
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+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
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+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
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+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
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+  
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+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
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+<form class="bd-search d-flex align-items-center"
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+      method="get">
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+         name="q"
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+         placeholder="Search the docs ..."
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+        <div class="navbar-item"><nav class="navbar-nav">
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+    Site Navigation
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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-</div>
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+  </div>
+`);
+</script></div>
       
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+    
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+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+  
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-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
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+                    <li class="nav-item">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
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+                    <li class="nav-item current active">
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+
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+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
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+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
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+      data-bs-toggle="dropdown"
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+      aria-label="Version switcher list"
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
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-
   
-  <div class="sidebar-start-items sidebar-primary__section">
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-    Section Navigation
-  </p>
-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,59 +530,85 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
-
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-</nav>
+</div>
+</nav></div>
     </div>
-  </div>
   
-
   
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+  
+  <div id="rtd-footer-container"></div>
+
 
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-      <main class="bd-main">
+      
+      <main id="main-content" class="bd-main">
         
         
-        <div class="bd-content">
-          <div class="bd-article-container">
-            
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   <section id="braindecode-augmentation-compose">
-<h1>braindecode.augmentation.Compose<a class="headerlink" href="#braindecode-augmentation-compose" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.Compose<a class="headerlink" href="#braindecode-augmentation-compose" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.Compose">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Compose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#Compose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Compose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L147-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose" title="Link to this definition">#</a></dt>
 <dd><p>Transform composition.</p>
 <p>Callable class allowing to cast a sequence of Transform objects into a
 single one.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – Sequence of Transforms to be composed.</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Sequence of Transforms to be composed.</p>
 </dd>
 </dl>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.Compose.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#Compose.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L163-L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Compose.forward" title="Link to this definition">#</a></dt>
 <dd><p>General forward pass for an augmentation transform.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
@@ -523,131 +621,105 @@ <h1>braindecode.augmentation.Compose<a class="headerlink" href="#braindecode-aug
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-compose">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Compose</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-compose" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Compose</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-compose" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
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-    </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Compose"><code class="docutils literal notranslate"><span class="pre">Compose</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Compose.forward"><code class="docutils literal notranslate"><span class="pre">Compose.forward()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-compose">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Compose</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.FTSurrogate.html b/stable/generated/braindecode.augmentation.FTSurrogate.html
index cba42902..ca5bbb06 100644
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-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,77 +530,104 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+  
   <section id="braindecode-augmentation-ftsurrogate">
-<h1>braindecode.augmentation.FTSurrogate<a class="headerlink" href="#braindecode-augmentation-ftsurrogate" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.FTSurrogate<a class="headerlink" href="#braindecode-augmentation-ftsurrogate" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FTSurrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#FTSurrogate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FTSurrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L78-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate" title="Link to this definition">#</a></dt>
 <dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#ra7c6c14d9bd9-1" id="id1">[1]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 setting the range over which the phase
-pertubation is uniformly sampled:
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
 <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">phase_noise_magnitude</span> <span class="pre">*</span> <span class="pre">2</span> <span class="pre">*</span> <span class="pre">pi]</span></code>. Defaults to 1.</p></li>
-<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to sample phase perturbations independently for each channel or
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to sample phase perturbations independently for each channel or
 not. It is advised to set it to False when spatial information is
 important for the task, like in BCI. Default False.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument. Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="ra7c6c14d9bd9-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+<div role="list" class="citation-list">
+<div class="citation" id="ra7c6c14d9bd9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
 Clifford, G. D. (2018). Addressing Class Imbalance in Classification
 Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
 preprint arXiv:1806.08675.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#FTSurrogate.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L127-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains:</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -544,33 +643,33 @@ <h1>braindecode.augmentation.FTSurrogate<a class="headerlink" href="#braindecode
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.FTSurrogate.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FTSurrogate.operation" title="Link to this definition">#</a></dt>
 <dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#r0a58a8f194ca-1" id="id2">[1]</a>.</p>
 <p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
 and modified.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
-pertubation is uniformly sampled:
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
 [0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
-<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
 not. It is advised to set it to False when spatial information is
 important for the task, like in BCI.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -579,153 +678,120 @@ <h1>braindecode.augmentation.FTSurrogate<a class="headerlink" href="#braindecode
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r0a58a8f194ca-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
-<dd><p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+<div role="list" class="citation-list">
+<div class="citation" id="r0a58a8f194ca-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
 Clifford, G. D. (2018). Addressing Class Imbalance in Classification
 Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
 preprint arXiv:1806.08675.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-ftsurrogate">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-ftsurrogate" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-ftsurrogate" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     FTSurrogate
-    </span>
-   </code>
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       FTSurrogate.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       FTSurrogate.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-ftsurrogate">
-   Examples using
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     braindecode.augmentation.FTSurrogate
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate"><code class="docutils literal notranslate"><span class="pre">FTSurrogate</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.FTSurrogate.operation"><code class="docutils literal notranslate"><span class="pre">FTSurrogate.operation()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-ftsurrogate">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.FrequencyShift.html b/stable/generated/braindecode.augmentation.FrequencyShift.html
index 6d42f0a3..ddfd8cea 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,48 +530,74 @@
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   <section id="braindecode-augmentation-frequencyshift">
-<h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindecode-augmentation-frequencyshift" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindecode-augmentation-frequencyshift" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FrequencyShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_delta_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#FrequencyShift"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">FrequencyShift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_delta_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L631-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift" title="Link to this definition">#</a></dt>
 <dd><p>Add a random shift in the frequency domain to all channels.</p>
 <p>Note that here, the shift is the same for all channels of a single example.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
-<li><p><strong>max_delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Maximum shift in Hz that can be sampled (in absolute value).
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>max_delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Maximum shift in Hz that can be sampled (in absolute value).
 Defaults to 2 (shift sampled between -2 and 2 Hz).</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
@@ -507,16 +605,16 @@ <h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindec
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#FrequencyShift.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L668-L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -532,27 +630,27 @@ <h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindec
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.FrequencyShift.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.FrequencyShift.operation" title="Link to this definition">#</a></dt>
 <dd><p>Adds a shift in the frequency domain to all channels.</p>
 <p>Note that here, the shift is the same for all channels of a single example.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -565,140 +663,106 @@ <h1>braindecode.augmentation.FrequencyShift<a class="headerlink" href="#braindec
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-frequencyshift">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.FrequencyShift</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-frequencyshift" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
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+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,82 +530,111 @@
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   <section id="braindecode-augmentation-gaussiannoise">
-<h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindecode-augmentation-gaussiannoise" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindecode-augmentation-gaussiannoise" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">GaussianNoise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#GaussianNoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">GaussianNoise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L283-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise" title="Link to this definition">#</a></dt>
 <dd><p>Randomly add white noise to all channels.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r8004aa1aead9-1" id="id1">[1]</a>, <a class="reference internal" href="#r8004aa1aead9-2" id="id2">[2]</a> and <a class="reference internal" href="#r8004aa1aead9-3" id="id3">[3]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation to use for the additive noise. Defaults to 0.1.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Standard deviation to use for the additive noise. Defaults to 0.1.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r8004aa1aead9-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+<div role="list" class="citation-list">
+<div class="citation" id="r8004aa1aead9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
 augmentation for eeg-based emotion recognition with deep convolutional
 neural networks. In International Conference on Multimedia Modeling
 (pp. 82-93).</p>
-</dd>
-<dt class="label" id="r8004aa1aead9-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+</div>
+<div class="citation" id="r8004aa1aead9-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r8004aa1aead9-3"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r8004aa1aead9-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#GaussianNoise.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L325-L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -549,28 +650,28 @@ <h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindeco
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.GaussianNoise.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.GaussianNoise.operation" title="Link to this definition">#</a></dt>
 <dd><p>Randomly add white Gaussian noise to all channels.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r0afcdefddcf0-1" id="id4">[1]</a>, <a class="reference internal" href="#r0afcdefddcf0-2" id="id5">[2]</a> and <a class="reference internal" href="#r0afcdefddcf0-3" id="id6">[3]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -579,24 +680,27 @@ <h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindeco
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r0afcdefddcf0-1"><span class="brackets"><a class="fn-backref" href="#id4">1</a></span></dt>
-<dd><p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+<div role="list" class="citation-list">
+<div class="citation" id="r0afcdefddcf0-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
 augmentation for eeg-based emotion recognition with deep convolutional
 neural networks. In International Conference on Multimedia Modeling
 (pp. 82-93).</p>
-</dd>
-<dt class="label" id="r0afcdefddcf0-2"><span class="brackets"><a class="fn-backref" href="#id5">2</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+</div>
+<div class="citation" id="r0afcdefddcf0-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id5">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r0afcdefddcf0-3"><span class="brackets"><a class="fn-backref" href="#id6">3</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r0afcdefddcf0-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id6">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -604,122 +708,97 @@ <h1>braindecode.augmentation.GaussianNoise<a class="headerlink" href="#braindeco
 <div style='clear:both'></div></section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     GaussianNoise
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       GaussianNoise.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       GaussianNoise.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise"><code class="docutils literal notranslate"><span class="pre">GaussianNoise</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.GaussianNoise.operation"><code class="docutils literal notranslate"><span class="pre">GaussianNoise.operation()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.IdentityTransform.html b/stable/generated/braindecode.augmentation.IdentityTransform.html
index 257f1c97..2f22bceb 100644
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+<form class="bd-search d-flex align-items-center"
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+        <div class="navbar-item"><nav class="navbar-nav">
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+     role="heading"
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+    Site Navigation
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+  <ul class="bd-navbar-elements navbar-nav">
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+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
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+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        </div>
       
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
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+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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-</div>
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+`);
+</script></div>
       
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+    
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-
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-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
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+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+    </nav>
+  
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+
   
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-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
                         Install
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-                
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+                      </a>
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
                         Tutorial and Examples
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+                    <li class="nav-item current active">
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+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
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+      <div class="sidebar-header-items__end">
+        
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+<script>
+document.write(`
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
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+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
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+      Choose version  <!-- this text may get changed later by javascript -->
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+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
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+        
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-  <div class="sidebar-start-items sidebar-primary__section">
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-    Section Navigation
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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   <section id="braindecode-augmentation-identitytransform">
-<h1>braindecode.augmentation.IdentityTransform<a class="headerlink" href="#braindecode-augmentation-identitytransform" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.IdentityTransform<a class="headerlink" href="#braindecode-augmentation-identitytransform" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">IdentityTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#IdentityTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">IdentityTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L139-L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform" title="Link to this definition">#</a></dt>
 <dd><p>Identity transform.</p>
 <p>Transform that does not change the input.</p>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.IdentityTransform.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.IdentityTransform.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.IdentityTransform.operation" title="Link to this definition">#</a></dt>
 <dd><p>Identity operation.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -519,113 +617,96 @@ <h1>braindecode.augmentation.IdentityTransform<a class="headerlink" href="#brain
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.IdentityTransform"><code class="docutils literal notranslate"><span class="pre">IdentityTransform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.IdentityTransform.operation"><code class="docutils literal notranslate"><span class="pre">IdentityTransform.operation()</span></code></a></li>
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-    Section Navigation
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,124 +530,154 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+          <div class="bd-content">
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   <section id="braindecode-augmentation-mixup">
-<h1>braindecode.augmentation.Mixup<a class="headerlink" href="#braindecode-augmentation-mixup" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.Mixup<a class="headerlink" href="#braindecode-augmentation-mixup" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.Mixup">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_per_sample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#Mixup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta_per_sample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1030-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup" title="Link to this definition">#</a></dt>
 <dd><p>Implements Iterator for Mixup for EEG data. See <a class="reference internal" href="#r8b127a6b4d44-1" id="id1">[1]</a>.
 Implementation based on <a class="reference internal" href="#r8b127a6b4d44-2" id="id2">[2]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Mixup hyperparameter.</p></li>
-<li><p><strong>beta_per_sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – By default, one mixing coefficient per batch is drawn from a beta
+<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Mixup hyperparameter.</p></li>
+<li><p><strong>beta_per_sample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – By default, one mixing coefficient per batch is drawn from a beta
 distribution. If True, one mixing coefficient per sample is drawn.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r8b127a6b4d44-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+<div role="list" class="citation-list">
+<div class="citation" id="r8b127a6b4d44-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
 (2018). mixup: Beyond Empirical Risk Minimization. In 2018
 International Conference on Learning Representations (ICLR)
 Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
-</dd>
-<dt class="label" id="r8b127a6b4d44-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
-</dd>
-</dl>
+</div>
+<div class="citation" id="r8b127a6b4d44-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.Mixup.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#Mixup.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L1068-L1106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>params</strong> – Contains the values sampled uniformly between 0 and 1 setting the
 linear interpolation between examples (lam) and the shuffled
 indices of examples that are mixed into original examples
 (idx_perm).</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.Mixup.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.Mixup.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Mixup.operation" title="Link to this definition">#</a></dt>
 <dd><p>Mixes two channels of EEG data.</p>
 <p>See <a class="reference internal" href="#r6a9a88664e19-1" id="id3">[1]</a> for details.
 Implementation based on <a class="reference internal" href="#r6a9a88664e19-2" id="id4">[2]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
-<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
 examples.</p></li>
-<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
 3 containing the labels of the two mixed channels and the mixing
 coefficient.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)">tuple</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r6a9a88664e19-1"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
-<dd><p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+<div role="list" class="citation-list">
+<div class="citation" id="r6a9a88664e19-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
 (2018). mixup: Beyond Empirical Risk Minimization. In 2018
 International Conference on Learning Representations (ICLR)
 Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
-</dd>
-<dt class="label" id="r6a9a88664e19-2"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
-<dd><p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
-</dd>
-</dl>
+</div>
+<div class="citation" id="r6a9a88664e19-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -583,122 +685,97 @@ <h1>braindecode.augmentation.Mixup<a class="headerlink" href="#braindecode-augme
 <div style='clear:both'></div></section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.Mixup">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     Mixup
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       Mixup.get_augmentation_params()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       Mixup.operation()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup"><code class="docutils literal notranslate"><span class="pre">Mixup</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Mixup.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Mixup.operation"><code class="docutils literal notranslate"><span class="pre">Mixup.operation()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.SensorsRotation.html b/stable/generated/braindecode.augmentation.SensorsRotation.html
index a7e7b3f7..42d7c0e2 100644
--- a/stable/generated/braindecode.augmentation.SensorsRotation.html
+++ b/stable/generated/braindecode.augmentation.SensorsRotation.html
@@ -1,362 +1,429 @@
 
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,46 +530,72 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
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   <section id="braindecode-augmentation-sensorsrotation">
-<h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#braindecode-augmentation-sensorsrotation" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#braindecode-augmentation-sensorsrotation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'z'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SensorsRotation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'z'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L733-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the desired axis
 with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
 <p>Suggested in <a class="reference internal" href="#r6bd6859a1fa8-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
 system. Should have shape (3, n_channels), where n_channels is the
 number of channels. Standard 10-20 positions can be obtained from
 <cite>mne</cite> through:</p>
@@ -508,38 +606,39 @@ <h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#brainde
 </div>
 </p></li>
 <li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em><em>, </em><em>optional</em>) – Axis around which to rotate. Defaults to ‘z’.</p></li>
-<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
 <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
 <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
 be used (as in the original paper). Defaults to True.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r6bd6859a1fa8-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="r6bd6859a1fa8-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SensorsRotation.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L809-L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains four elements:</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -572,24 +671,24 @@ <h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#brainde
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsRotation.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsRotation.operation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the desired axis
 with the desired angle.</p>
 <p>Suggested in <a class="reference internal" href="#rd4626e24d252-1" id="id2">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
 system. Should have shape (3, n_channels), where n_channels is the
 number of channels. Standard 10-20 positions can be obtained from
 <code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
@@ -602,21 +701,22 @@ <h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#brainde
 <li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
 <li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
 angles (in degrees) for each element of the input batch.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
 <cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
 used (as in the original paper).</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rd4626e24d252-1"><span class="brackets"><a class="fn-backref" href="#id2">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="rd4626e24d252-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
@@ -624,122 +724,97 @@ <h1>braindecode.augmentation.SensorsRotation<a class="headerlink" href="#brainde
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-      </span>
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- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SensorsRotation.operation"><code class="docutils literal notranslate"><span class="pre">SensorsRotation.operation()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.SensorsXRotation.html b/stable/generated/braindecode.augmentation.SensorsXRotation.html
index e15589f5..0f44234f 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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@@ -458,170 +530,188 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-sensorsxrotation">
-<h1>braindecode.augmentation.SensorsXRotation<a class="headerlink" href="#braindecode-augmentation-sensorsxrotation" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SensorsXRotation<a class="headerlink" href="#braindecode-augmentation-sensorsxrotation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsXRotation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsXRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SensorsXRotation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsXRotation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsXRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L974-L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsXRotation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the X axis
 with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
 <p>Suggested in <a class="reference internal" href="#r44460ea0e68a-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
 Has to be in standard 10-20 style. The order has to be consistent with
 the order of channels in the input matrices that will be fed to the
 transform. This channel will be used to compute approximate sensors
 positions from a standard 10-20 montage.</p></li>
-<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
 <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
 <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
 be used (as in the original paper). Defaults to True.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r44460ea0e68a-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="r44460ea0e68a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
-</dd>
-</dl>
+</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,170 +530,188 @@
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   <section id="braindecode-augmentation-sensorsyrotation">
-<h1>braindecode.augmentation.SensorsYRotation<a class="headerlink" href="#braindecode-augmentation-sensorsyrotation" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SensorsYRotation<a class="headerlink" href="#braindecode-augmentation-sensorsyrotation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsYRotation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsYRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SensorsYRotation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsYRotation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsYRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L918-L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsYRotation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the Y axis
 with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
 <p>Suggested in <a class="reference internal" href="#r05cec9135bb3-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
 Has to be in standard 10-20 style. The order has to be consistent with
 the order of channels in the input matrices that will be fed to the
 transform. This channel will be used to compute approximate sensors
 positions from a standard 10-20 montage.</p></li>
-<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
 <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
 <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
 be used (as in the original paper). Defaults to True.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r05cec9135bb3-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="r05cec9135bb3-1" role="doc-biblioentry">
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+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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@@ -458,170 +530,188 @@
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   <section id="braindecode-augmentation-sensorszrotation">
-<h1>braindecode.augmentation.SensorsZRotation<a class="headerlink" href="#braindecode-augmentation-sensorszrotation" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SensorsZRotation<a class="headerlink" href="#braindecode-augmentation-sensorszrotation" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SensorsZRotation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsZRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SensorsZRotation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsZRotation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SensorsZRotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ordered_ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_degrees</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L862-L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SensorsZRotation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the Z axis
 with an angle sampled uniformly between <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>.</p>
 <p>Suggested in <a class="reference internal" href="#r8d1fae5fc86d-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>ordered_ch_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of strings representing the channels of the montage considered.
 Has to be in standard 10-20 style. The order has to be consistent with
 the order of channels in the input matrices that will be fed to the
 transform. This channel will be used to compute approximate sensors
 positions from a standard 10-20 montage.</p></li>
-<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
+<li><p><strong>max_degree</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Maximum rotation. Rotation angles will be sampled between
 <code class="docutils literal notranslate"><span class="pre">-max_degree</span></code> and <code class="docutils literal notranslate"><span class="pre">max_degree</span></code>. Defaults to 15 degrees.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether to use spherical splines for the interpolation or not. When
 <code class="docutils literal notranslate"><span class="pre">False</span></code>, standard scipy.interpolate.Rbf (with quadratic kernel) will
 be used (as in the original paper). Defaults to True.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r8d1fae5fc86d-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="r8d1fae5fc86d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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@@ -458,44 +530,70 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-signflip">
-<h1>braindecode.augmentation.SignFlip<a class="headerlink" href="#braindecode-augmentation-signflip" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SignFlip<a class="headerlink" href="#braindecode-augmentation-signflip" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SignFlip">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SignFlip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SignFlip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SignFlip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L53-L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip" title="Link to this definition">#</a></dt>
 <dd><p>Flip the sign axis of each input with a given probability.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument. Defaults to None.</p></li>
 </ul>
@@ -504,16 +602,16 @@ <h1>braindecode.augmentation.SignFlip<a class="headerlink" href="#braindecode-au
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.SignFlip.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.SignFlip.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SignFlip.operation" title="Link to this definition">#</a></dt>
 <dd><p>Flip the sign axis of each input.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -526,131 +624,105 @@ <h1>braindecode.augmentation.SignFlip<a class="headerlink" href="#braindecode-au
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-signflip">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SignFlip</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-signflip" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SignFlip</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-signflip" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
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diff --git a/stable/generated/braindecode.augmentation.SmoothTimeMask.html b/stable/generated/braindecode.augmentation.SmoothTimeMask.html
index a5a97d09..658e452b 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,78 +530,106 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-smoothtimemask">
-<h1>braindecode.augmentation.SmoothTimeMask<a class="headerlink" href="#braindecode-augmentation-smoothtimemask" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.SmoothTimeMask<a class="headerlink" href="#braindecode-augmentation-smoothtimemask" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SmoothTimeMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SmoothTimeMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">SmoothTimeMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L431-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask" title="Link to this definition">#</a></dt>
 <dd><p>Smoothly replace a randomly chosen contiguous part of all channels by
 zeros.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#rdc48fbfa356b-1" id="id1">[1]</a> and <a class="reference internal" href="#rdc48fbfa356b-2" id="id2">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Number of consecutive samples to zero out. Will be ignored if
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em>, </em><em>optional</em>) – Number of consecutive samples to zero out. Will be ignored if
 magnitude is not set to None. Defaults to 100.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rdc48fbfa356b-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="rdc48fbfa356b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="rdc48fbfa356b-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="rdc48fbfa356b-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#SmoothTimeMask.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L476-L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd><p>Return transform parameters.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>tensor.Tensor</em>) – The data.</p></li>
 <li><p><strong>y</strong> (<em>tensor.Tensor</em>) – The labels.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><p><strong>params</strong> – Contains two elements:</p>
 <ul class="simple">
 <li><dl class="simple">
@@ -548,29 +648,29 @@ <h1>braindecode.augmentation.SmoothTimeMask<a class="headerlink" href="#braindec
 </ul>
 </p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.SmoothTimeMask.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.SmoothTimeMask.operation" title="Link to this definition">#</a></dt>
 <dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
 <p>Originally proposed in <a class="reference internal" href="#rbbea5cf64590-1" id="id3">[1]</a> and <a class="reference internal" href="#rbbea5cf64590-2" id="id4">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 <li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
 start masking the signal. Should have the same size as the first
 dimension of X (i.e. one start position per example in the batch).</p></li>
-<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -579,157 +679,125 @@ <h1>braindecode.augmentation.SmoothTimeMask<a class="headerlink" href="#braindec
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rbbea5cf64590-1"><span class="brackets"><a class="fn-backref" href="#id3">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="rbbea5cf64590-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="rbbea5cf64590-2"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="rbbea5cf64590-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-smoothtimemask">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SmoothTimeMask</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-smoothtimemask" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SmoothTimeMask</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-smoothtimemask" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask">
-   <code class="docutils literal notranslate">
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.operation">
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-      </span>
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-     braindecode.augmentation.SmoothTimeMask
-    </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.get_augmentation_params()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.SmoothTimeMask.operation"><code class="docutils literal notranslate"><span class="pre">SmoothTimeMask.operation()</span></code></a></li>
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+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-smoothtimemask">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.SmoothTimeMask</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.TimeReverse.html b/stable/generated/braindecode.augmentation.TimeReverse.html
index 3d887c75..3d21a16f 100644
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-    Section Navigation
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,44 +530,70 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+
 
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+          <div class="bd-content">
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+<div class="header-article-items header-article__inner">
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   <section id="braindecode-augmentation-timereverse">
-<h1>braindecode.augmentation.TimeReverse<a class="headerlink" href="#braindecode-augmentation-timereverse" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.TimeReverse<a class="headerlink" href="#braindecode-augmentation-timereverse" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">TimeReverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/transforms.html#TimeReverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">TimeReverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/transforms.py#L28-L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse" title="Link to this definition">#</a></dt>
 <dd><p>Flip the time axis of each input with a given probability.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float setting the probability of applying the operation.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument. Defaults to None.</p></li>
 </ul>
@@ -504,16 +602,16 @@ <h1>braindecode.augmentation.TimeReverse<a class="headerlink" href="#braindecode
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.TimeReverse.operation">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#braindecode.augmentation.TimeReverse.operation" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">operation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.TimeReverse.operation" title="Link to this definition">#</a></dt>
 <dd><p>Flip the time axis of each input.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -528,113 +626,96 @@ <h1>braindecode.augmentation.TimeReverse<a class="headerlink" href="#braindecode
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse">
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-     TimeReverse
-    </span>
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-  </a>
-  <ul class="nav section-nav flex-column">
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse.operation">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       TimeReverse.operation()
-      </span>
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-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse"><code class="docutils literal notranslate"><span class="pre">TimeReverse</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.TimeReverse.operation"><code class="docutils literal notranslate"><span class="pre">TimeReverse.operation()</span></code></a></li>
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-    Section Navigation
-  </p>
-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,48 +530,74 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
-
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+</nav></div>
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
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+  
+  <div id="rtd-footer-container"></div>
+
 
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+          <div class="bd-content">
+            <div class="bd-article-container">
               
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+<div class="header-article-items header-article__inner">
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+              
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+<div>
+  
   <section id="braindecode-augmentation-transform">
-<h1>braindecode.augmentation.Transform<a class="headerlink" href="#braindecode-augmentation-transform" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.Transform<a class="headerlink" href="#braindecode-augmentation-transform" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.augmentation.Transform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#Transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probability</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L36-L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform" title="Link to this definition">#</a></dt>
 <dd><p>Basic transform class used for implementing data augmentation
 operations.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>operation</strong> (<em>callable</em>) – A function taking arrays X, y (inputs and targets resp.) and
 other required arguments, and returning the transformed X and y.</p></li>
-<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 defining the uniform probability of applying the
+<li><p><strong>probability</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Float between 0 and 1 defining the uniform probability of applying the
 operation. Set to 1.0 by default (e.g always apply the operation).</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – Seed to be used to instatiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to decide whether or not to transform given the probability
 argument. Defaults to None.</p></li>
 </ul>
@@ -508,16 +606,16 @@ <h1>braindecode.augmentation.Transform<a class="headerlink" href="#braindecode-a
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.Transform.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><span class="pre">Tensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.10)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.10)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#Transform.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L71-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.forward" title="Link to this definition">#</a></dt>
 <dd><p>General forward pass for an augmentation transform.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><em>None</em>) – EEG labels for the example or batch. Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor, optional</em> – Transformed labels. Only returned when y is not None.</p></li>
@@ -529,149 +627,115 @@ <h1>braindecode.augmentation.Transform<a class="headerlink" href="#braindecode-a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.augmentation.Transform.get_augmentation_params">
-<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/base.html#Transform.get_augmentation_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.get_augmentation_params" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_augmentation_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/base.py#L68-L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.Transform.get_augmentation_params" title="Link to this definition">#</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-augmentation-transform">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Transform</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-transform" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Transform</span></code><a class="headerlink" href="#examples-using-braindecode-augmentation-transform" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.augmentation.Transform">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     Transform
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.Transform.forward">
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-       Transform.forward()
-      </span>
-     </code>
-    </a>
-   </li>
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-    <a class="reference internal nav-link" href="#braindecode.augmentation.Transform.get_augmentation_params">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       Transform.get_augmentation_params()
-      </span>
-     </code>
-    </a>
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-transform">
-   Examples using
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     braindecode.augmentation.Transform
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform"><code class="docutils literal notranslate"><span class="pre">Transform</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.forward"><code class="docutils literal notranslate"><span class="pre">Transform.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.Transform.get_augmentation_params"><code class="docutils literal notranslate"><span class="pre">Transform.get_augmentation_params()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-augmentation-transform">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.augmentation.Transform</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.functional.bandstop_filter.html b/stable/generated/braindecode.augmentation.functional.bandstop_filter.html
index 8a9d949b..a2d5ec05 100644
--- a/stable/generated/braindecode.augmentation.functional.bandstop_filter.html
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-    <title>braindecode.augmentation.functional.bandstop_filter &#8212; Braindecode 0.7 documentation</title>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-augmentation-functional-bandstop-filter">
-<h1>braindecode.augmentation.functional.bandstop_filter<a class="headerlink" href="#braindecode-augmentation-functional-bandstop-filter" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.bandstop_filter<a class="headerlink" href="#braindecode-augmentation-functional-bandstop-filter" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.bandstop_filter">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">bandstop_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#bandstop_filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.bandstop_filter" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">bandstop_filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_to_notch</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L408-L461"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.bandstop_filter" title="Link to this definition">#</a></dt>
 <dd><p>Apply a band-stop filter with desired bandwidth at the desired frequency
 position.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r8d0f6c52c5ba-1" id="id1">[1]</a> and <a class="reference internal" href="#r8d0f6c52c5ba-2" id="id2">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
-<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be filtered.</p></li>
+<li><p><strong>bandwidth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Bandwidth of the filter, i.e. distance between the low and high cut
 frequencies.</p></li>
 <li><p><strong>freqs_to_notch</strong> (<em>array-like</em><em> | </em><em>None</em>) – Array of floats of size <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the center of the
 frequency band to filter out for each sample in the batch. Frequencies
@@ -507,7 +605,7 @@ <h1>braindecode.augmentation.functional.bandstop_filter<a class="headerlink" hre
 <code class="docutils literal notranslate"><span class="pre">sfreq/2</span> <span class="pre">-</span> <span class="pre">bandwidth/2</span> <span class="pre">-</span> <span class="pre">transition</span></code> (where <code class="docutils literal notranslate"><span class="pre">transition</span> <span class="pre">=</span> <span class="pre">1</span> <span class="pre">Hz</span></code>).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -516,119 +614,112 @@ <h1>braindecode.augmentation.functional.bandstop_filter<a class="headerlink" hre
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r8d0f6c52c5ba-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r8d0f6c52c5ba-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r8d0f6c52c5ba-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r8d0f6c52c5ba-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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diff --git a/stable/generated/braindecode.augmentation.functional.channels_dropout.html b/stable/generated/braindecode.augmentation.functional.channels_dropout.html
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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   <section id="braindecode-augmentation-functional-channels-dropout">
-<h1>braindecode.augmentation.functional.channels_dropout<a class="headerlink" href="#braindecode-augmentation-functional-channels-dropout" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.channels_dropout<a class="headerlink" href="#braindecode-augmentation-functional-channels-dropout" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_dropout">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#channels_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_dropout" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_drop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L192-L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_dropout" title="Link to this definition">#</a></dt>
 <dd><p>Randomly set channels to flat signal.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#r993501bc6f56-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the probability of dropping each channel.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -511,114 +609,106 @@ <h1>braindecode.augmentation.functional.channels_dropout<a class="headerlink" hr
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r993501bc6f56-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="r993501bc6f56-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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-  <a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_dropout">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_dropout"><code class="docutils literal notranslate"><span class="pre">channels_dropout()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.functional.channels_permute.html b/stable/generated/braindecode.augmentation.functional.channels_permute.html
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
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   <section id="braindecode-augmentation-functional-channels-permute">
-<h1>braindecode.augmentation.functional.channels_permute<a class="headerlink" href="#braindecode-augmentation-functional-channels-permute" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.channels_permute<a class="headerlink" href="#braindecode-augmentation-functional-channels-permute" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_permute">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#channels_permute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_permute" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_permute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">permutation</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L333-L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_permute" title="Link to this definition">#</a></dt>
 <dd><p>Permute EEG channels according to fixed permutation matrix.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#rcdffecb1125b-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>permutation</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – List of integers defining the new channels order.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -509,114 +607,106 @@ <h1>braindecode.augmentation.functional.channels_permute<a class="headerlink" hr
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rcdffecb1125b-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
+<div role="list" class="citation-list">
+<div class="citation" id="rcdffecb1125b-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Deiss, O., Biswal, S., Jin, J., Sun, H., Westover, M. B., &amp; Sun, J.
 (2018). HAMLET: interpretable human and machine co-learning technique.
 arXiv preprint arXiv:1803.09702.</p>
-</dd>
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-  <a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_permute">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_permute"><code class="docutils literal notranslate"><span class="pre">channels_permute()</span></code></a></li>
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,53 +530,79 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+          <div class="bd-content">
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   <section id="braindecode-augmentation-functional-channels-shuffle">
-<h1>braindecode.augmentation.functional.channels_shuffle<a class="headerlink" href="#braindecode-augmentation-functional-channels-shuffle" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.channels_shuffle<a class="headerlink" href="#braindecode-augmentation-functional-channels-shuffle" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.channels_shuffle">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#channels_shuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_shuffle" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">channels_shuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L244-L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.channels_shuffle" title="Link to this definition">#</a></dt>
 <dd><p>Randomly shuffle channels in EEG data matrix.</p>
 <p>Part of the CMSAugment policy proposed in <a class="reference internal" href="#rc9211ddb2257-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>p_shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> | </em><em>None</em>) – Float between 0 and 1 setting the probability of including the channel
 in the set of permutted channels.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Used to sample which channels to shuffle and to carry the shuffle.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -513,114 +611,106 @@ <h1>braindecode.augmentation.functional.channels_shuffle<a class="headerlink" hr
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rc9211ddb2257-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="rc9211ddb2257-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Saeed, A., Grangier, D., Pietquin, O., &amp; Zeghidour, N. (2020).
 Learning from Heterogeneous EEG Signals with Differentiable Channel
 Reordering. arXiv preprint arXiv:2010.13694.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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-  <a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_shuffle">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.channels_shuffle"><code class="docutils literal notranslate"><span class="pre">channels_shuffle()</span></code></a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,50 +530,76 @@
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   <section id="braindecode-augmentation-functional-frequency-shift">
-<h1>braindecode.augmentation.functional.frequency_shift<a class="headerlink" href="#braindecode-augmentation-functional-frequency-shift" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.frequency_shift<a class="headerlink" href="#braindecode-augmentation-functional-frequency-shift" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.frequency_shift">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">frequency_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#frequency_shift"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.frequency_shift" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">frequency_shift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L508-L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.frequency_shift" title="Link to this definition">#</a></dt>
 <dd><p>Adds a shift in the frequency domain to all channels.</p>
 <p>Note that here, the shift is the same for all channels of a single example.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>delta_freq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – The amplitude of the frequency shift (in Hz).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the signals to be transformed.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -514,102 +612,93 @@ <h1>braindecode.augmentation.functional.frequency_shift<a class="headerlink" hre
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-  <a class="reference internal nav-link" href="#braindecode.augmentation.functional.frequency_shift">
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,56 +530,82 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-functional-ft-surrogate">
-<h1>braindecode.augmentation.functional.ft_surrogate<a class="headerlink" href="#braindecode-augmentation-functional-ft-surrogate" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.ft_surrogate<a class="headerlink" href="#braindecode-augmentation-functional-ft-surrogate" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.ft_surrogate">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">ft_surrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#ft_surrogate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.ft_surrogate" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">ft_surrogate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase_noise_magnitude</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_indep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L108-L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.ft_surrogate" title="Link to this definition">#</a></dt>
 <dd><p>FT surrogate augmentation of a single EEG channel, as proposed in <a class="reference internal" href="#r52a4658fffa7-1" id="id1">[1]</a>.</p>
 <p>Function copied from <a class="github reference external" href="https://github.com/cliffordlab/sleep-convolutions-tf">cliffordlab/sleep-convolutions-tf</a>
 and modified.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
-pertubation is uniformly sampled:
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>phase_noise_magnitude</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Float between 0 and 1 setting the range over which the phase
+perturbation is uniformly sampled:
 [0, <cite>phase_noise_magnitude</cite> * 2 * <cite>pi</cite>].</p></li>
-<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
+<li><p><strong>channel_indep</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to sample phase perturbations independently for each channel or
 not. It is advised to set it to False when spatial information is
 important for the task, like in BCI.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Used to draw the phase perturbation. Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -516,115 +614,107 @@ <h1>braindecode.augmentation.functional.ft_surrogate<a class="headerlink" href="
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r52a4658fffa7-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
+<div role="list" class="citation-list">
+<div class="citation" id="r52a4658fffa7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schwabedal, J. T., Snyder, J. C., Cakmak, A., Nemati, S., &amp;
 Clifford, G. D. (2018). Addressing Class Imbalance in Classification
 Problems of Noisy Signals by using Fourier Transform Surrogates. arXiv
 preprint arXiv:1806.08675.</p>
-</dd>
-</dl>
+</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,51 +530,77 @@
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   <section id="braindecode-augmentation-functional-gaussian-noise">
-<h1>braindecode.augmentation.functional.gaussian_noise<a class="headerlink" href="#braindecode-augmentation-functional-gaussian-noise" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.gaussian_noise<a class="headerlink" href="#braindecode-augmentation-functional-gaussian-noise" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.gaussian_noise">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#gaussian_noise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.gaussian_noise" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_noise</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L283-L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.gaussian_noise" title="Link to this definition">#</a></dt>
 <dd><p>Randomly add white Gaussian noise to all channels.</p>
 <p>Suggested e.g. in <a class="reference internal" href="#r6e3153c55efd-1" id="id1">[1]</a>, <a class="reference internal" href="#r6e3153c55efd-2" id="id2">[2]</a> and <a class="reference internal" href="#r6e3153c55efd-3" id="id3">[3]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
-<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Standard deviation to use for the additive noise.</p></li>
+<li><p><strong>random_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><em>numpy.random.Generator</em></a><em>, </em><em>optional</em>) – Seed to be used to instantiate numpy random number generator instance.
 Defaults to None.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -511,125 +609,119 @@ <h1>braindecode.augmentation.functional.gaussian_noise<a class="headerlink" href
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r6e3153c55efd-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
+<div role="list" class="citation-list">
+<div class="citation" id="r6e3153c55efd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Wang, F., Zhong, S. H., Peng, J., Jiang, J., &amp; Liu, Y. (2018). Data
 augmentation for eeg-based emotion recognition with deep convolutional
 neural networks. In International Conference on Multimedia Modeling
 (pp. 82-93).</p>
-</dd>
-<dt class="label" id="r6e3153c55efd-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+</div>
+<div class="citation" id="r6e3153c55efd-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="r6e3153c55efd-3"><span class="brackets"><a class="fn-backref" href="#id3">3</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="r6e3153c55efd-3" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id3">3</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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diff --git a/stable/generated/braindecode.augmentation.functional.identity.html b/stable/generated/braindecode.augmentation.functional.identity.html
index 48bf33c5..99071973 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,47 +530,73 @@
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   <section id="braindecode-augmentation-functional-identity">
-<h1>braindecode.augmentation.functional.identity<a class="headerlink" href="#braindecode-augmentation-functional-identity" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.identity<a class="headerlink" href="#braindecode-augmentation-functional-identity" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.identity">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.identity" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L17-L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.identity" title="Link to this definition">#</a></dt>
 <dd><p>Identity operation.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -511,102 +609,93 @@ <h1>braindecode.augmentation.functional.identity<a class="headerlink" href="#bra
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.augmentation.functional.identity"><code class="docutils literal notranslate"><span class="pre">identity()</span></code></a></li>
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diff --git a/stable/generated/braindecode.augmentation.functional.mixup.html b/stable/generated/braindecode.augmentation.functional.mixup.html
index 30012f6e..0bd3b8ee 100644
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   <section id="braindecode-augmentation-functional-mixup">
-<h1>braindecode.augmentation.functional.mixup<a class="headerlink" href="#braindecode-augmentation-functional-mixup" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.mixup<a class="headerlink" href="#braindecode-augmentation-functional-mixup" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.mixup">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#mixup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.mixup" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">mixup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lam</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_perm</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L913-L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.mixup" title="Link to this definition">#</a></dt>
 <dd><p>Mixes two channels of EEG data.</p>
 <p>See <a class="reference internal" href="#r3a833d0ab9c7-1" id="id1">[1]</a> for details.
 Implementation based on <a class="reference internal" href="#r3a833d0ab9c7-2" id="id2">[2]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
-<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG data in form <code class="docutils literal notranslate"><span class="pre">batch_size,</span> <span class="pre">n_channels,</span> <span class="pre">n_times</span></code></p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target of length <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
+<li><p><strong>lam</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Values between 0 and 1 setting the linear interpolation between
 examples.</p></li>
-<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
+<li><p><strong>idx_perm</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Permuted indices of example that are mixed into original examples.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><code class="docutils literal notranslate"><span class="pre">X</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code>. Where <code class="docutils literal notranslate"><span class="pre">X</span></code> is augmented and <code class="docutils literal notranslate"><span class="pre">y</span></code> is a tuple  of length
 3 containing the labels of the two mixed channels and the mixing
 coefficient.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)">tuple</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r3a833d0ab9c7-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+<div role="list" class="citation-list">
+<div class="citation" id="r3a833d0ab9c7-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
 (2018). mixup: Beyond Empirical Risk Minimization. In 2018
 International Conference on Learning Representations (ICLR)
 Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
-</dd>
-<dt class="label" id="r3a833d0ab9c7-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
-</dd>
-</dl>
+</div>
+<div class="citation" id="r3a833d0ab9c7-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p><a class="github reference external" href="https://github.com/facebookresearch/mixup-cifar10/blob/master/train.py">facebookresearch/mixup-cifar10</a></p>
+</div>
+</div>
 </dd></dl>
 
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diff --git a/stable/generated/braindecode.augmentation.functional.sensors_rotation.html b/stable/generated/braindecode.augmentation.functional.sensors_rotation.html
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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   <section id="braindecode-augmentation-functional-sensors-rotation">
-<h1>braindecode.augmentation.functional.sensors_rotation<a class="headerlink" href="#braindecode-augmentation-functional-sensors-rotation" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.sensors_rotation<a class="headerlink" href="#braindecode-augmentation-functional-sensors-rotation" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.sensors_rotation">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sensors_rotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#sensors_rotation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sensors_rotation" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sensors_rotation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sensors_positions_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">angles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spherical_splines</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L864-L910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sensors_rotation" title="Link to this definition">#</a></dt>
 <dd><p>Interpolates EEG signals over sensors rotated around the desired axis
 with the desired angle.</p>
 <p>Suggested in <a class="reference internal" href="#r228e8514004c-1" id="id1">[1]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
-<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>sensors_positions_matrix</strong> (<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><em>numpy.ndarray</em></a>) – <p>Matrix giving the positions of each sensor in a 3D cartesian coordinate
 system. Should have shape (3, n_channels), where n_channels is the
 number of channels. Standard 10-20 positions can be obtained from
 <code class="docutils literal notranslate"><span class="pre">mne</span></code> through:</p>
@@ -511,122 +609,114 @@ <h1>braindecode.augmentation.functional.sensors_rotation<a class="headerlink" hr
 <li><p><strong>axis</strong> (<em>'x'</em><em> | </em><em>'y'</em><em> | </em><em>'z'</em>) – Axis around which to rotate.</p></li>
 <li><p><strong>angles</strong> (<em>array-like</em>) – Array of float of shape <code class="docutils literal notranslate"><span class="pre">(batch_size,)</span></code> containing the rotation
 angles (in degrees) for each element of the input batch.</p></li>
-<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
+<li><p><strong>spherical_splines</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use spherical splines for the interpolation or not. When
 <cite>False</cite>, standard scipy.interpolate.Rbf (with quadratic kernel) will be
 used (as in the original paper).</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r228e8514004c-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
+<div role="list" class="citation-list">
+<div class="citation" id="r228e8514004c-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Krell, M. M., &amp; Kim, S. K. (2017). Rotational data augmentation for
 electroencephalographic data. In 2017 39th Annual International
 Conference of the IEEE Engineering in Medicine and Biology Society
 (EMBC) (pp. 471-474).</p>
-</dd>
-</dl>
+</div>
+</div>
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+ document.write(`
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-augmentation-functional-sign-flip">
-<h1>braindecode.augmentation.functional.sign_flip<a class="headerlink" href="#braindecode-augmentation-functional-sign-flip" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.sign_flip<a class="headerlink" href="#braindecode-augmentation-functional-sign-flip" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.sign_flip">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sign_flip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#sign_flip"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sign_flip" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">sign_flip</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L57-L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.sign_flip" title="Link to this definition">#</a></dt>
 <dd><p>Flip the sign axis of each input.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -511,102 +609,93 @@ <h1>braindecode.augmentation.functional.sign_flip<a class="headerlink" href="#br
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diff --git a/stable/generated/braindecode.augmentation.functional.smooth_time_mask.html b/stable/generated/braindecode.augmentation.functional.smooth_time_mask.html
index 3b0080ac..f0e640cc 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,52 +530,78 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-augmentation-functional-smooth-time-mask">
-<h1>braindecode.augmentation.functional.smooth_time_mask<a class="headerlink" href="#braindecode-augmentation-functional-smooth-time-mask" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.smooth_time_mask<a class="headerlink" href="#braindecode-augmentation-functional-smooth-time-mask" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.smooth_time_mask">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_time_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#smooth_time_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.smooth_time_mask" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_time_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_start_per_sample</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_len_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L363-L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.smooth_time_mask" title="Link to this definition">#</a></dt>
 <dd><p>Smoothly replace a contiguous part of all channels by zeros.</p>
 <p>Originally proposed in <a class="reference internal" href="#rbf2d67638580-1" id="id1">[1]</a> and <a class="reference internal" href="#rbf2d67638580-2" id="id2">[2]</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 <li><p><strong>mask_start_per_sample</strong> (<em>torch.tensor</em>) – Tensor of integers containing the position (in last dimension) where to
 start masking the signal. Should have the same size as the first
 dimension of X (i.e. one start position per example in the batch).</p></li>
-<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
+<li><p><strong>mask_len_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive samples to zero out.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -512,119 +610,112 @@ <h1>braindecode.augmentation.functional.smooth_time_mask<a class="headerlink" hr
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rbf2d67638580-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
+<div role="list" class="citation-list">
+<div class="citation" id="rbf2d67638580-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., &amp; Azemi, E. (2020).
 Subject-aware contrastive learning for biosignals. arXiv preprint
 arXiv:2007.04871.</p>
-</dd>
-<dt class="label" id="rbf2d67638580-2"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
-<dd><p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
+</div>
+<div class="citation" id="rbf2d67638580-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">2</a><span class="fn-bracket">]</span></span>
+<p>Mohsenvand, M. N., Izadi, M. R., &amp; Maes, P. (2020). Contrastive
 Representation Learning for Electroencephalogram Classification. In
 Machine Learning for Health (pp. 238-253). PMLR.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,47 +530,73 @@
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   <section id="braindecode-augmentation-functional-time-reverse">
-<h1>braindecode.augmentation.functional.time_reverse<a class="headerlink" href="#braindecode-augmentation-functional-time-reverse" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.augmentation.functional.time_reverse<a class="headerlink" href="#braindecode-augmentation-functional-time-reverse" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.augmentation.functional.time_reverse">
-<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">time_reverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/augmentation/functional.html#time_reverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.time_reverse" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.augmentation.functional.</span></span><span class="sig-name descname"><span class="pre">time_reverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/augmentation/functional.py#L37-L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.augmentation.functional.time_reverse" title="Link to this definition">#</a></dt>
 <dd><p>Flip the time axis of each input.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
-<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
+<li><p><strong>X</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG input example or batch.</p></li>
+<li><p><strong>y</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – EEG labels for the example or batch.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>torch.Tensor</em> – Transformed inputs.</p></li>
 <li><p><em>torch.Tensor</em> – Transformed labels.</p></li>
@@ -511,102 +609,93 @@ <h1>braindecode.augmentation.functional.time_reverse<a class="headerlink" href="
 <div style='clear:both'></div></section>
 
 
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diff --git a/stable/generated/braindecode.classifier.EEGClassifier.html b/stable/generated/braindecode.classifier.EEGClassifier.html
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 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.classifier.EEGClassifier</a></li>
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 </ul>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,67 +530,93 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-classifier-eegclassifier">
-<h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode-classifier-eegclassifier" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode-classifier-eegclassifier" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.classifier.</span></span><span class="sig-name descname"><span class="pre">EEGClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.classifier.</span></span><span class="sig-name descname"><span class="pre">EEGClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">criterion=&lt;class</span> <span class="pre">'torch.nn.modules.loss.CrossEntropyLoss'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L22-L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier" title="Link to this definition">#</a></dt>
 <dd><p>Classifier that does not assume softmax activation.
 Calls loss function directly without applying log or anything.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>module</strong> (<em>torch module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
 uninstantiated class should be passed, although instantiated
 modules will also work.</p></li>
 <li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.NLLLoss</em><em>)</em>) – Negative log likelihood loss. Note that the module should return
 probabilities, the log is applied during <code class="docutils literal notranslate"><span class="pre">get_loss</span></code>.</p></li>
-<li><p><strong>classes</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> (</em><em>default=None</em><em>)</em>) – If None, the <code class="docutils literal notranslate"><span class="pre">classes_</span></code> attribute will be inferred from the
+<li><p><strong>classes</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> (</em><em>default=None</em><em>)</em>) – If None, the <code class="docutils literal notranslate"><span class="pre">classes_</span></code> attribute will be inferred from the
 <code class="docutils literal notranslate"><span class="pre">y</span></code> data passed to <code class="docutils literal notranslate"><span class="pre">fit</span></code>. If a non-empty list is passed,
 that list will be returned as <code class="docutils literal notranslate"><span class="pre">classes_</span></code>. If the initial
 skorch behavior should be restored, i.e. raising an
 <code class="docutils literal notranslate"><span class="pre">AttributeError</span></code>, pass an empty list.</p></li>
 <li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
 module</p></li>
-<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
 of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
-<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
 may keyboard-interrupt training at any time.</p></li>
-<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
 <code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
 which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
 a single batch with all the data will be used during training
 and validation.</p></li>
-<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
 training data.</p></li>
-<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
 validation and test data, i.e. during inference.</p></li>
 <li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
 pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
@@ -529,14 +627,14 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
 to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
 arguments may be passed.</p></li>
-<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If None, there is no train/validation split. Else, train_split
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
 should be a function or callable that is called with X and y
 data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
-The validation data may be None.</p>
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
 those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
-any of the callbacks.</p>
+any of the callbacks, not even the default callbacks.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
 addition to the default callbacks. Each callback should be an
 instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
@@ -552,8 +650,8 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 </p></li>
 <li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
 ‘auto’, infers the correct nonlinearity based on the criterion
-(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
-<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
 or if the parameter is None, just use the identity
 function. Don’t pass a lambda function if you want the net to be
 pickleable.</p>
@@ -573,24 +671,48 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 not anywhere else – notably, the loss is unaffected by this
 nonlinearity.</p>
 </p></li>
-<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
 module (cold start) or whether the module should be trained
 further (warm start).</p></li>
-<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
 the net and its callbacks. By setting this value to 0, e.g. the
 summary scores at the end of each epoch are no longer printed.
 This can be useful when running a hyperparameter search. The
 summary scores are always logged in the history attribute,
 regardless of the verbose setting.</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.12)"><em>torch.device</em></a><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’, data in torch
-tensors will be pushed to cuda tensors before being sent to the
-module. If set to None, then all compute devices will be left
-unmodified.</p></li>
-<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>use_caching</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – Optionally override the caching behavior of scoring callbacks. Callbacks
+such as <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchScoring</span></code> allow to cache
+the inference call to save time when calculating scores during training at
+the expense of memory. In certain situations, e.g. when memory is tight,
+you may want to disable caching. As it is cumbersome to change the setting
+on each callback individually, this parameter allows to override their
+behavior globally.
+By default (<code class="docutils literal notranslate"><span class="pre">'auto'</span></code>), the callbacks will determine if caching is used
+or not. If this argument is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, caching will be disabled on
+all callbacks. If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, caching will be enabled on all
+callbacks.
+Implementation note: It is the job of the callbacks to honor this setting.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
 Currently used for callbacks definition.</p></li>
-<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
 <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
 list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
 functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
 or a list of callbacks where the callback names are inferred from the
@@ -602,58 +724,59 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
 <code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
 <span class="pre">'train_loss'])</span></code>).</p></li>
-<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
 shuffle the train dataset by default this one overwrites this option.</p></li>
-<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
 cropped mode.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.prefixes_">
-<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.prefixes_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.prefixes_" title="Link to this definition">#</a></dt>
 <dd><p>Contains the prefixes to special parameters. E.g., since there
-is the <code class="docutils literal notranslate"><span class="pre">'module'</span></code> prefix, it is possible to set parameters like
-so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>.</p>
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">
-<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
 <dd><p>Contains a list of all attribute prefixes whose values depend on a
 CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
 is unpickled on a machine without CUDA or with CUDA disabled, the
 listed attributes are mapped to CPU.  Expand this list if you
 want to add other cuda-dependent attributes.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.initialized_">
-<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.initialized_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.initialized_" title="Link to this definition">#</a></dt>
 <dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)">bool</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.module_">
-<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.module_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.module_" title="Link to this definition">#</a></dt>
 <dd><p>The instantiated module.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>torch module (instance)</p>
 </dd>
 </dl>
@@ -661,10 +784,10 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.criterion_">
-<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.criterion_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.criterion_" title="Link to this definition">#</a></dt>
 <dd><p>The instantiated criterion.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>torch criterion (instance)</p>
 </dd>
 </dl>
@@ -672,61 +795,61 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.callbacks_">
-<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.callbacks_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.callbacks_" title="Link to this definition">#</a></dt>
 <dd><p>The complete (i.e. default and other), initialized callbacks, in
 a tuple with unique names.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of tuples</p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._modules">
-<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._modules" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._modules" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all modules that are torch modules. This list is
 collected dynamically when the net is initialized. Typically, there is no
 reason for a user to modify this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._criteria">
-<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._criteria" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._criteria" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all criteria that are torch modules. This list is
 collected dynamically when the net is initialized. Typically, there is no
 reason for a user to modify this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier._optimizers">
-<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._optimizers" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier._optimizers" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all optimizers. This list is collected dynamically when
 the net is initialized. Typically, there is no reason for a user to modify
 this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.classes_">
-<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.classes_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#braindecode.classifier.EEGClassifier.classes_" title="Link to this definition">#</a></dt>
 <dd><p>A list of class labels known to the classifier.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>array, shape (n_classes, )</p>
 </dd>
 </dl>
@@ -735,25 +858,25 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_iterator">
-<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.get_iterator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_iterator" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L90-L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_iterator" title="Link to this definition">#</a></dt>
 <dd><p>Get an iterator that allows to loop over the batches of the
 given data.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
 <code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
 <code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
 data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
-<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>An instantiated iterator that allows to loop over the
 mini-batches.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>iterator</p>
 </dd>
 </dl>
@@ -761,10 +884,10 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.get_loss">
-<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.get_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_loss" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L142-L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.get_loss" title="Link to this definition">#</a></dt>
 <dd><p>Return the loss for this batch by calling NeuralNet get_loss.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>y_pred</strong> (<em>torch tensor</em>) – Predicted target values</p></li>
 <li><p><strong>y_true</strong> (<em>torch tensor</em>) – True target values.</p></li>
@@ -783,29 +906,24 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 <p>If this doesn’t work with your data, you have to pass a
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
 </p></li>
-<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)">float</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
 </dd>
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.on_batch_end">
-<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.on_batch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.on_batch_end" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L174-L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict" title="Link to this definition">#</a></dt>
 <dd><p>Return class labels for samples in X.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
 <blockquote>
 <div><ul class="simple">
@@ -822,10 +940,10 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
 </p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>y_pred</strong></p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>numpy ndarray</p>
 </dd>
 </dl>
@@ -833,7 +951,7 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_proba" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L97-L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return the output of the module’s forward method as a numpy
 array. In case of cropped decoding returns averaged values for
 each trial.</p>
@@ -844,7 +962,7 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
 instead.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
 <blockquote>
 <div><ul class="simple">
@@ -861,10 +979,10 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
 </p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>y_proba</strong></p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>numpy ndarray</p>
 </dd>
 </dl>
@@ -872,17 +990,17 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_trials">
-<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.predict_trials"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_trials" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/classifier.py#L200-L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_trials" title="Link to this definition">#</a></dt>
 <dd><p>Create trialwise predictions and optionally also return trialwise
 labels from cropped dataset.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
-<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
 the number of predictions depend on the chosen window size and the
@@ -896,267 +1014,115 @@ <h1>braindecode.classifier.EEGClassifier<a class="headerlink" href="#braindecode
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="braindecode.classifier.EEGClassifier.predict_with_window_inds_and_ys">
-<span class="sig-name descname"><span class="pre">predict_with_window_inds_and_ys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/classifier.html#EEGClassifier.predict_with_window_inds_and_ys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.classifier.EEGClassifier.predict_with_window_inds_and_ys" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
 </dd></dl>
 
 <div style='clear:both'></div></section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     EEGClassifier
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.prefixes_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.prefixes_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.cuda_dependent_attributes_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.initialized_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.initialized_
-      </span>
-     </code>
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-     <code class="docutils literal notranslate">
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-       EEGClassifier.module_
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.criterion_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.criterion_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.callbacks_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.callbacks_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._modules">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier._modules
-      </span>
-     </code>
-    </a>
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._criteria">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier._criteria
-      </span>
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-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._optimizers">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier._optimizers
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.classes_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.classes_
-      </span>
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-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_iterator">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.get_iterator()
-      </span>
-     </code>
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_loss">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.get_loss()
-      </span>
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-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.on_batch_end">
-     <code class="docutils literal notranslate">
-      <span class="pre">
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-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict">
-     <code class="docutils literal notranslate">
-      <span class="pre">
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-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_proba">
-     <code class="docutils literal notranslate">
-      <span class="pre">
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-      </span>
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-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_trials">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.predict_trials()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_with_window_inds_and_ys">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGClassifier.predict_with_window_inds_and_ys()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier"><code class="docutils literal notranslate"><span class="pre">EEGClassifier</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.module_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._modules"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._criteria"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGClassifier._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.classes_"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.classes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.get_loss"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.get_loss()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.classifier.EEGClassifier.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGClassifier.predict_trials()</span></code></a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-bcicompetitionivdataset4">
-<h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#braindecode-datasets-bcicompetitionivdataset4" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#braindecode-datasets-bcicompetitionivdataset4" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BCICompetitionIVDataset4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/bcicomp.html#BCICompetitionIVDataset4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BCICompetitionIVDataset4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L23-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4" title="Link to this definition">#</a></dt>
 <dd><p>BCI competition IV dataset 4.</p>
 <p>Contains ECoG recordings for three patients moving fingers during the experiment.
 Targets correspond to the time courses of the flexion of each of five fingers.
@@ -499,27 +597,28 @@ <h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#br
 <p class="rubric">Notes</p>
 <p>When using this dataset please cite <a class="reference internal" href="#rb33c1a7bbf7d-1" id="id1">[1]</a> .</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
 subjects. Should be in range 1-3.</p>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rb33c1a7bbf7d-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Miller, Kai J. “A library of human electrocorticographic data and analyses.”</p>
-</dd>
-</dl>
+<div role="list" class="citation-list">
+<div class="citation" id="rb33c1a7bbf7d-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Miller, Kai J. “A library of human electrocorticographic data and analyses.”</p>
+</div>
+</div>
 <p>Nature human behaviour 3, no. 11 (2019): 1225-1235. <a class="reference external" href="https://doi.org/10.1038/s41562-019-0678-3">https://doi.org/10.1038/s41562-019-0678-3</a></p>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BCICompetitionIVDataset4.download">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/bcicomp.html#BCICompetitionIVDataset4.download"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.download" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">download</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_update</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/bcicomp.py#L74-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BCICompetitionIVDataset4.download" title="Link to this definition">#</a></dt>
 <dd><p>Download the dataset.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>location.</strong> (<em>path</em><em>  (</em><em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>) </em><em>– Location</em><em> of </em><em>where to look for the data storing</em>) – </p></li>
+<li><p><strong>location.</strong> (<em>path</em><em>  (</em><em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>) </em><em>– Location</em><em> of </em><em>where to look for the data storing</em>) – </p></li>
 <li><p><strong>None</strong> (<em>or None</em><em>) </em><em>– If not</em>) – </p></li>
 <li><p><strong>parameter</strong> (<em>the environment variable</em><em> or </em><em>config</em>) – </p></li>
 <li><p><strong>exist</strong> (<em>MNE_DATASETS_</em><em>(</em><em>dataset</em><em>)</em><em>_PATH is used. If it doesn’t</em>) – </p></li>
@@ -527,7 +626,7 @@ <h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#br
 <li><p><strong>path</strong> (<em>directory is used. If the dataset is not found under the given</em>) – </p></li>
 <li><p><strong>data</strong> (<em>the</em>) – </p></li>
 <li><p><strong>folder.</strong> (<em>will be automatically downloaded to the specified</em>) – </p></li>
-<li><p><strong>exists.</strong> (<em>force_update</em><em> (</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>) </em><em>– Force update</em><em> of </em><em>the dataset even if a local copy</em>) – </p></li>
+<li><p><strong>exists.</strong> (<em>force_update</em><em> (</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>) </em><em>– Force update</em><em> of </em><em>the dataset even if a local copy</em>) – </p></li>
 <li><p><strong>(</strong><strong>bool</strong> (<em>verbose</em>) – </p></li>
 <li><p><strong>str</strong> – </p></li>
 <li><p><strong>int</strong> – </p></li>
@@ -542,134 +641,108 @@ <h1>braindecode.datasets.BCICompetitionIVDataset4<a class="headerlink" href="#br
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-bcicompetitionivdataset4">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BCICompetitionIVDataset4</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-bcicompetitionivdataset4" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BCICompetitionIVDataset4</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-bcicompetitionivdataset4" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     BCICompetitionIVDataset4
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.download">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       BCICompetitionIVDataset4.download()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#examples-using-braindecode-datasets-bcicompetitionivdataset4">
-   Examples using
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     braindecode.datasets.BCICompetitionIVDataset4
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BCICompetitionIVDataset4.download"><code class="docutils literal notranslate"><span class="pre">BCICompetitionIVDataset4.download()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-bcicompetitionivdataset4">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BCICompetitionIVDataset4</span></code></a></li>
+</ul>
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diff --git a/stable/generated/braindecode.datasets.BNCI2014001.html b/stable/generated/braindecode.datasets.BNCI2014001.html
index c9b8f87f..77faa1e6 100644
--- a/stable/generated/braindecode.datasets.BNCI2014001.html
+++ b/stable/generated/braindecode.datasets.BNCI2014001.html
@@ -1,362 +1,429 @@
 
 <!DOCTYPE html>
 
-<html lang="en">
+
+<html lang="en" data-theme="light">
+
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>braindecode.datasets.BNCI2014001 &#8212; Braindecode 0.7 documentation</title>
-  <script>
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+  
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+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
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+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
+  </button>
 
   
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
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+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
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+  
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+<form class="bd-search d-flex align-items-center"
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+      method="get">
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+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
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+        <div class="navbar-item"><nav class="navbar-nav">
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+     role="heading"
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+    Site Navigation
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+        <div class="navbar-item navbar-persistent--container">
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+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        </div>
       
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-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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-</div>
-      </div>
+  </div>
+`);
+</script></div>
       
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+    
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-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
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+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+    </nav>
+  
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-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
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+                    </li>
                 
 
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
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+                    <li class="nav-item current active">
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+
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+                      <a class="nav-link nav-internal" href="../help.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
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+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
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+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
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-  <div class="sidebar-start-items sidebar-primary__section">
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-    Section Navigation
-  </p>
-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,41 +530,70 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
-
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-</nav>
+</div>
+</nav></div>
     </div>
-  </div>
   
-
   
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
   </div>
+  
+  <div id="rtd-footer-container"></div>
+
 
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-      <main class="bd-main">
+      
+      <main id="main-content" class="bd-main">
         
         
-        <div class="bd-content">
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   <section id="braindecode-datasets-bnci2014001">
-<h1>braindecode.datasets.BNCI2014001<a class="headerlink" href="#braindecode-datasets-bnci2014001" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.BNCI2014001<a class="headerlink" href="#braindecode-datasets-bnci2014001" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.BNCI2014001">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/moabb.html#BNCI2014001"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BNCI2014001" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BNCI2014001</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L114-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BNCI2014001" title="Link to this definition">#</a></dt>
 <dd><p>BNCI 2014-001 Motor Imagery dataset.</p>
 <blockquote>
-<div><p>Dataset IIa from BCI Competition 4 <span id="id1">[R382d436f3223-1]</span>.</p>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+</div>
+<p>Dataset IIa from BCI Competition 4 <span id="id1">[R382d436f3223-1]</span>.</p>
 <p><strong>Dataset Description</strong></p>
 <p>This data set consists of EEG data from 9 subjects.  The cue-based BCI
 paradigm consisted of four different motor imagery tasks, namely the imag-
@@ -520,7 +621,7 @@ <h1>braindecode.datasets.BNCI2014001<a class="headerlink" href="#braindecode-dat
 enabled to suppress line noise</p>
 </div></blockquote>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
 <dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
 subjects is fetched.</p>
@@ -534,102 +635,93 @@ <h1>braindecode.datasets.BNCI2014001<a class="headerlink" href="#braindecode-dat
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,45 +530,71 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-baseconcatdataset">
-<h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindecode-datasets-baseconcatdataset" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindecode-datasets-baseconcatdataset" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseConcatDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L386-L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset" title="Link to this definition">#</a></dt>
 <dd><p>A base class for concatenated datasets. Holds either mne.Raw or
 mne.Epoch in self.datasets and has a pandas DataFrame with additional
 description.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>list_of_ds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – list of BaseDataset, BaseConcatDataset or WindowsDataset</p></li>
+<li><p><strong>list_of_ds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – list of BaseDataset, BaseConcatDataset or WindowsDataset</p></li>
 <li><p><strong>target_transform</strong> (<em>callable</em><em> | </em><em>None</em>) – Optional function to call on targets before returning them.</p></li>
 </ul>
 </dd>
@@ -504,15 +602,15 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.get_metadata">
-<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseConcatDataset.get_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.get_metadata" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">get_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L490-L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.get_metadata" title="Link to this definition">#</a></dt>
 <dd><p>Concatenate the metadata and description of the wrapped Epochs.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>metadata</strong> – DataFrame containing as many rows as there are windows in the
 BaseConcatDataset, with the metadata and description information
 for each window.</p>
 </dd>
-<dt class="field-even">Return type</dt>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
 <dd class="field-even"><p>pd.DataFrame</p>
 </dd>
 </dl>
@@ -520,7 +618,7 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseConcatDataset.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.save" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L628-L709"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.save" title="Link to this definition">#</a></dt>
 <dd><p>Save datasets to files by creating one subdirectory for each dataset:
 path/</p>
 <blockquote>
@@ -542,16 +640,16 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 </dl>
 </div></blockquote>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – <dl class="simple">
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – <dl class="simple">
 <dt>Directory in which subdirectories are created to store</dt><dd><p>-raw.fif | -epo.fif and .json files to.</p>
 </dd>
 </dl>
 </p></li>
-<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to delete old subdirectories that will be saved to in this
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to delete old subdirectories that will be saved to in this
 call.</p></li>
-<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – If provided, the integer is added to the id of the dataset in the
+<li><p><strong>offset</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – If provided, the integer is added to the id of the dataset in the
 concat. This is useful in the setting of very large datasets, where
 one dataset has to be processed and saved at a time to account for
 its original position.</p></li>
@@ -562,14 +660,14 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.set_description">
-<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseConcatDataset.set_description"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.set_description" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L611-L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.set_description" title="Link to this definition">#</a></dt>
 <dd><p>Update (add or overwrite) the dataset description.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>pd.DataFrame</em>) – Description in the form key: value where the length of the value
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.DataFrame</em>) – Description in the form key: value where the length of the value
 has to match the number of datasets.</p></li>
-<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
 dataset description.</p></li>
 </ul>
 </dd>
@@ -578,30 +676,30 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseConcatDataset.split">
-<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">property</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseConcatDataset.split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.split" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">property</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L436-L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseConcatDataset.split" title="Link to this definition">#</a></dt>
 <dd><p>Split the dataset based on information listed in its description
 DataFrame or based on indices.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a string, splitting is performed based on the
+<li><p><strong>by</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a string, splitting is performed based on the
 description DataFrame column with this name.
 If <code class="docutils literal notranslate"><span class="pre">by</span></code> is a (list of) list of integers, the position in the first
 list corresponds to the split id and the integers to the
 datapoints of that split.
 If a dict then each key will be used in the returned
 splits dict and each value should be a list of int.</p></li>
-<li><p><strong>property</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Some property which is listed in info DataFrame.</p></li>
-<li><p><strong>split_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – List of indices to be combined in a subset.
+<li><p><strong>property</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Some property which is listed in info DataFrame.</p></li>
+<li><p><strong>split_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – List of indices to be combined in a subset.
 It can be a list of int or a list of list of int.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>splits</strong> – A dictionary with the name of the split (a string) as key and the
 dataset as value.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)">dict</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -609,218 +707,171 @@ <h1>braindecode.datasets.BaseConcatDataset<a class="headerlink" href="#braindeco
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-baseconcatdataset">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-baseconcatdataset" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show multiple ways of how to split datasets."><img alt="Split Dataset Example" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_split_dataset.html#sphx-glr-auto-examples-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-baseconcatdataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
   <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="MNE Dataset Example" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_mne_dataset_example.html#sphx-glr-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="Custom Dataset Example" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_custom_dataset_example.html#sphx-glr-auto-examples-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of multiple discrete targets per recording with the TUH EEG ..."><img alt="Multiple discrete targets with the TUH EEG Corpus" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.get_metadata"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.get_metadata()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.save"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.save()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.set_description()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseConcatDataset.split"><code class="docutils literal notranslate"><span class="pre">BaseConcatDataset.split()</span></code></a></li>
 </ul>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-baseconcatdataset">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a></li>
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diff --git a/stable/generated/braindecode.datasets.BaseDataset.html b/stable/generated/braindecode.datasets.BaseDataset.html
index 2d579e87..dbcfdc6f 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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@@ -458,48 +530,74 @@
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   <section id="braindecode-datasets-basedataset">
-<h1>braindecode.datasets.BaseDataset<a class="headerlink" href="#braindecode-datasets-basedataset" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.BaseDataset<a class="headerlink" href="#braindecode-datasets-basedataset" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">BaseDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L36-L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset" title="Link to this definition">#</a></dt>
 <dd><p>Returns samples from an mne.io.Raw object along with a target.</p>
 <p>Dataset which serves samples from an mne.io.Raw object along with a target.
 The target is unique for the dataset, and is obtained through the
 <cite>description</cite> attribute.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.2)"><em>mne.io.Raw</em></a>) – Continuous data.</p></li>
-<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional description about the continuous signal / subject.</p></li>
-<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em> | </em><em>None</em>) – Name(s) of the index in <cite>description</cite> that should be used to provide the
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – Continuous data.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional description about the continuous signal / subject.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em> | </em><em>None</em>) – Name(s) of the index in <cite>description</cite> that should be used to provide the
 target (e.g., to be used in a prediction task later on).</p></li>
 <li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to the example before it is returned.</p></li>
 </ul>
@@ -508,13 +606,13 @@ <h1>braindecode.datasets.BaseDataset<a class="headerlink" href="#braindecode-dat
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.BaseDataset.set_description">
-<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#BaseDataset.set_description"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset.set_description" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L92-L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.BaseDataset.set_description" title="Link to this definition">#</a></dt>
 <dd><p>Update (add or overwrite) the dataset description.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
-<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
 dataset description.</p></li>
 </ul>
 </dd>
@@ -524,131 +622,105 @@ <h1>braindecode.datasets.BaseDataset<a class="headerlink" href="#braindecode-dat
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-basedataset">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-basedataset" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-basedataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset"><code class="docutils literal notranslate"><span class="pre">BaseDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.BaseDataset.set_description"><code class="docutils literal notranslate"><span class="pre">BaseDataset.set_description()</span></code></a></li>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-basedataset">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.BaseDataset</span></code></a></li>
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diff --git a/stable/generated/braindecode.datasets.HGD.html b/stable/generated/braindecode.datasets.HGD.html
index 881c7c91..fc9dbd77 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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   <section id="braindecode-datasets-hgd">
-<h1>braindecode.datasets.HGD<a class="headerlink" href="#braindecode-datasets-hgd" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.HGD<a class="headerlink" href="#braindecode-datasets-hgd" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.HGD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">HGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/moabb.html#HGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD" title="Permalink to this definition">#</a></dt>
-<dd><p>High-gamma dataset discribed in Schirrmeister et al. 2017</p>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">HGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L133-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.HGD" title="Link to this definition">#</a></dt>
+<dd><p>High-gamma dataset described in Schirrmeister et al. 2017.</p>
 <blockquote>
-<div><p>Dataset from <span id="id1">[R5e478952091a-1]</span></p>
+<div><div class="admonition-dataset-summary admonition">
+<p class="admonition-title">Dataset summary</p>
+<table class="table">
+<thead>
+<tr class="row-odd"><th class="head"><p>Name</p></th>
+<th class="head"><p>#Subj</p></th>
+<th class="head"><p>#Chan</p></th>
+<th class="head"><p>#Classes</p></th>
+<th class="head"><p>#Trials / class</p></th>
+<th class="head"><p>Trials len</p></th>
+<th class="head"><p>Sampling rate</p></th>
+<th class="head"><p>#Sessions</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Schirrmeister2017</p></td>
+<td><p>14</p></td>
+<td><p>128</p></td>
+<td><p>4</p></td>
+<td><p>120</p></td>
+<td><p>4s</p></td>
+<td><p>500Hz</p></td>
+<td><p>1</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<p>Dataset from <span id="id1">[R5e478952091a-1]</span></p>
 <p>Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
 44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
 healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
@@ -520,7 +645,7 @@ <h1>braindecode.datasets.HGD<a class="headerlink" href="#braindecode-datasets-hg
 of Freiburg.</p>
 </div></blockquote>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl class="simple">
 <dt><strong>subject_ids: list(int) | int | None</strong></dt><dd><p>(list of) int of subject(s) to be fetched. If None, data of all
 subjects is fetched.</p>
@@ -534,102 +659,93 @@ <h1>braindecode.datasets.HGD<a class="headerlink" href="#braindecode-datasets-hg
 <div style='clear:both'></div></section>
 
 
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-  <a class="reference internal nav-link" href="#braindecode.datasets.HGD">
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diff --git a/stable/generated/braindecode.datasets.MOABBDataset.html b/stable/generated/braindecode.datasets.MOABBDataset.html
index 50c1095b..79785f43 100644
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+        <div class="navbar-item">
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+ document.write(`
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+    </div>
+  
 
   
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+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
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+    Site Navigation
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+      data-bs-toggle="dropdown"
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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   <section id="braindecode-datasets-moabbdataset">
-<h1>braindecode.datasets.MOABBDataset<a class="headerlink" href="#braindecode-datasets-moabbdataset" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.MOABBDataset<a class="headerlink" href="#braindecode-datasets-moabbdataset" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.MOABBDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">MOABBDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/moabb.html#MOABBDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.MOABBDataset" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">MOABBDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/moabb.py#L93-L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.MOABBDataset" title="Link to this definition">#</a></dt>
 <dd><p>A class for moabb datasets.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – name of dataset included in moabb to be fetched</p></li>
-<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be fetched. If None, data of all
+<li><p><strong>dataset_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – name of dataset included in moabb to be fetched</p></li>
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be fetched. If None, data of all
 subjects is fetched.</p></li>
-<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
+<li><p><strong>dataset_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>, </em><em>optional</em>) – optional dictionary containing keyword arguments
 to pass to the moabb dataset when instantiating it.</p></li>
 </ul>
 </dd>
@@ -505,144 +603,129 @@ <h1>braindecode.datasets.MOABBDataset<a class="headerlink" href="#braindecode-da
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-moabbdataset">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.MOABBDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-moabbdataset" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show multiple ways of how to split datasets."><img alt="Split Dataset Example" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_split_dataset.html#sphx-glr-auto-examples-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.MOABBDataset</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-moabbdataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
   <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
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diff --git a/stable/generated/braindecode.datasets.SleepPhysionet.html b/stable/generated/braindecode.datasets.SleepPhysionet.html
index e8dbd93d..78be991b 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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   <section id="braindecode-datasets-sleepphysionet">
-<h1>braindecode.datasets.SleepPhysionet<a class="headerlink" href="#braindecode-datasets-sleepphysionet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.SleepPhysionet<a class="headerlink" href="#braindecode-datasets-sleepphysionet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.SleepPhysionet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">SleepPhysionet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_eeg_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_wake_mins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/sleep_physionet.html#SleepPhysionet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.SleepPhysionet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">SleepPhysionet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">subject_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_eeg_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop_wake_mins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">crop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/sleep_physionet.py#L16-L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.SleepPhysionet" title="Link to this definition">#</a></dt>
 <dd><p>Sleep Physionet dataset.</p>
 <p>Sleep dataset from <a class="reference external" href="https://physionet.org/content/sleep-edfx/1.0.0/">https://physionet.org/content/sleep-edfx/1.0.0/</a>.
 Contains overnight recordings from 78 healthy subjects.</p>
 <p>See [MNE example](<a class="reference external" href="https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html">https://mne.tools/stable/auto_tutorials/sample-datasets/plot_sleep.html</a>).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
+<li><p><strong>subject_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – (list of) int of subject(s) to be loaded. If None, load all available
 subjects.</p></li>
-<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><em>None</em>) – Recordings to load per subject (each subject except 13 has two
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><em>None</em>) – Recordings to load per subject (each subject except 13 has two
 recordings). Can be [1], [2] or [1, 2] (same as None).</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
-<li><p><strong>load_eeg_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, only load the EEG channels and discard the others (EOG, EMG,
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>load_eeg_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, only load the EEG channels and discard the others (EOG, EMG,
 temperature, respiration) to avoid resampling the other signals.</p></li>
-<li><p><strong>crop_wake_mins</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Number of minutes of wake time to keep before the first sleep event
+<li><p><strong>crop_wake_mins</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Number of minutes of wake time to keep before the first sleep event
 and after the last sleep event. Used to reduce the imbalance in this
 dataset. Default of 30 mins.</p></li>
-<li><p><strong>crop</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a>) – If not None crop the raw files (e.g. to use only the first 3h).
+<li><p><strong>crop</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a>) – If not None crop the raw files (e.g. to use only the first 3h).
 Example: <code class="docutils literal notranslate"><span class="pre">crop=(0,</span> <span class="pre">3600*3)</span></code> to keep only the first 3h.</p></li>
 </ul>
 </dd>
@@ -515,132 +613,114 @@ <h1>braindecode.datasets.SleepPhysionet<a class="headerlink" href="#braindecode-
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-sleepphysionet">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-sleepphysionet" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-sleepphysionet" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,177 +530,185 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-tuh">
-<h1>braindecode.datasets.TUH<a class="headerlink" href="#braindecode-datasets-tuh" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.TUH<a class="headerlink" href="#braindecode-datasets-tuh" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.TUH">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/tuh.html#TUH"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUH" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUH</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L26-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUH" title="Link to this definition">#</a></dt>
 <dd><p>Temple University Hospital (TUH) EEG Corpus
 (www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
-<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
 overwrite default chronological order, e.g. if recording_ids=[1,0],
 then the first recording returned by this class will be chronologically
 later then the second recording. Provide recording_ids in ascending
 order to preserve chronological order.).</p></li>
-<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Can be ‘gender’, or ‘age’.</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
-<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
 description.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
 </ul>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-tuh">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUH</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuh" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of multiple discrete targets per recording with the TUH EEG ..."><img alt="Multiple discrete targets with the TUH EEG Corpus" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUH</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuh" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
   <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
   <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-tuh">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUH</span></code></a></li>
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
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+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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@@ -458,52 +530,78 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-tuhabnormal">
-<h1>braindecode.datasets.TUHAbnormal<a class="headerlink" href="#braindecode-datasets-tuhabnormal" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.TUHAbnormal<a class="headerlink" href="#braindecode-datasets-tuhabnormal" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.TUHAbnormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUHAbnormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pathological'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/tuh.html#TUHAbnormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUHAbnormal" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">TUHAbnormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recording_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'pathological'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_physician_reports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/tuh.py#L274-L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.TUHAbnormal" title="Link to this definition">#</a></dt>
 <dd><p>Temple University Hospital (TUH) Abnormal EEG Corpus.
 see www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tuab</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
-<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Parent directory of the dataset.</p></li>
+<li><p><strong>recording_ids</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>) </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – A (list of) int of recording id(s) to be read (order matters and will
 overwrite default chronological order, e.g. if recording_ids=[1,0],
 then the first recording returned by this class will be chronologically
 later then the second recording. Provide recording_ids in ascending
 order to preserve chronological order.).</p></li>
-<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Can be ‘pathological’, ‘gender’, or ‘age’.</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
-<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Can be ‘pathological’, ‘gender’, or ‘age’.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Raw objects.</p></li>
+<li><p><strong>add_physician_reports</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the physician reports will be read from disk and added to the
 description.</p></li>
 </ul>
 </dd>
@@ -511,120 +609,102 @@ <h1>braindecode.datasets.TUHAbnormal<a class="headerlink" href="#braindecode-dat
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-tuhabnormal">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUHAbnormal</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuhabnormal" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="Benchmarking eager and lazy loading" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUHAbnormal</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-tuhabnormal" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.datasets.TUHAbnormal">
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-    </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.TUHAbnormal"><code class="docutils literal notranslate"><span class="pre">TUHAbnormal</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-tuhabnormal">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.TUHAbnormal</span></code></a></li>
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diff --git a/stable/generated/braindecode.datasets.WindowsDataset.html b/stable/generated/braindecode.datasets.WindowsDataset.html
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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-<h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-datasets-windowsdataset" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-datasets-windowsdataset" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">WindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#WindowsDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">WindowsDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L271-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset" title="Link to this definition">#</a></dt>
 <dd><p>Returns windows from an mne.Epochs object along with a target.</p>
 <p>Dataset which serves windows from an mne.Epochs object along with their
 target and additional information. The <cite>metadata</cite> attribute of the Epochs
@@ -501,13 +599,13 @@ <h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-
 See <cite>braindecode.datautil.windowers</cite> to directly create a <cite>WindowsDataset</cite>
 from a <cite>BaseDataset</cite> object.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>windows</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.2)"><em>mne.Epochs</em></a>) – Windows obtained through the application of a windower to a BaseDataset
+<li><p><strong>windows</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><em>mne.Epochs</em></a>) – Windows obtained through the application of a windower to a BaseDataset
 (see <cite>braindecode.datautil.windowers</cite>).</p></li>
-<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pandas.Series</em><em> | </em><em>None</em>) – Holds additional info about the windows.</p></li>
 <li><p><strong>transform</strong> (<em>callable</em><em> | </em><em>None</em>) – On-the-fly transform applied to a window before it is returned.</p></li>
-<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs <cite>misc</cite>
+<li><p><strong>targets_from</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Defines whether targets will be extracted from mne.Epochs metadata or mne.Epochs <cite>misc</cite>
 channels (time series targets). It can be <cite>metadata</cite> (default) or <cite>channels</cite>.</p></li>
 </ul>
 </dd>
@@ -515,13 +613,13 @@ <h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.datasets.WindowsDataset.set_description">
-<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/base.html#WindowsDataset.set_description"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset.set_description" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">set_description</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">description</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/base.py#L365-L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.WindowsDataset.set_description" title="Link to this definition">#</a></dt>
 <dd><p>Update (add or overwrite) the dataset description.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
-<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
+<li><p><strong>description</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>pd.Series</em>) – Description in the form key: value.</p></li>
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Has to be True if a key in description already exists in the
 dataset description.</p></li>
 </ul>
 </dd>
@@ -533,113 +631,96 @@ <h1>braindecode.datasets.WindowsDataset<a class="headerlink" href="#braindecode-
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     WindowsDataset
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
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-    <a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset.set_description">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       WindowsDataset.set_description()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset"><code class="docutils literal notranslate"><span class="pre">WindowsDataset</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.WindowsDataset.set_description"><code class="docutils literal notranslate"><span class="pre">WindowsDataset.set_description()</span></code></a></li>
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diff --git a/stable/generated/braindecode.datasets.create_from_X_y.html b/stable/generated/braindecode.datasets.create_from_X_y.html
index c0ed215d..79e9279a 100644
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-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,179 +530,187 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+  
   <section id="braindecode-datasets-create-from-x-y">
-<h1>braindecode.datasets.create_from_X_y<a class="headerlink" href="#braindecode-datasets-create-from-x-y" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.create_from_X_y<a class="headerlink" href="#braindecode-datasets-create-from-x-y" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.datasets.create_from_X_y">
-<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/xy.html#create_from_X_y"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_X_y" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_X_y</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/xy.py#L16-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_X_y" title="Link to this definition">#</a></dt>
 <dd><p>Create a BaseConcatDataset of WindowsDatasets from X and y to be used for
 decoding with skorch and braindecode, where X is a list of pre-cut trials
 and y are corresponding targets.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<em>array-like</em>) – list of pre-cut trials as n_trials x n_channels x n_times</p></li>
 <li><p><strong>y</strong> (<em>array-like</em>) – targets corresponding to the trials</p></li>
-<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
 windows/windows do not equally divide the continuous signal</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Sampling frequency of signals.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of signals.</p></li>
 <li><p><strong>ch_names</strong> (<em>array-like</em>) – Names of the channels.</p></li>
-<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – window size</p></li>
-<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
 and braindecode</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-create-from-x-y">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_X_y</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-x-y" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="Custom Dataset Example" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_custom_dataset_example.html#sphx-glr-auto-examples-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_X_y</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-x-y" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to convert data X and y as numpy arrays to a braindecode compatible data..."><img alt="" src="../_images/sphx_glr_plot_custom_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_custom_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-custom-dataset-example-py"><span class="std std-ref">Custom Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">Custom Dataset Example</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-create-from-x-y">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_X_y</span></code></a></li>
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
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+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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@@ -458,174 +530,182 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-create-from-mne-epochs">
-<h1>braindecode.datasets.create_from_mne_epochs<a class="headerlink" href="#braindecode-datasets-create-from-mne-epochs" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.create_from_mne_epochs<a class="headerlink" href="#braindecode-datasets-create-from-mne-epochs" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_epochs">
-<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_epochs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_epochs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/mne.html#create_from_mne_epochs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_epochs" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_epochs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_of_epochs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L87-L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_epochs" title="Link to this definition">#</a></dt>
 <dd><p>Create WindowsDatasets from mne.Epochs</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>list_of_epochs</strong> (<em>array-like</em>) – list of mne.Epochs</p></li>
-<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – window size</p></li>
-<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – stride between windows</p></li>
-<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
 windows do not equally divide the continuous signal</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compativle with skorch
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
 and braindecode</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-create-from-mne-epochs">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_epochs</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-epochs" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="MNE Dataset Example" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_mne_dataset_example.html#sphx-glr-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_epochs</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-epochs" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
 </div></div><div style='clear:both'></div></section>
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-    </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.datasets.create_from_mne_epochs"><code class="docutils literal notranslate"><span class="pre">create_from_mne_epochs()</span></code></a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-create-from-mne-epochs">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_epochs</span></code></a></li>
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
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+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,188 +530,196 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-datasets-create-from-mne-raw">
-<h1>braindecode.datasets.create_from_mne_raw<a class="headerlink" href="#braindecode-datasets-create-from-mne-raw" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datasets.create_from_mne_raw<a class="headerlink" href="#braindecode-datasets-create-from-mne-raw" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.datasets.create_from_mne_raw">
-<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raws</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">descriptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datasets/mne.html#create_from_mne_raw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_raw" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.datasets.</span></span><span class="sig-name descname"><span class="pre">create_from_mne_raw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raws</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">descriptions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datasets/mne.py#L13-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datasets.create_from_mne_raw" title="Link to this definition">#</a></dt>
 <dd><p>Create WindowsDatasets from mne.RawArrays</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>raws</strong> (<em>array-like</em>) – list of mne.RawArrays</p></li>
-<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – start offset from original trial onsets in samples</p></li>
-<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – stop offset from original trial stop in samples</p></li>
-<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – window size</p></li>
-<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – stride between windows</p></li>
-<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether or not have a last overlapping window, when
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – start offset from original trial onsets in samples</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stop offset from original trial stop in samples</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – window size</p></li>
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – stride between windows</p></li>
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – whether or not have a last overlapping window, when
 windows do not equally divide the continuous signal</p></li>
 <li><p><strong>descriptions</strong> (<em>array-like</em>) – list of dicts or pandas.Series with additional information about the raws</p></li>
-<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – mapping from event description to target value</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – if True, preload the data of the Epochs objects.</p></li>
-<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – mapping from event description to target value</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – if True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
 step allows identifying e.g., windows that fall outside of the
 continuous recording. It is suggested to run this step here as otherwise
 the BaseConcatDataset has to be updated as well.</p></li>
-<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials withinconsistent length in a raw. If
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials withinconsistent length in a raw. If
 the number of trials whose length is exceeded by the window size is
 smaller than this, then only the corresponding trials are dropped, but
 the computation continues. Otherwise, an error is raised. Defaults to
 0.0 (raise an error).</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compativle with skorch
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – X and y transformed to a dataset format that is compatible with skorch
 and braindecode</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-datasets-create-from-mne-raw">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_raw</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-raw" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="MNE Dataset Example" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_mne_dataset_example.html#sphx-glr-auto-examples-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_raw</span></code><a class="headerlink" href="#examples-using-braindecode-datasets-create-from-mne-raw" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="MNE Dataset Example"><img alt="" src="../_images/sphx_glr_plot_mne_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_mne_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-mne-dataset-example-py"><span class="std std-ref">MNE Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MNE Dataset Example</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-datasets-create-from-mne-raw">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datasets.create_from_mne_raw</span></code></a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-datautil-load-concat-dataset">
-<h1>braindecode.datautil.load_concat_dataset<a class="headerlink" href="#braindecode-datautil-load-concat-dataset" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.datautil.load_concat_dataset<a class="headerlink" href="#braindecode-datautil-load-concat-dataset" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.datautil.load_concat_dataset">
-<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.</span></span><span class="sig-name descname"><span class="pre">load_concat_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids_to_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/datautil/serialization.html#load_concat_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.load_concat_dataset" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.datautil.</span></span><span class="sig-name descname"><span class="pre">load_concat_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ids_to_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/datautil/serialization.py#L157-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.datautil.load_concat_dataset" title="Link to this definition">#</a></dt>
 <dd><p>Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from
 files.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Path to the directory of the .fif / -epo.fif and .json files.</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to preload the data.</p></li>
-<li><p><strong>ids_to_load</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Ids of specific files to load.</p></li>
-<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><em>None</em>) – Load specific description column as target. If not given, take saved
+<li><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/pathlib.html#pathlib.Path" title="(in Python v3.12)"><em>pathlib.Path</em></a>) – Path to the directory of the .fif / -epo.fif and .json files.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to preload the data.</p></li>
+<li><p><strong>ids_to_load</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Ids of specific files to load.</p></li>
+<li><p><strong>target_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><em>None</em>) – Load specific description column as target. If not given, take saved
 target name.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to be used to read files in parallel.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>concat_dataset</strong></p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a> of BaseDatasets or WindowsDatasets</p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-datautil-load-concat-dataset">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datautil.load_concat_dataset</span></code><a class="headerlink" href="#examples-using-braindecode-datautil-load-concat-dataset" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.datautil.load_concat_dataset</span></code><a class="headerlink" href="#examples-using-braindecode-datautil-load-concat-dataset" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
   <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
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+<h1>braindecode.models.ATCNet<a class="headerlink" href="#braindecode-models-atcnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.ATCNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">ATCNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_kernel_length_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_pool_size_2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_depth_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_head_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_num_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tcn_activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm_const</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L14-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ATCNet" title="Link to this definition">#</a></dt>
+<dd><p>ATCNet model from <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id1">[1]</a></p>
+<p>Pytorch implementation based on official tensorflow code <a class="reference internal" href="#r2ecdb73d6ab9-2" id="id2">[2]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Time length of inputs, in seconds. Defaults to 4.5 s, as in BCI-IV 2a
+dataset.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Sampling frequency of the inputs, in Hz. Default to 250 Hz, as in
+BCI-IV 2a dataset.</p></li>
+<li><p><strong>conv_block_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number temporal filters in the first convolutional layer of the
+convolutional block, denoted F1 in figure 2 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id3">[1]</a>. Defaults
+to 16 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id4">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the first convolutional layer of the
+convolutional block, denoted Kc in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id5">[1]</a>. Defaults
+to 64 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id6">[1]</a>.</p></li>
+<li><p><strong>conv_block_kernel_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal filters in the last convolutional layer of the
+convolutional block. Defaults to 16 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id7">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_1</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block.
+Defaults to 8 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id8">[1]</a>.</p></li>
+<li><p><strong>conv_block_pool_size_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of first average pooling kernel in the convolutional block,
+denoted P2 in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id9">[1]</a>. Defaults to 7 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id10">[1]</a>.</p></li>
+<li><p><strong>conv_block_depth_mult</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier of depthwise convolution in the convolutional block,
+denoted D in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id11">[1]</a>. Defaults to 2 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id12">[1]</a>.</p></li>
+<li><p><strong>conv_block_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the convolution block, denoted pc in
+table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id13">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id14">[1]</a>.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sliding windows, denoted n in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id15">[1]</a>. Defaults to 5 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id16">[1]</a>.</p></li>
+<li><p><strong>att_head_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Embedding dimension used in each self-attention head, denoted dh in
+table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id17">[1]</a>. Defaults to 8 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id18">[1]</a>.</p></li>
+<li><p><strong>att_num_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads, denoted H in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id19">[1]</a>.
+Defaults to 2 as in [<a href="#id32"><span class="problematic" id="id33">1_</span></a>.</p></li>
+<li><p><strong>att_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the attention block, denoted pa in table 1
+of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id20">[1]</a>. Defaults to 0.5 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id21">[1]</a>.</p></li>
+<li><p><strong>tcn_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth of Temporal Convolutional Network block (i.e. number of TCN
+Residual blocks), denoted L in table 1 of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id22">[1]</a>. Defaults to 2
+as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id23">[1]</a>.</p></li>
+<li><p><strong>tcn_kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Temporal kernel size used in TCN block, denoted Kt in table 1 of the
+paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id24">[1]</a>. Defaults to 4 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id25">[1]</a>.</p></li>
+<li><p><strong>tcn_n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of filters used in TCN convolutional layers (Ft). Defaults to
+32 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id26">[1]</a>.</p></li>
+<li><p><strong>tcn_dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability used in the TCN block, denoted pt in table 1
+of the paper <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id27">[1]</a>. Defaults to 0.3 as in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id28">[1]</a>.</p></li>
+<li><p><strong>tcn_activation</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – Nonlinear activation to use. Defaults to nn.ELU().</p></li>
+<li><p><strong>concat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <code class="docutils literal notranslate"><span class="pre">True</span></code>, concatenates each slidding window embedding before
+feeding it to a fully-connected layer, as done in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id29">[1]</a>. When <code class="docutils literal notranslate"><span class="pre">False</span></code>,
+maps each slidding window to <cite>n_outputs</cite> logits and average them.
+Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code> contrary to what is reported in <a class="reference internal" href="#r2ecdb73d6ab9-1" id="id30">[1]</a>, but
+matching what the official code does <a class="reference internal" href="#r2ecdb73d6ab9-2" id="id31">[2]</a>.</p></li>
+<li><p><strong>max_norm_const</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Maximum L2-norm constraint imposed on weights of the last
+fully-connected layer. Defaults to 0.25.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r2ecdb73d6ab9-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id3">2</a>,<a role="doc-backlink" href="#id4">3</a>,<a role="doc-backlink" href="#id5">4</a>,<a role="doc-backlink" href="#id6">5</a>,<a role="doc-backlink" href="#id7">6</a>,<a role="doc-backlink" href="#id8">7</a>,<a role="doc-backlink" href="#id9">8</a>,<a role="doc-backlink" href="#id10">9</a>,<a role="doc-backlink" href="#id11">10</a>,<a role="doc-backlink" href="#id12">11</a>,<a role="doc-backlink" href="#id13">12</a>,<a role="doc-backlink" href="#id14">13</a>,<a role="doc-backlink" href="#id15">14</a>,<a role="doc-backlink" href="#id16">15</a>,<a role="doc-backlink" href="#id17">16</a>,<a role="doc-backlink" href="#id18">17</a>,<a role="doc-backlink" href="#id19">18</a>,<a role="doc-backlink" href="#id20">19</a>,<a role="doc-backlink" href="#id21">20</a>,<a role="doc-backlink" href="#id22">21</a>,<a role="doc-backlink" href="#id23">22</a>,<a role="doc-backlink" href="#id24">23</a>,<a role="doc-backlink" href="#id25">24</a>,<a role="doc-backlink" href="#id26">25</a>,<a role="doc-backlink" href="#id27">26</a>,<a role="doc-backlink" href="#id28">27</a>,<a role="doc-backlink" href="#id29">28</a>,<a role="doc-backlink" href="#id30">29</a>)</span>
+<p>H. Altaheri, G. Muhammad and M. Alsulaiman, “Physics-informed
+attention temporal convolutional network for EEG-based motor imagery
+classification,” in IEEE Transactions on Industrial Informatics,
+2022, doi: 10.1109/TII.2022.3197419.</p>
+</div>
+<div class="citation" id="r2ecdb73d6ab9-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id31">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/Altaheri/EEG-ATCNet/blob/main/models.py">Altaheri/EEG-ATCNet</a></p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.ATCNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/atcnet.py#L239-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ATCNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
+  
+</div>
+
+              
+              
+              
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diff --git a/stable/generated/braindecode.models.Deep4Net.html b/stable/generated/braindecode.models.Deep4Net.html
index e4aa71ed..973b88d0 100644
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 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.models.Deep4Net</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
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@@ -402,10 +469,15 @@
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   <section id="braindecode-models-deep4net">
-<h1>braindecode.models.Deep4Net<a class="headerlink" href="#braindecode-models-deep4net" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.Deep4Net<a class="headerlink" href="#braindecode-models-deep4net" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.Deep4Net">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">Deep4Net</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_2=50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_2=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_3=100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_3=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_4=200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_4=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_before_pool=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/deep4.html#Deep4Net"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.Deep4Net" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">Deep4Net</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_2=50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_2=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_3=100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_3=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_4=200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length_4=10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_conv_nonlin=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_mode='max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">later_pool_nonlin=&lt;function</span> <span class="pre">identity&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_before_pool=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deep4.py#L15-L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.Deep4Net" title="Link to this definition">#</a></dt>
 <dd><p>Deep ConvNet model from Schirrmeister et al 2017.</p>
 <p>Model described in <a class="reference internal" href="#rb8ef6c2733ce-schirrmeister2017" id="id1">[Schirrmeister2017]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG input channels.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes to predict (number of output filters of last layer).</p></li>
-<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Only used to determine the length of the last convolutional kernel if
-final_conv_length is “auto”.</p></li>
-<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Length of the final convolution layer.
-If set to “auto”, input_window_samples must not be None.</p></li>
-<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters.</p></li>
-<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of spatial filters.</p></li>
-<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of the temporal filter in layer 1.</p></li>
-<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
-<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
-<li><p><strong>n_filters_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters in layer 2.</p></li>
-<li><p><strong>filter_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of the temporal filter in layer 2.</p></li>
-<li><p><strong>n_filters_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters in layer 3.</p></li>
-<li><p><strong>filter_length_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of the temporal filter in layer 3.</p></li>
-<li><p><strong>n_filters_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters in layer 4.</p></li>
-<li><p><strong>filter_length_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of the temporal filter in layer 4.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, n_times must not be None. Default: “auto”.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 1.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>n_filters_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 2.</p></li>
+<li><p><strong>filter_length_2</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 2.</p></li>
+<li><p><strong>n_filters_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 3.</p></li>
+<li><p><strong>filter_length_3</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 3.</p></li>
+<li><p><strong>n_filters_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters in layer 4.</p></li>
+<li><p><strong>filter_length_4</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter in layer 4.</p></li>
 <li><p><strong>first_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in layer 1.</p></li>
-<li><p><strong>first_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Pooling mode in layer 1. “max” or “mean”.</p></li>
+<li><p><strong>first_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in layer 1. “max” or “mean”.</p></li>
 <li><p><strong>first_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in layer 1.</p></li>
 <li><p><strong>later_conv_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after convolution in later layers.</p></li>
-<li><p><strong>later_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Pooling mode in later layers. “max” or “mean”.</p></li>
+<li><p><strong>later_pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Pooling mode in later layers. “max” or “mean”.</p></li>
 <li><p><strong>later_pool_nonlin</strong> (<em>callable</em>) – Non-linear activation function to be used after pooling in later layers.</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout probability.</p></li>
-<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
 There would be no non-linearity between the split layers.</p></li>
-<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
-<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
-<li><p><strong>stride_before_pool</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Stride before pooling.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>stride_before_pool</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Stride before pooling.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rb8ef6c2733ce-schirrmeister2017"><span class="brackets"><a class="fn-backref" href="#id1">Schirrmeister2017</a></span></dt>
-<dd><p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+<div role="list" class="citation-list">
+<div class="citation" id="rb8ef6c2733ce-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
 L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
 &amp; Ball, T. (2017).
 Deep learning with convolutional neural networks for EEG decoding and
 visualization.
 Human Brain Mapping , Aug. 2017.
 Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 <section id="examples-using-braindecode-models-deep4net">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.Deep4Net</span></code><a class="headerlink" href="#examples-using-braindecode-models-deep4net" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="Benchmarking eager and lazy loading" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.Deep4Net</span></code><a class="headerlink" href="#examples-using-braindecode-models-deep4net" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
 </div></div><div style='clear:both'></div></section>
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+  <section id="braindecode-models-deepsleepnet">
+<h1>braindecode.models.DeepSleepNet<a class="headerlink" href="#braindecode-models-deepsleepnet" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.DeepSleepNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">DeepSleepNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L167-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.DeepSleepNet" title="Link to this definition">#</a></dt>
+<dd><p>Sleep staging architecture from Supratak et al 2017.</p>
+<p>Convolutional neural network and bidirectional-Long Short-Term
+for single channels sleep staging described in <a class="reference internal" href="#r28b528aca953-supratak2017" id="id1">[Supratak2017]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+(before the final linear layer). If False, pass the features through
+the final linear layer.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r28b528aca953-supratak2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Supratak2017</a><span class="fn-bracket">]</span></span>
+<p>Supratak, A., Dong, H., Wu, C., &amp; Guo, Y. (2017).
+DeepSleepNet: A model for automatic sleep stage scoring based
+on raw single-channel EEG. IEEE Transactions on Neural Systems
+and Rehabilitation Engineering, 25(11), 1998-2008.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.DeepSleepNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/deepsleepnet.py#L232-L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.DeepSleepNet.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.DeepSleepNet.forward"><code class="docutils literal notranslate"><span class="pre">DeepSleepNet.forward()</span></code></a></li>
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+  <section id="braindecode-models-eegconformer">
+<h1>braindecode.models.EEGConformer<a class="headerlink" href="#braindecode-models-eegconformer" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGConformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">att_drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_fc_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2440</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_features</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L14-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer" title="Link to this definition">#</a></dt>
+<dd><p>EEG Conformer.</p>
+<p>Convolutional Transformer for EEG decoding.</p>
+<p>The paper and original code with more details about the methodological
+choices are available at the <a class="reference internal" href="#rd6c0fefc356a-song2022" id="id1">[Song2022]</a> and <a class="reference internal" href="#rd6c0fefc356a-conformercode" id="id2">[ConformerCode]</a>.</p>
+<p>This neural network architecture receives a traditional braindecode input.
+The input shape should be three-dimensional matrix representing the EEG
+signals.</p>
+<blockquote>
+<div><p><cite>(batch_size, n_channels, n_timesteps)</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>The EEG Conformer architecture is composed of three modules:</dt><dd><ul class="simple">
+<li><p>PatchEmbedding</p></li>
+<li><p>TransformerEncoder</p></li>
+<li><p>ClassificationHead</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters, defines also embedding size.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the convolutional layer.</p></li>
+<li><p><strong>att_depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of self-attention layers.</p></li>
+<li><p><strong>att_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads.</p></li>
+<li><p><strong>att_drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate of the self-attention layer.</p></li>
+<li><p><strong>final_fc_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – The dimension of the fully connected layer.</p></li>
+<li><p><strong>return_features</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, the forward method returns the features before the
+last classification layer. Defaults to False.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>n_channels</strong> – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The authors recommend using data augmentation before using Conformer,
+e.g. segmentation and recombination,
+Please refer to the original paper and code for more details.</p>
+<p>The model was initially tuned on 4 seconds of 250 Hz data.
+Please adjust the scale of the temporal convolutional layer,
+and the pooling layer for better performance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.8.</span></p>
+</div>
+<p>We aggregate the parameters based on the parts of the models, or
+when the parameters were used first, e.g. n_filters_time.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rd6c0fefc356a-song2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Song2022</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering,
+31, pp.710-719. <a class="reference external" href="https://ieeexplore.ieee.org/document/9991178">https://ieeexplore.ieee.org/document/9991178</a></p>
+</div>
+<div class="citation" id="rd6c0fefc356a-conformercode" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">ConformerCode</a><span class="fn-bracket">]</span></span>
+<p>Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG
+conformer: Convolutional transformer for EEG decoding and visualization.
+<a class="github reference external" href="https://github.com/eeyhsong/EEG-Conformer">eeyhsong/EEG-Conformer</a>.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L163-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGConformer.get_fc_size">
+<span class="sig-name descname"><span class="pre">get_fc_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegconformer.py#L171-L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGConformer.get_fc_size" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGConformer.forward"><code class="docutils literal notranslate"><span class="pre">EEGConformer.forward()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGConformer.get_fc_size"><code class="docutils literal notranslate"><span class="pre">EEGConformer.get_fc_size()</span></code></a></li>
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diff --git a/stable/generated/braindecode.models.EEGITNet.html b/stable/generated/braindecode.models.EEGITNet.html
index d04e6c80..b4c13542 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-models-eegitnet">
-<h1>braindecode.models.EEGITNet<a class="headerlink" href="#braindecode-models-eegitnet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.EEGITNet<a class="headerlink" href="#braindecode-models-eegitnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.EEGITNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGITNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/eegitnet.html#EEGITNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGITNet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGITNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegitnet.py#L89-L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGITNet" title="Link to this definition">#</a></dt>
 <dd><dl class="simple">
 <dt>EEG-ITNet: An Explainable Inception Temporal</dt><dd><p>Convolutional Network for motor imagery classification from
 Salami et. al 2022.</p>
@@ -498,130 +596,140 @@ <h1>braindecode.models.EEGITNet<a class="headerlink" href="#braindecode-models-e
 <p>See <a class="reference internal" href="#r7fe571f46200-salami2022" id="id1">[Salami2022]</a> for details.</p>
 <p>Code adapted from <a class="github reference external" href="https://github.com/abbassalami/eeg-itnet">abbassalami/eeg-itnet</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of outputs of the decoding task (for example number of classes in
-classification)</p></li>
-<li><p><strong>n_in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of input EEG channels</p></li>
-<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of time samples.</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on author implementation.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r7fe571f46200-salami2022"><span class="brackets"><a class="fn-backref" href="#id1">Salami2022</a></span></dt>
-<dd><ol class="upperalpha simple">
+<div role="list" class="citation-list">
+<div class="citation" id="r7fe571f46200-salami2022" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Salami2022</a><span class="fn-bracket">]</span></span>
+<ol class="upperalpha simple">
 <li><p>Salami, J. Andreu-Perez and H. Gillmeister, “EEG-ITNet: An Explainable</p></li>
 </ol>
-</dd>
-</dl>
+</div>
+</div>
 <p>Inception Temporal Convolutional Network for motor imagery classification,” in IEEE Access,
 doi: 10.1109/ACCESS.2022.3161489.</p>
-<p class="rubric">Notes</p>
-<p>This implementation is not guaranteed to be correct, has not been checked
-by original authors, only reimplemented from the paper based on author implementation.</p>
 </dd></dl>
 
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diff --git a/stable/generated/braindecode.models.EEGInception.html b/stable/generated/braindecode.models.EEGInception.html
index 44c014fe..97f20bb4 100644
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+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,42 +530,71 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-models-eeginception">
-<h1>braindecode.models.EEGInception<a class="headerlink" href="#braindecode-models-eeginception" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.EEGInception<a class="headerlink" href="#braindecode-models-eeginception" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.EEGInception">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/eeginception.html#EEGInception"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInception" title="Permalink to this definition">#</a></dt>
-<dd><p>EEG Inception.</p>
-<p>EEG Inception for ERP-based classification described in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id1">[Santamaria2020]</a>.
-The code for the paper and this model is also available at <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id2">[Santamaria2020]</a>
-and an adaptation for PyTorch <a class="reference internal" href="#r15fdf33b022f-2" id="id3">[2]</a>.</p>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception.py#L17-L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInception" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for ERP-based classification</p>
+<p>–&gt; DEPRECATED &lt;–
+THIS CLASS IS DEPRECATED AND WILL BE REMOVED IN THE RELEASE 0.9 OF
+BRAINDECODE. PLEASE USE braindecode.models.EEGInceptionERP INSTEAD IN THE
+FUTURE.</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id1">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#r15fdf33b022f-2" id="id2">[2]</a>.</p>
 <p>The model is strongly based on the original InceptionNet for an image. The main goal is
 to extract features in parallel with different scales. The authors extracted three scales
 proportional to the window sample size. The network had three parts:
@@ -501,148 +602,157 @@ <h1>braindecode.models.EEGInception<a class="headerlink" href="#braindecode-mode
 for classification.</p>
 <p>One advantage of the EEG-Inception block is that it allows a network
 to learn simultaneous components of low and high frequency associated with the signal.
-The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id4">[R15fdf33b022f-beetl]</span>.</p>
-<p>The model is fully described in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id5">[Santamaria2020]</a>.</p>
-<p class="rubric">Notes</p>
-<p>This implementation is not guaranteed to be correct, has not been checked
-by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r15fdf33b022f-2" id="id6">[2]</a>.</p>
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id3">[R15fdf33b022f-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id4">[Santamaria2020]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG channels.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes.</p></li>
-<li><p><strong>input_size_ms</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Size of the input, in milliseconds. Set to 1000 in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id7">[Santamaria2020]</a>.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – EEG sampling frequency.</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout rate inside all the network.</p></li>
-<li><p><strong>scales_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>)</em>) – Windows for inception block, must be a list with proportional values of
-the input_size_ms.
-According to the authors: temporal scale (ms) of the convolutions
-on each Inception module.
-This parameter determines the kernel sizes of the filters.</p></li>
-<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Initial number of convolutional filters. Set to 8 in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id8">[Santamaria2020]</a>.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate inside all the network.</p></li>
+<li><p><strong>scales_samples_s</strong> – The description is missing.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 8 in <a class="reference internal" href="#r15fdf33b022f-santamaria2020" id="id5">[Santamaria2020]</a>.</p></li>
 <li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function, default: ELU activation.</p></li>
-<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
-<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Depth multiplier for the depthwise convolution.</p></li>
-<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>)</em>) – Pooling sizes for the inception block.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Depth multiplier for the depthwise convolution.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Pooling sizes for the inception block.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r15fdf33b022f-2" id="id6">[2]</a>.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r15fdf33b022f-santamaria2020"><span class="brackets">Santamaria2020</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id2">2</a>,<a href="#id5">3</a>,<a href="#id7">4</a>,<a href="#id8">5</a>)</span></dt>
-<dd><p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+<div role="list" class="citation-list">
+<div class="citation" id="r15fdf33b022f-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
 Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
 EEG-inception: A novel deep convolutional neural network for assistive
 ERP-based brain-computer interfaces.
 IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
 Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
-</dd>
-<dt class="label" id="r15fdf33b022f-2"><span class="brackets">2</span><span class="fn-backref">(<a href="#id3">1</a>,<a href="#id6">2</a>)</span></dt>
-<dd><p>Grifcc. Implementation of the EEGInception in torch (2022).
+</div>
+<div class="citation" id="r15fdf33b022f-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id6">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
 Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
-</dd>
-</dl>
+</div>
+</div>
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+<h1>braindecode.models.EEGInceptionERP<a class="headerlink" href="#braindecode-models-eeginceptionerp" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionERP">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionERP</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scales_samples_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">0.25,</span> <span class="pre">0.125)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ELU(alpha=1.0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth_multiplier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(4,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_erp.py#L16-L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionERP" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for ERP-based classification</p>
+<p>The code for the paper and this model is also available at <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id1">[Santamaria2020]</a>
+and an adaptation for PyTorch <a class="reference internal" href="#r37c4761d4e92-2" id="id2">[2]</a>.</p>
+<p>The model is strongly based on the original InceptionNet for an image. The main goal is
+to extract features in parallel with different scales. The authors extracted three scales
+proportional to the window sample size. The network had three parts:
+1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
+for classification.</p>
+<p>One advantage of the EEG-Inception block is that it allows a network
+to learn simultaneous components of low and high frequency associated with the signal.
+The winners of BEETL Competition/NeurIps 2021 used parts of the model <span id="id3">[R37c4761d4e92-beetl]</span>.</p>
+<p>The model is fully described in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id4">[Santamaria2020]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Size of the input, in number of samples. Set to 128 (1s) as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id5">[Santamaria2020]</a>.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency. Defaults to 128 as in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id6">[Santamaria2020]</a>.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Dropout rate inside all the network. Defaults to 0.5 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id7">[Santamaria2020]</a>.</p></li>
+<li><p><strong>scales_samples_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>)</em><em>, </em><em>optional</em>) – Windows for inception block. Temporal scale (s) of the convolutions on
+each Inception module. This parameter determines the kernel sizes of
+the filters. Defaults to 0.5, 0.25, 0.125 seconds, as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id8">[Santamaria2020]</a>.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Initial number of convolutional filters. Defaults to 8 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id9">[Santamaria2020]</a>.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – Activation function. Defaults to ELU activation as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id10">[Santamaria2020]</a>.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Momentum for BatchNorm2d. Defaults to 0.01.</p></li>
+<li><p><strong>depth_multiplier</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Depth multiplier for the depthwise convolution. Defaults to 2 as in
+<a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id11">[Santamaria2020]</a>.</p></li>
+<li><p><strong>pooling_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em><em>, </em><em>optional</em>) – Pooling sizes for the inception blocks. Defaults to 4, 2, 2 and 2, as
+in <a class="reference internal" href="#r37c4761d4e92-santamaria2020" id="id12">[Santamaria2020]</a>.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented from the paper based on <a class="reference internal" href="#r37c4761d4e92-2" id="id13">[2]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="r37c4761d4e92-santamaria2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Santamaria2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id4">2</a>,<a role="doc-backlink" href="#id5">3</a>,<a role="doc-backlink" href="#id6">4</a>,<a role="doc-backlink" href="#id7">5</a>,<a role="doc-backlink" href="#id8">6</a>,<a role="doc-backlink" href="#id9">7</a>,<a role="doc-backlink" href="#id10">8</a>,<a role="doc-backlink" href="#id11">9</a>,<a role="doc-backlink" href="#id12">10</a>)</span>
+<p>Santamaria-Vazquez, E., Martinez-Cagigal, V.,
+Vaquerizo-Villar, F., &amp; Hornero, R. (2020).
+EEG-inception: A novel deep convolutional neural network for assistive
+ERP-based brain-computer interfaces.
+IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
+Online: <a class="reference external" href="http://dx.doi.org/10.1109/TNSRE.2020.3048106">http://dx.doi.org/10.1109/TNSRE.2020.3048106</a></p>
+</div>
+<div class="citation" id="r37c4761d4e92-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>2<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id13">2</a>)</span>
+<p>Grifcc. Implementation of the EEGInception in torch (2022).
+Online: <a class="github reference external" href="https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022">Grifcc/EEG</a></p>
+</div>
+</div>
+</dd></dl>
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+  <section id="braindecode-models-eeginceptionmi">
+<h1>braindecode.models.EEGInceptionMI<a class="headerlink" href="#braindecode-models-eeginceptionmi" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionMI">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGInceptionMI</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">250</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_convs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">48</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_unit_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ReLU()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L13-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionMI" title="Link to this definition">#</a></dt>
+<dd><p>EEG Inception for Motor Imagery, as proposed in <a class="reference internal" href="#rc36ed781f4f5-1" id="id1">[1]</a></p>
+<p>The model is strongly based on the original InceptionNet for computer
+vision. The main goal is to extract features in parallel with different
+scales. The network has two blocks made of 3 inception modules with a skip
+connection.</p>
+<p>The model is fully described in <a class="reference internal" href="#rc36ed781f4f5-1" id="id2">[1]</a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size of the input, in seconds. Set to 4.5 s as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id3">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – EEG sampling frequency in Hz. Defaults to 250 Hz as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id4">[1]</a> for dataset
+BCI IV 2a.</p></li>
+<li><p><strong>n_convs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolution per inception wide branching. Defaults to 5 as
+in <a class="reference internal" href="#rc36ed781f4f5-1" id="id5">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – Number of convolutional filters for all layers of this type. Set to 48
+as in <a class="reference internal" href="#rc36ed781f4f5-1" id="id6">[1]</a> for dataset BCI IV 2a.</p></li>
+<li><p><strong>kernel_unit_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Size in seconds of the basic 1D convolutional kernel used in inception
+modules. Each convolutional layer in such modules have kernels of
+increasing size, odd multiples of this value (e.g. 0.1, 0.3, 0.5, 0.7,
+0.9 here for <a href="#id7"><span class="problematic" id="id8">`</span></a>n_convs`=5). Defaults to 0.1 s.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em>) – Activation function. Defaults to ReLU activation.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>This implementation is not guaranteed to be correct, has not been checked
+by original authors, only reimplemented bosed on the paper <a class="reference internal" href="#rc36ed781f4f5-1" id="id9">[1]</a>.</p>
+<p class="rubric">References</p>
+<div role="list" class="citation-list">
+<div class="citation" id="rc36ed781f4f5-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>,<a role="doc-backlink" href="#id6">6</a>,<a role="doc-backlink" href="#id9">7</a>)</span>
+<p>Zhang, C., Kim, Y. K., &amp; Eskandarian, A. (2021).
+EEG-inception: an accurate and robust end-to-end neural network
+for EEG-based motor imagery classification.
+Journal of Neural Engineering, 18(4), 046014.</p>
+</div>
+</div>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGInceptionMI.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><span class="pre">Tensor</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eeginception_mi.py#L185-L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGInceptionMI.forward" title="Link to this definition">#</a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>X</strong> – The description is missing.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGInceptionMI"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGInceptionMI.forward"><code class="docutils literal notranslate"><span class="pre">EEGInceptionMI.forward()</span></code></a></li>
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+  <section id="braindecode-models-eegmodulemixin">
+<h1>braindecode.models.EEGModuleMixin<a class="headerlink" href="#braindecode-models-eegmodulemixin" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.12)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.12)"><span class="pre">Dict</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L34-L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin" title="Link to this definition">#</a></dt>
+<dd><p>Mixin class for all EEG models in braindecode.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
+<p class="rubric">Methods</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.get_output_shape">
+<span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L189-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.get_output_shape" title="Link to this definition">#</a></dt>
+<dd><p>Returns shape of neural network output for batch size equal 1.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Tuple[<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.get_torchinfo_statistics">
+<span class="sig-name descname"><span class="pre">get_torchinfo_statistics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">col_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('input_size',</span> <span class="pre">'output_size',</span> <span class="pre">'num_params',</span> <span class="pre">'kernel_size')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_settings</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Iterable" title="(in Python v3.12)"><span class="pre">Iterable</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">('var_names',</span> <span class="pre">'depth')</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ModelStatistics</span></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L277-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.get_torchinfo_statistics" title="Link to this definition">#</a></dt>
+<dd><p>Generate table describing the model using torchinfo.summary.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>col_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which columns to show in the output, see torchinfo for details, by default
+(“input_size”, “output_size”, “num_params”, “kernel_size”)</p></li>
+<li><p><strong>row_settings</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Specify which features to show in a row, see torchinfo for details, by default
+(“var_names”, “depth”)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>ModelStatistics generated by torchinfo.summary.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torchinfo.ModelStatistics</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.load_state_dict">
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L222-L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.load_state_dict" title="Link to this definition">#</a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="braindecode.models.EEGModuleMixin.to_dense_prediction_model">
+<span class="sig-name descname"><span class="pre">to_dense_prediction_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Tuple" title="(in Python v3.12)"><span class="pre">Tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(2,</span> <span class="pre">3)</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/base.py#L234-L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGModuleMixin.to_dense_prediction_model" title="Link to this definition">#</a></dt>
+<dd><p>Transform a sequential model with strides to a model that outputs
+dense predictions by removing the strides and instead inserting dilations.
+Modifies model in-place.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>axis</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> or </em><em>(</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>,</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>)</em>) – Axis to transform (in terms of intermediate output axes)
+can either be 2, 3, or (2,3).</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Does not yet work correctly for average pooling.
+Prior to version 0.1.7, there had been a bug that could move strides
+backwards one layer.</p>
+</dd></dl>
+
+</dd></dl>
+
+<section id="examples-using-braindecode-models-eegmodulemixin">
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.EEGModuleMixin</span></code><a class="headerlink" href="#examples-using-braindecode-models-eegmodulemixin" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div></div><div style='clear:both'></div></section>
+</section>
+
+
+  
+</div>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.get_output_shape"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_output_shape()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.get_torchinfo_statistics"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.get_torchinfo_statistics()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.load_state_dict"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.load_state_dict()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGModuleMixin.to_dense_prediction_model"><code class="docutils literal notranslate"><span class="pre">EEGModuleMixin.to_dense_prediction_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-models-eegmodulemixin">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.EEGModuleMixin</span></code></a></li>
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-models-eegnetv1">
-<h1>braindecode.models.EEGNetv1<a class="headerlink" href="#braindecode-models-eegnetv1" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.EEGNetv1<a class="headerlink" href="#braindecode-models-eegnetv1" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.EEGNetv1">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">32)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/eegnet.html#EEGNetv1"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv1" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv1</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'max'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">second_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">32)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L217-L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv1" title="Link to this definition">#</a></dt>
 <dd><p>EEGNet model from Lawhern et al. 2016.</p>
 <p>See details in <a class="reference internal" href="#rcbe3da2652d4-eegnet" id="id1">[EEGNet]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – XXX</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> – The description is missing.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>second_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>This implementation is not guaranteed to be correct, has not been checked
 by original authors, only reimplemented from the paper description.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rcbe3da2652d4-eegnet"><span class="brackets"><a class="fn-backref" href="#id1">EEGNet</a></span></dt>
-<dd><p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+<div role="list" class="citation-list">
+<div class="citation" id="rcbe3da2652d4-eegnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">EEGNet</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
 S. M., Hung, C. P., &amp; Lance, B. J. (2016).
 EEGNet: A Compact Convolutional Network for EEG-based
 Brain-Computer Interfaces.
 arXiv preprint arXiv:1611.08024.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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   <section id="braindecode-models-eegnetv4">
-<h1>braindecode.models.EEGNetv4<a class="headerlink" href="#braindecode-models-eegnetv4" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.EEGNetv4<a class="headerlink" href="#braindecode-models-eegnetv4" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.EEGNetv4">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/eegnet.html#EEGNetv4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv4" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGNetv4</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">F2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">third_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(8,</span> <span class="pre">4)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegnet.py#L27-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGNetv4" title="Link to this definition">#</a></dt>
 <dd><p>EEGNet v4 model from Lawhern et al 2018.</p>
 <p>See details in <a class="reference internal" href="#rbd7837e27d7f-eegnet4" id="id1">[EEGNet4]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – XXX</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>&quot;auto&quot;</em>) – If int, final length of convolutional filters.</p></li>
+<li><p><strong>pool_mode</strong> – The description is missing.</p></li>
+<li><p><strong>F1</strong> – The description is missing.</p></li>
+<li><p><strong>D</strong> – The description is missing.</p></li>
+<li><p><strong>F2</strong> – The description is missing.</p></li>
+<li><p><strong>kernel_length</strong> – The description is missing.</p></li>
+<li><p><strong>third_kernel_size</strong> – The description is missing.</p></li>
+<li><p><strong>drop_prob</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>This implementation is not guaranteed to be correct, has not been checked
 by original authors, only reimplemented from the paper description.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rbd7837e27d7f-eegnet4"><span class="brackets"><a class="fn-backref" href="#id1">EEGNet4</a></span></dt>
-<dd><p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
+<div role="list" class="citation-list">
+<div class="citation" id="rbd7837e27d7f-eegnet4" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">EEGNet4</a><span class="fn-bracket">]</span></span>
+<p>Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
 S. M., Hung, C. P., &amp; Lance, B. J. (2018).
 EEGNet: A Compact Convolutional Network for EEG-based
 Brain-Computer Interfaces.
 arXiv preprint arXiv:1611.08024.</p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
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diff --git a/stable/generated/braindecode.models.EEGResNet.html b/stable/generated/braindecode.models.EEGResNet.html
index 3a851170..c9ecad9e 100644
--- a/stable/generated/braindecode.models.EEGResNet.html
+++ b/stable/generated/braindecode.models.EEGResNet.html
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-models-eegresnet">
-<h1>braindecode.models.EEGResNet<a class="headerlink" href="#braindecode-models-eegresnet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.EEGResNet<a class="headerlink" href="#braindecode-models-eegresnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.EEGResNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGResNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_pool_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_first_filters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layers_per_block=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_filter_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_epsilon=0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_weight_init_fn=&lt;function</span> <span class="pre">EEGResNet.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/eegresnet.html#EEGResNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGResNet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">EEGResNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_pool_length=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_first_filters=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_layers_per_block=2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">first_filter_length=3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity=&lt;function</span> <span class="pre">elu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_epsilon=0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_weight_init_fn=&lt;function</span> <span class="pre">EEGResNet.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/eegresnet.py#L19-L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.EEGResNet" title="Link to this definition">#</a></dt>
 <dd><p>Residual Network for EEG.</p>
 <p>XXX missing reference</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – XXX</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>final_pool_length</strong> – The description is missing.</p></li>
+<li><p><strong>n_first_filters</strong> – The description is missing.</p></li>
+<li><p><strong>n_layers_per_block</strong> – The description is missing.</p></li>
+<li><p><strong>first_filter_length</strong> – The description is missing.</p></li>
+<li><p><strong>nonlinearity</strong> – The description is missing.</p></li>
+<li><p><strong>split_first_layer</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_alpha</strong> – The description is missing.</p></li>
+<li><p><strong>batch_norm_epsilon</strong> – The description is missing.</p></li>
+<li><p><strong>conv_weight_init_fn</strong> – The description is missing.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> – <p>Alias for <cite>n_chans</cite>.
+n_classes :</p>
+<blockquote>
+<div><p>Alias for <cite>n_outputs</cite>.</p>
+</div></blockquote>
+<dl class="simple">
+<dt>input_window_samples :</dt><dd><p>Alias for <cite>n_times</cite>.</p>
 </dd>
 </dl>
+</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 </dd></dl>
 
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.EEGResNet"><code class="docutils literal notranslate"><span class="pre">EEGResNet</span></code></a></li>
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diff --git a/stable/generated/braindecode.models.HybridNet.html b/stable/generated/braindecode.models.HybridNet.html
index f8afc55b..44d2bf31 100644
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-models-hybridnet">
-<h1>braindecode.models.HybridNet<a class="headerlink" href="#braindecode-models-hybridnet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.HybridNet<a class="headerlink" href="#braindecode-models-hybridnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.HybridNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">HybridNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/hybrid.html#HybridNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">HybridNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L15-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet" title="Link to this definition">#</a></dt>
 <dd><p>Hybrid ConvNet model from Schirrmeister et al 2017.</p>
 <p>See <a class="reference internal" href="#r4d1af390a17e-schirrmeister2017" id="id1">[Schirrmeister2017]</a> for details.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – The description is missing.</p></li>
+<li><p><strong>n_classes</strong> – The description is missing.</p></li>
+<li><p><strong>input_window_samples</strong> – The description is missing.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r4d1af390a17e-schirrmeister2017"><span class="brackets"><a class="fn-backref" href="#id1">Schirrmeister2017</a></span></dt>
-<dd><p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+<div role="list" class="citation-list">
+<div class="citation" id="r4d1af390a17e-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
 L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
 &amp; Ball, T. (2017).
 Deep learning with convolutional neural networks for EEG decoding and
 visualization.
 Human Brain Mapping , Aug. 2017.
 Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.HybridNet.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/hybrid.html#HybridNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/hybrid.py#L117-L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.HybridNet.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
 </dd>
 </dl>
 </dd></dl>
@@ -521,113 +647,96 @@ <h1>braindecode.models.HybridNet<a class="headerlink" href="#braindecode-models-
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.models.HybridNet">
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-      </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.HybridNet"><code class="docutils literal notranslate"><span class="pre">HybridNet</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.HybridNet.forward"><code class="docutils literal notranslate"><span class="pre">HybridNet.forward()</span></code></a></li>
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diff --git a/stable/generated/braindecode.models.ShallowFBCSPNet.html b/stable/generated/braindecode.models.ShallowFBCSPNet.html
index 960bdb90..a62cf001 100644
--- a/stable/generated/braindecode.models.ShallowFBCSPNet.html
+++ b/stable/generated/braindecode.models.ShallowFBCSPNet.html
@@ -1,362 +1,429 @@
 
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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   <section id="braindecode-models-shallowfbcspnet">
-<h1>braindecode.models.ShallowFBCSPNet<a class="headerlink" href="#braindecode-models-shallowfbcspnet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.ShallowFBCSPNet<a class="headerlink" href="#braindecode-models-shallowfbcspnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.ShallowFBCSPNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">ShallowFBCSPNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_nonlin=&lt;function</span> <span class="pre">square&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode='mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_nonlin=&lt;function</span> <span class="pre">safe_log&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/shallow_fbcsp.html#ShallowFBCSPNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ShallowFBCSPNet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">ShallowFBCSPNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_time=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_time_length=25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters_spat=40</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_length=75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_time_stride=15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_conv_length=30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_nonlin=&lt;function</span> <span class="pre">square&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_mode='mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool_nonlin=&lt;function</span> <span class="pre">safe_log&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_first_layer=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_norm_alpha=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob=0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/shallow_fbcsp.py#L14-L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.ShallowFBCSPNet" title="Link to this definition">#</a></dt>
 <dd><p>Shallow ConvNet model from Schirrmeister et al 2017.</p>
 <p>Model described in <a class="reference internal" href="#r9432a19f6121-schirrmeister2017" id="id1">[Schirrmeister2017]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG input channels.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes to predict (number of output filters of last layer).</p></li>
-<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Only used to determine the length of the last convolutional kernel if
-final_conv_length is “auto”.</p></li>
-<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters.</p></li>
-<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of the temporal filter.</p></li>
-<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of spatial filters.</p></li>
-<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
-<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
-<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Length of the final convolution layer.
-If set to “auto”, input_window_samples must not be None.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_filters_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>filter_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of the temporal filter.</p></li>
+<li><p><strong>n_filters_spat</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of spatial filters.</p></li>
+<li><p><strong>pool_time_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of temporal pooling filter.</p></li>
+<li><p><strong>pool_time_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Length of stride between temporal pooling filters.</p></li>
+<li><p><strong>final_conv_length</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Length of the final convolution layer.
+If set to “auto”, length of the input signal must be specified.</p></li>
 <li><p><strong>conv_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after convolution layers.</p></li>
-<li><p><strong>pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – Method to use on pooling layers. “max” or “mean”.</p></li>
+<li><p><strong>pool_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – Method to use on pooling layers. “max” or “mean”.</p></li>
 <li><p><strong>pool_nonlin</strong> (<em>callable</em>) – Non-linear function to be used after pooling layers.</p></li>
-<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
+<li><p><strong>split_first_layer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Split first layer into temporal and spatial layers (True) or just use temporal (False).
 There would be no non-linearity between the split layers.</p></li>
-<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
-<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use batch normalisation.</p></li>
+<li><p><strong>batch_norm_alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Momentum for BatchNorm2d.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Alias for <cite>n_times</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r9432a19f6121-schirrmeister2017"><span class="brackets"><a class="fn-backref" href="#id1">Schirrmeister2017</a></span></dt>
-<dd><p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
+<div role="list" class="citation-list">
+<div class="citation" id="r9432a19f6121-schirrmeister2017" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Schirrmeister2017</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer,
 L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F.
 &amp; Ball, T. (2017).
 Deep learning with convolutional neural networks for EEG decoding and
 visualization.
 Human Brain Mapping , Aug. 2017.
 Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 <section id="examples-using-braindecode-models-shallowfbcspnet">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.ShallowFBCSPNet</span></code><a class="headerlink" href="#examples-using-braindecode-models-shallowfbcspnet" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.ShallowFBCSPNet</span></code><a class="headerlink" href="#examples-using-braindecode-models-shallowfbcspnet" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="The braindecode library gives you access to a large number of neural network architectures that..."><img alt="" src="../_images/sphx_glr_plot_basic_training_epochs_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_basic_training_epochs.html#sphx-glr-auto-examples-model-building-plot-basic-training-epochs-py"><span class="std std-ref">Simple training on MNE epochs</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Simple training on MNE epochs</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
   <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="Benchmarking eager and lazy loading" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
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@@ -458,78 +530,131 @@
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   <section id="braindecode-models-sleepstagerblanco2020">
-<h1>braindecode.models.SleepStagerBlanco2020<a class="headerlink" href="#braindecode-models-sleepstagerblanco2020" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.SleepStagerBlanco2020<a class="headerlink" href="#braindecode-models-sleepstagerblanco2020" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerBlanco2020">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerBlanco2020</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_blanco_2020.html#SleepStagerBlanco2020"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerBlanco2020</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L11-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020" title="Link to this definition">#</a></dt>
 <dd><p>Sleep staging architecture from Blanco et al 2020.</p>
 <p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id1">[Blanco2020]</a>.
 A series of seven convolutional layers with kernel sizes running down from 7 to 3,
 in an attempt to extract more general features at the beginning, while more specific
 and complex features were extracted in the final stages.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG channels.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – EEG sampling frequency.</p></li>
-<li><p><strong>n_conv_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of convolutional channels. Set to 20 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id2">[Blanco2020]</a>.</p></li>
-<li><p><strong>n_groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of groups for the convolution. Set to 2 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id3">[Blanco2020]</a> for 2 Channel EEG.
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 20 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id2">[Blanco2020]</a>.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of groups for the convolution. Set to 2 in <a class="reference internal" href="#rb3eee9d9e81a-blanco2020" id="id3">[Blanco2020]</a> for 2 Channel EEG.
 controls the connections between inputs and outputs. n_channels and n_conv_chans must be
 divisible by n_groups.</p></li>
-<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of the input, in seconds.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes.</p></li>
-<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
-<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+<li><p><strong>max_pool_size</strong> – The description is missing.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
 layers.</p></li>
-<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
 (before the final linear layer). If False, pass the features through
 the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rb3eee9d9e81a-blanco2020"><span class="brackets">Blanco2020</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id2">2</a>,<a href="#id3">3</a>)</span></dt>
-<dd><p>Fernandez-Blanco, E., Rivero, D. &amp; Pazos, A. Convolutional
+<div role="list" class="citation-list">
+<div class="citation" id="rb3eee9d9e81a-blanco2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Blanco2020<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>)</span>
+<p>Fernandez-Blanco, E., Rivero, D. &amp; Pazos, A. Convolutional
 neural networks for sleep stage scoring on a two-channel EEG signal.
 Soft Comput 24, 4067–4079 (2020). <a class="reference external" href="https://doi.org/10.1007/s00500-019-04174-1">https://doi.org/10.1007/s00500-019-04174-1</a></p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerBlanco2020.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_blanco_2020.html#SleepStagerBlanco2020.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020.forward" title="Permalink to this definition">#</a></dt>
-<dd><p>Forward pass.
-:param x: Batch of EEG windows of shape (batch_size, n_channels, n_times).
-:type x: torch.Tensor</p>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_blanco_2020.py#L144-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerBlanco2020.forward" title="Link to this definition">#</a></dt>
+<dd><p>Forward pass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+</dd>
+</dl>
 </dd></dl>
 
 </dd></dl>
@@ -537,113 +662,96 @@ <h1>braindecode.models.SleepStagerBlanco2020<a class="headerlink" href="#brainde
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.models.SleepStagerBlanco2020">
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-     SleepStagerBlanco2020
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- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerBlanco2020"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerBlanco2020.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerBlanco2020.forward()</span></code></a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,81 +530,125 @@
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   <section id="braindecode-models-sleepstagerchambon2018">
-<h1>braindecode.models.SleepStagerChambon2018<a class="headerlink" href="#braindecode-models-sleepstagerchambon2018" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.SleepStagerChambon2018<a class="headerlink" href="#braindecode-models-sleepstagerchambon2018" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerChambon2018">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerChambon2018</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_chambon_2018.html#SleepStagerChambon2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerChambon2018</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_conv_chs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_pool_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L11-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018" title="Link to this definition">#</a></dt>
 <dd><p>Sleep staging architecture from Chambon et al 2018.</p>
 <p>Convolutional neural network for sleep staging described in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id1">[Chambon2018]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG channels.</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – EEG sampling frequency.</p></li>
-<li><p><strong>n_conv_chs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of convolutional channels. Set to 8 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id2">[Chambon2018]</a>.</p></li>
-<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of filters in temporal convolution layers, in seconds. Set to 0.5
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_conv_chs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of convolutional channels. Set to 8 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id2">[Chambon2018]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of filters in temporal convolution layers, in seconds. Set to 0.5
 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id3">[Chambon2018]</a> (64 samples at sfreq=128).</p></li>
-<li><p><strong>max_pool_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Max pooling size, in seconds. Set to 0.125 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id4">[Chambon2018]</a> (16
+<li><p><strong>max_pool_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Max pooling size, in seconds. Set to 0.125 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id4">[Chambon2018]</a> (16
 samples at sfreq=128).</p></li>
-<li><p><strong>pad_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Padding size, in seconds. Set to 0.25 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id5">[Chambon2018]</a> (half the
+<li><p><strong>pad_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Padding size, in seconds. Set to 0.25 in <a class="reference internal" href="#r89163c5eab6a-chambon2018" id="id5">[Chambon2018]</a> (half the
 temporal convolution kernel size).</p></li>
-<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of the input, in seconds.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes.</p></li>
-<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
-<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate before the output dense layer.</p></li>
+<li><p><strong>apply_batch_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, apply batch normalization after both temporal convolutional
 layers.</p></li>
-<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
 (before the final linear layer). If False, pass the features through
 the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>input_size_s</strong> – Alias for <cite>input_window_seconds</cite>.</p></li>
+<li><p><strong>n_classes</strong> – Alias for <cite>n_outputs</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r89163c5eab6a-chambon2018"><span class="brackets">Chambon2018</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id2">2</a>,<a href="#id3">3</a>,<a href="#id4">4</a>,<a href="#id5">5</a>)</span></dt>
-<dd><p>Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &amp;
+<div role="list" class="citation-list">
+<div class="citation" id="r89163c5eab6a-chambon2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>Chambon2018<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>)</span>
+<p>Chambon, S., Galtier, M. N., Arnal, P. J., Wainrib, G., &amp;
 Gramfort, A. (2018). A deep learning architecture for temporal sleep
 stage classification using multivariate and multimodal time series.
 IEEE Transactions on Neural Systems and Rehabilitation Engineering,
 26(4), 758-769.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerChambon2018.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_chambon_2018.html#SleepStagerChambon2018.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_chambon_2018.py#L135-L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerChambon2018.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
 </dd>
 </dl>
 </dd></dl>
@@ -540,134 +656,108 @@ <h1>braindecode.models.SleepStagerChambon2018<a class="headerlink" href="#braind
 </dd></dl>
 
 <section id="examples-using-braindecode-models-sleepstagerchambon2018">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerChambon2018</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagerchambon2018" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerChambon2018</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagerchambon2018" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.models.SleepStagerChambon2018">
-   <code class="docutils literal notranslate">
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-    </span>
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-      </span>
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-  <a class="reference internal nav-link" href="#examples-using-braindecode-models-sleepstagerchambon2018">
-   Examples using
-   <code class="docutils literal notranslate">
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-     braindecode.models.SleepStagerChambon2018
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerChambon2018"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerChambon2018.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerChambon2018.forward()</span></code></a></li>
 </ul>
+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-models-sleepstagerchambon2018">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerChambon2018</span></code></a></li>
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diff --git a/stable/generated/braindecode.models.SleepStagerEldele2021.html b/stable/generated/braindecode.models.SleepStagerEldele2021.html
index c4976b21..7c96cfdf 100644
--- a/stable/generated/braindecode.models.SleepStagerEldele2021.html
+++ b/stable/generated/braindecode.models.SleepStagerEldele2021.html
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-    <title>braindecode.models.SleepStagerEldele2021 &#8212; Braindecode 0.7 documentation</title>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,38 +530,64 @@
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   <section id="braindecode-models-sleepstagereldele2021">
-<h1>braindecode.models.SleepStagerEldele2021<a class="headerlink" href="#braindecode-models-sleepstagereldele2021" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.SleepStagerEldele2021<a class="headerlink" href="#braindecode-models-sleepstagereldele2021" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerEldele2021">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerEldele2021</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sfreq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">80</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_ff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_attn_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">after_reduced_cnn_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_eldele_2021.html#SleepStagerEldele2021"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">SleepStagerEldele2021</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_tce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">80</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_ff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_attn_heads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">after_reduced_cnn_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_feats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L16-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021" title="Link to this definition">#</a></dt>
 <dd><p>Sleep Staging Architecture from Eldele et al 2021.</p>
 <p>Attention based Neural Net for sleep staging as described in <a class="reference internal" href="#r63c3502458ce-eldele2021" id="id1">[Eldele2021]</a>.
 The code for the paper and this model is also available at <a class="reference internal" href="#r63c3502458ce-1" id="id2">[1]</a>.
@@ -502,50 +600,70 @@ <h1>braindecode.models.SleepStagerEldele2021<a class="headerlink" href="#brainde
 the reference architecture from <a class="reference internal" href="#r63c3502458ce-1" id="id3">[1]</a> which was validated on SHHS dataset <a class="reference internal" href="#r63c3502458ce-2" id="id4">[2]</a> will be used)
 to use any other input is likely to make the model perform in unintended ways.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – EEG sampling frequency.</p></li>
-<li><p><strong>n_tce</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of TCE clones.</p></li>
-<li><p><strong>d_model</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Input dimension for the TCE.
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_tce</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of TCE clones.</p></li>
+<li><p><strong>d_model</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Input dimension for the TCE.
 Also the input dimension of the first FC layer in the feed forward
 and the output of the second FC layer in the same.
 Increase for higher sampling rate/signal length.
 It should be divisible by n_attn_heads</p></li>
-<li><p><strong>d_ff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Output dimension of the first FC layer in the feed forward and the
+<li><p><strong>d_ff</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output dimension of the first FC layer in the feed forward and the
 input dimension of the second FC layer in the same.</p></li>
-<li><p><strong>n_attn_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of attention heads. It should be a factor of d_model</p></li>
-<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout rate in the PositionWiseFeedforward layer and the TCE layers.</p></li>
-<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of the input, in seconds.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes.</p></li>
-<li><p><strong>after_reduced_cnn_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of output channels produced by the convolution in the AFR module.</p></li>
-<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
+<li><p><strong>n_attn_heads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of attention heads. It should be a factor of d_model</p></li>
+<li><p><strong>dropout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout rate in the PositionWiseFeedforward layer and the TCE layers.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>after_reduced_cnn_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of output channels produced by the convolution in the AFR module.</p></li>
+<li><p><strong>return_feats</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, return the features, i.e. the output of the feature extractor
 (before the final linear layer). If False, pass the features through
 the final linear layer.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_outputs</cite>.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for <cite>input_window_seconds</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r63c3502458ce-eldele2021"><span class="brackets"><a class="fn-backref" href="#id1">Eldele2021</a></span></dt>
-<dd><p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
+<div role="list" class="citation-list">
+<div class="citation" id="r63c3502458ce-eldele2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Eldele2021</a><span class="fn-bracket">]</span></span>
+<p>E. Eldele et al., “An Attention-Based Deep Learning Approach for Sleep Stage
 Classification With Single-Channel EEG,” in IEEE Transactions on Neural Systems and
 Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.</p>
-</dd>
-<dt class="label" id="r63c3502458ce-1"><span class="brackets">1</span><span class="fn-backref">(<a href="#id2">1</a>,<a href="#id3">2</a>)</span></dt>
-<dd><p><a class="github reference external" href="https://github.com/emadeldeen24/AttnSleep">emadeldeen24/AttnSleep</a></p>
-</dd>
-<dt class="label" id="r63c3502458ce-2"><span class="brackets"><a class="fn-backref" href="#id4">2</a></span></dt>
-<dd><p><a class="reference external" href="https://sleepdata.org/datasets/shhs">https://sleepdata.org/datasets/shhs</a></p>
-</dd>
-</dl>
+</div>
+<div class="citation" id="r63c3502458ce-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id2">1</a>,<a role="doc-backlink" href="#id3">2</a>)</span>
+<p><a class="github reference external" href="https://github.com/emadeldeen24/AttnSleep">emadeldeen24/AttnSleep</a></p>
+</div>
+<div class="citation" id="r63c3502458ce-2" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">2</a><span class="fn-bracket">]</span></span>
+<p><a class="reference external" href="https://sleepdata.org/datasets/shhs">https://sleepdata.org/datasets/shhs</a></p>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.SleepStagerEldele2021.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/sleep_stager_eldele_2021.html#SleepStagerEldele2021.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/sleep_stager_eldele_2021.py#L145-L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.SleepStagerEldele2021.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
 </dd>
 </dl>
 </dd></dl>
@@ -553,131 +671,105 @@ <h1>braindecode.models.SleepStagerEldele2021<a class="headerlink" href="#brainde
 </dd></dl>
 
 <section id="examples-using-braindecode-models-sleepstagereldele2021">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagereldele2021" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code><a class="headerlink" href="#examples-using-braindecode-models-sleepstagereldele2021" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
   <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerEldele2021"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.SleepStagerEldele2021.forward"><code class="docutils literal notranslate"><span class="pre">SleepStagerEldele2021.forward()</span></code></a></li>
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+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-models-sleepstagereldele2021">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code></a></li>
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,72 +530,118 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-models-tcn">
-<h1>braindecode.models.TCN<a class="headerlink" href="#braindecode-models-tcn" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.TCN<a class="headerlink" href="#braindecode-models-tcn" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.TCN">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TCN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/tcn.html#TCN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TCN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L15-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN" title="Link to this definition">#</a></dt>
 <dd><p>Temporal Convolutional Network (TCN) from Bai et al 2018.</p>
 <p>See <a class="reference internal" href="#rcfca6ae7a220-bai2018" id="id1">[Bai2018]</a> for details.</p>
 <p>Code adapted from <a class="github reference external" href="https://github.com/locuslab/TCN/blob/master/TCN/tcn.py">locuslab/TCN</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of input EEG channels</p></li>
-<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of outputs of the decoding task (for example number of classes in
-classification)</p></li>
-<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of output filters of each convolution</p></li>
-<li><p><strong>n_blocks</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – number of temporal blocks in the network</p></li>
-<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – kernel size of the convolutions</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – dropout probability</p></li>
-<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether to add a log softmax layer</p></li>
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_blocks</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of temporal blocks in the network</p></li>
+<li><p><strong>n_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – number of output filters of each convolution</p></li>
+<li><p><strong>kernel_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – kernel size of the convolutions</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – dropout probability</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Length of the input window in seconds.</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Sampling frequency of the EEG recordings.</p></li>
+<li><p><strong>n_in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for <cite>n_chans</cite>.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="rcfca6ae7a220-bai2018"><span class="brackets"><a class="fn-backref" href="#id1">Bai2018</a></span></dt>
-<dd><p>Bai, S., Kolter, J. Z., &amp; Koltun, V. (2018).
+<div role="list" class="citation-list">
+<div class="citation" id="rcfca6ae7a220-bai2018" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Bai2018</a><span class="fn-bracket">]</span></span>
+<p>Bai, S., Kolter, J. Z., &amp; Koltun, V. (2018).
 An empirical evaluation of generic convolutional and recurrent networks
 for sequence modeling.
 arXiv preprint arXiv:1803.01271.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.TCN.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/tcn.html#TCN.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tcn.py#L105-L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TCN.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
 </dd>
 </dl>
 </dd></dl>
@@ -533,113 +651,96 @@ <h1>braindecode.models.TCN<a class="headerlink" href="#braindecode-models-tcn" t
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.models.TCN">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     TCN
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
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-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.TCN"><code class="docutils literal notranslate"><span class="pre">TCN</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.TCN.forward"><code class="docutils literal notranslate"><span class="pre">TCN.forward()</span></code></a></li>
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+<form class="bd-search d-flex align-items-center"
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+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
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+    Site Navigation
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+  <ul class="bd-navbar-elements navbar-nav">
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+    <div class="navbar-header-items__end">
       
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+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
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+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        </div>
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+        <div class="navbar-item">
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+document.write(`
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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-</div>
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+`);
+</script></div>
       
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+    
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+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
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+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+  
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+
   
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-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
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+                      </a>
+                    </li>
                 
 
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
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+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="../api.html">
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+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
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+                      <a class="nav-link nav-internal" href="../whats_new.html">
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+      <div class="sidebar-header-items__end">
+        
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+<script>
+document.write(`
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
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+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
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+      Choose version  <!-- this text may get changed later by javascript -->
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-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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+</script></div>
+        
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
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   <section id="braindecode-models-tidnet">
-<h1>braindecode.models.TIDNet<a class="headerlink" href="#braindecode-models-tidnet" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.TIDNet<a class="headerlink" href="#braindecode-models-tidnet" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.TIDNet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TIDNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_growth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spat_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_span</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bottleneck</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">summary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/tidnet.html#TIDNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TIDNet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">s_growth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">24</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pooling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spat_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temp_span</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bottleneck</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">summary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L169-L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet" title="Link to this definition">#</a></dt>
 <dd><p>Thinker Invariance DenseNet model from Kostas et al 2020.</p>
 <p>See <a class="reference internal" href="#re74dd80418c9-tidnet" id="id1">[TIDNet]</a> for details.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes.</p></li>
-<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG channels.</p></li>
-<li><p><strong>input_window_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of samples.</p></li>
-<li><p><strong>s_growth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – DenseNet-style growth factor (added filters per DenseFilter)</p></li>
-<li><p><strong>t_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal filters.</p></li>
-<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Dropout probability</p></li>
-<li><p><strong>pooling</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Max temporal pooling (width and stride)</p></li>
-<li><p><strong>temp_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of temporal layers</p></li>
-<li><p><strong>spat_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of DenseFilters</p></li>
-<li><p><strong>temp_span</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Percentage of input_window_samples that defines the temporal filter length:
-temp_len = ceil(temp_span * input_window_samples)
-e.g A value of 0.05 for temp_span with 1500 input_window_samples will yield a temporal
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG channels.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs of the model. This is the number of classes
+in the case of classification.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> – Alias for n_outputs.</p></li>
+<li><p><strong>input_window_samples</strong> – Alias for n_times.</p></li>
+<li><p><strong>s_growth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – DenseNet-style growth factor (added filters per DenseFilter)</p></li>
+<li><p><strong>t_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal filters.</p></li>
+<li><p><strong>drop_prob</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Dropout probability</p></li>
+<li><p><strong>pooling</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Max temporal pooling (width and stride)</p></li>
+<li><p><strong>temp_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of temporal layers</p></li>
+<li><p><strong>spat_layers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of DenseFilters</p></li>
+<li><p><strong>temp_span</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Percentage of n_times that defines the temporal filter length:
+temp_len = ceil(temp_span * n_times)
+e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal
 filter of length 75.</p></li>
-<li><p><strong>bottleneck</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Bottleneck factor within Densefilter</p></li>
-<li><p><strong>summary</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated
-automatically (input_window_samples // pooling).</p></li>
+<li><p><strong>bottleneck</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Bottleneck factor within Densefilter</p></li>
+<li><p><strong>summary</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated
+automatically (n_times // pooling).</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
 <p>Code adapted from: <a class="github reference external" href="https://github.com/SPOClab-ca/ThinkerInvariance/">SPOClab-ca/ThinkerInvariance</a></p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="re74dd80418c9-tidnet"><span class="brackets"><a class="fn-backref" href="#id1">TIDNet</a></span></dt>
-<dd><p>Kostas, D. &amp; Rudzicz, F.
+<div role="list" class="citation-list">
+<div class="citation" id="re74dd80418c9-tidnet" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">TIDNet</a><span class="fn-bracket">]</span></span>
+<p>Kostas, D. &amp; Rudzicz, F.
 Thinker invariance: enabling deep neural networks for BCI across more
 people.
 J. Neural Eng. 17, 056008 (2020).
 doi: 10.1088/1741-2552/abb7a7.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.TIDNet.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/tidnet.html#TIDNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/tidnet.py#L267-L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TIDNet.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Batch of EEG windows of shape (batch_size, n_channels, n_times).</p>
 </dd>
 </dl>
 </dd></dl>
@@ -543,113 +657,96 @@ <h1>braindecode.models.TIDNet<a class="headerlink" href="#braindecode-models-tid
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diff --git a/stable/generated/braindecode.models.TimeDistributed.html b/stable/generated/braindecode.models.TimeDistributed.html
index f52e889c..233528ed 100644
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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 <ul class="current nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,38 +530,64 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-models-timedistributed">
-<h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-models-timedistributed" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-models-timedistributed" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.TimeDistributed">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TimeDistributed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/modules.html#TimeDistributed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">TimeDistributed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L153-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed" title="Link to this definition">#</a></dt>
 <dd><p>Apply module on multiple windows.</p>
 <p>Apply the provided module on a sequence of windows and return their
 concatenation.
@@ -497,7 +595,7 @@ <h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-m
 a sequence of consecutive windows to the label of the middle window in the
 sequence).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>module</strong> (<em>nn.Module</em>) – Module to be applied to the input windows. Must accept an input of
 shape (batch_size, n_channels, n_times).</p>
 </dd>
@@ -505,17 +603,17 @@ <h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-m
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.TimeDistributed.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/modules.html#TimeDistributed.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/modules.py#L173-L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.TimeDistributed.forward" title="Link to this definition">#</a></dt>
 <dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Sequence of windows, of shape (batch_size, seq_len, n_channels,
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Sequence of windows, of shape (batch_size, seq_len, n_channels,
 n_times).</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>Shape (batch_size, seq_len, output_size).</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)">torch.Tensor</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)">torch.Tensor</a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -523,134 +621,108 @@ <h1>braindecode.models.TimeDistributed<a class="headerlink" href="#braindecode-m
 </dd></dl>
 
 <section id="examples-using-braindecode-models-timedistributed">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.TimeDistributed</span></code><a class="headerlink" href="#examples-using-braindecode-models-timedistributed" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.TimeDistributed</span></code><a class="headerlink" href="#examples-using-braindecode-models-timedistributed" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
   <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
 </div></div><div style='clear:both'></div></section>
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diff --git a/stable/generated/braindecode.models.USleep.html b/stable/generated/braindecode.models.USleep.html
index 45bb21d5..f1ef4e04 100644
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+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-models-usleep">
-<h1>braindecode.models.USleep<a class="headerlink" href="#braindecode-models-usleep" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.USleep<a class="headerlink" href="#braindecode-models-usleep" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.models.USleep">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">USleep</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_time_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">complexity_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.67</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_skip_connection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0703125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_odd_conv_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/usleep.html#USleep"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">USleep</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sfreq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_time_filters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">complexity_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.67</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_skip_connection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_seconds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">30</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_conv_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0703125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_odd_conv_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chs_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_times</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size_s</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_log_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L108-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep" title="Link to this definition">#</a></dt>
 <dd><p>Sleep staging architecture from Perslev et al 2021.</p>
 <p>U-Net (autoencoder with skip connections) feature-extractor for sleep
 staging described in <a class="reference internal" href="#r58a5e8182f0a-1" id="id1">[1]</a>.</p>
@@ -509,173 +607,172 @@ <h1>braindecode.models.USleep<a class="headerlink" href="#braindecode-models-usl
 </dl>
 <p>Both do so at exponential rates.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of EEG or EOG channels. Set to 2 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id2">[1]</a> (1 EEG, 1 EOG).</p></li>
-<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – EEG sampling frequency. Set to 128 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id3">[1]</a>.</p></li>
-<li><p><strong>depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of conv blocks in encoding layer (number of 2x2 max pools)
+<li><p><strong>n_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of EEG or EOG channels. Set to 2 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id2">[1]</a> (1 EEG, 1 EOG).</p></li>
+<li><p><strong>sfreq</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – EEG sampling frequency. Set to 128 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id3">[1]</a>.</p></li>
+<li><p><strong>depth</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of conv blocks in encoding layer (number of 2x2 max pools)
 Note: each block halve the spatial dimensions of the features.</p></li>
-<li><p><strong>n_time_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Initial number of convolutional filters. Set to 5 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id4">[1]</a>.</p></li>
-<li><p><strong>complexity_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Multiplicative factor for number of channels at each layer of the U-Net.
+<li><p><strong>n_time_filters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Initial number of convolutional filters. Set to 5 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id4">[1]</a>.</p></li>
+<li><p><strong>complexity_factor</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Multiplicative factor for number of channels at each layer of the U-Net.
 Set to 2 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id5">[1]</a>.</p></li>
-<li><p><strong>with_skip_connection</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, use skip connections in decoder blocks.</p></li>
-<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of classes. Set to 5.</p></li>
-<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of the input, in seconds. Set to 30 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id6">[1]</a>.</p></li>
-<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Size of the temporal convolution kernel, in seconds. Set to 9 / 128 in
+<li><p><strong>with_skip_connection</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, use skip connections in decoder blocks.</p></li>
+<li><p><strong>n_outputs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of outputs/classes. Set to 5.</p></li>
+<li><p><strong>input_window_seconds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the input, in seconds. Set to 30 in <a class="reference internal" href="#r58a5e8182f0a-1" id="id6">[1]</a>.</p></li>
+<li><p><strong>time_conv_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Size of the temporal convolution kernel, in seconds. Set to 9 / 128 in
 <a class="reference internal" href="#r58a5e8182f0a-1" id="id7">[1]</a>.</p></li>
-<li><p><strong>ensure_odd_conv_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True and the size of the convolutional kernel is an even number, one
+<li><p><strong>ensure_odd_conv_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True and the size of the convolutional kernel is an even number, one
 will be added to it to ensure it is odd, so that the decoder blocks can
 work. This can ne useful when using different sampling rates from 128
 or 100 Hz.</p></li>
-<li><p><strong>apply_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, apply softmax on output (e.g. when using nn.NLLLoss). Use
-False if using nn.CrossEntropyLoss.</p></li>
+<li><p><strong>chs_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Information about each individual EEG channel. This should be filled with
+<code class="docutils literal notranslate"><span class="pre">info[&quot;chs&quot;]</span></code>. Refer to <a class="reference external" href="https://mne.tools/stable/generated/mne.Info.html#mne.Info" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Info</span></code></a> for more details.</p></li>
+<li><p><strong>n_times</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of time samples of the input window.</p></li>
+<li><p><strong>in_chans</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_chans.</p></li>
+<li><p><strong>n_classes</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Alias for n_outputs.</p></li>
+<li><p><strong>input_size_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Alias for input_window_seconds.</p></li>
+<li><p><strong>add_log_softmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to use log-softmax non-linearity as the output function.
+LogSoftmax final layer will be removed in the future.
+Please adjust your loss function accordingly (e.g. CrossEntropyLoss)!
+Check the documentation of the torch.nn loss functions:
+<a class="reference external" href="https://pytorch.org/docs/stable/nn.html#loss-functions">https://pytorch.org/docs/stable/nn.html#loss-functions</a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#ValueError" title="(in Python v3.12)"><strong>ValueError</strong></a> – If some input signal-related parameters are not specified: and can not be inferred.</p></li>
+<li><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.12)"><strong>FutureWarning</strong></a> – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.</p></li>
 </ul>
 </dd>
 </dl>
+<p class="rubric">Notes</p>
+<p>If some input signal-related parameters are not specified,
+there will be an attempt to infer them from the other parameters.</p>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r58a5e8182f0a-1"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id2">2</a>,<a href="#id3">3</a>,<a href="#id4">4</a>,<a href="#id5">5</a>,<a href="#id6">6</a>,<a href="#id7">7</a>)</span></dt>
-<dd><p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
+<div role="list" class="citation-list">
+<div class="citation" id="r58a5e8182f0a-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></span>
+<span class="backrefs">(<a role="doc-backlink" href="#id1">1</a>,<a role="doc-backlink" href="#id2">2</a>,<a role="doc-backlink" href="#id3">3</a>,<a role="doc-backlink" href="#id4">4</a>,<a role="doc-backlink" href="#id5">5</a>,<a role="doc-backlink" href="#id6">6</a>,<a role="doc-backlink" href="#id7">7</a>)</span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ, Igel C.
 U-Sleep: resilient high-frequency sleep staging. npj Digit. Med. 4, 72 (2021).
 <a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.models.USleep.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/usleep.html#USleep.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/models/usleep.py#L297-L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.USleep.forward" title="Link to this definition">#</a></dt>
 <dd><p>If input x has shape (B, S, C, T), return y_pred of shape (B, n_classes, S).
 If input x has shape (B, C, T), return y_pred of shape (B, n_classes).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> – The description is missing.</p>
+</dd>
+</dl>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-models-usleep">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code><a class="headerlink" href="#examples-using-braindecode-models-usleep" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code><a class="headerlink" href="#examples-using-braindecode-models-usleep" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
   <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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-    </span>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.USleep"><code class="docutils literal notranslate"><span class="pre">USleep</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.models.USleep.forward"><code class="docutils literal notranslate"><span class="pre">USleep.forward()</span></code></a></li>
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+</li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-models-usleep">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code></a></li>
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
+<ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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   <section id="braindecode-models-get-output-shape">
-<h1>braindecode.models.get_output_shape<a class="headerlink" href="#braindecode-models-get-output-shape" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.models.get_output_shape<a class="headerlink" href="#braindecode-models-get-output-shape" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.models.get_output_shape">
-<span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/models/util.html#get_output_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.get_output_shape" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.models.</span></span><span class="sig-name descname"><span class="pre">get_output_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_chans</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_window_samples</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/../../../../.local/lib/python3.10/site-packages/sklearn/utils/deprecation.py#L65-L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.models.get_output_shape" title="Link to this definition">#</a></dt>
 <dd><p>Returns shape of neural network output for batch size equal 1.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>output_shape</strong> – shape of the network output for <cite>batch_size==1</cite> (1, …)</p>
 </dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)">tuple</a></p>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)">tuple</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-models-get-output-shape">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.get_output_shape</span></code><a class="headerlink" href="#examples-using-braindecode-models-get-output-shape" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.models.get_output_shape</span></code><a class="headerlink" href="#examples-using-braindecode-models-get-output-shape" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
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diff --git a/stable/generated/braindecode.preprocessing.Crop.html b/stable/generated/braindecode.preprocessing.Crop.html
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+<h1>braindecode.preprocessing.Crop<a class="headerlink" href="#braindecode-preprocessing-crop" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Crop">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Crop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tmin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_tmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Crop" title="Link to this definition">#</a></dt>
+<dd><p>Crop raw data file.</p>
+<blockquote>
+<div><p>Limit the data from the raw file to go between specific times. Note
+that the new <code class="docutils literal notranslate"><span class="pre">tmin</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">t=0</span></code> for all subsequently
+called functions (e.g., <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.time_as_index" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">time_as_index()</span></code></a>, or
+<a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Epochs</span></code></a>). New <a class="reference external" href="https://mne.tools/stable/glossary.html#term-first_samp" title="(in MNE v1.5)"><span class="xref std std-term">first_samp</span></a> and <a class="reference external" href="https://mne.tools/stable/glossary.html#term-last_samp" title="(in MNE v1.5)"><span class="xref std std-term">last_samp</span></a>
+are set accordingly.</p>
+<p>Thus function operates in-place on the instance.
+Use <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.copy" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.copy()</span></code></a> if operation on a copy is desired.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>tmin</strong><span class="classifier">float</span></dt><dd><p>Start time of the raw data to use in seconds (must be &gt;= 0).</p>
+</dd>
+<dt><strong>tmax</strong><span class="classifier">float</span></dt><dd><p>End time of the raw data to use in seconds (cannot exceed data duration).</p>
+</dd>
+<dt><strong>include_tmax</strong><span class="classifier">bool</span></dt><dd><p>If True (default), include tmax. If False, exclude tmax (similar to how
+Python indexing typically works).</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>raw</strong><span class="classifier">instance of Raw</span></dt><dd><p>The cropped raw object, modified in-place.</p>
+</dd>
+<dt>See more details in mne.io.base.crop</dt><dd></dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
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diff --git a/stable/generated/braindecode.preprocessing.DropChannels.html b/stable/generated/braindecode.preprocessing.DropChannels.html
new file mode 100644
index 00000000..6084656b
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+  <section id="braindecode-preprocessing-dropchannels">
+<h1>braindecode.preprocessing.DropChannels<a class="headerlink" href="#braindecode-preprocessing-dropchannels" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.DropChannels">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">DropChannels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'raise'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.DropChannels" title="Link to this definition">#</a></dt>
+<dd><p>Drop channel(s).</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ch_names</strong><span class="classifier">iterable or str</span></dt><dd><p>Iterable (e.g. list) of channel name(s) or channel name to remove.</p>
+</dd>
+<dt><strong>on_missing</strong><span class="classifier">‘raise’ | ‘warn’ | ‘ignore’</span></dt><dd><p>Can be <code class="docutils literal notranslate"><span class="pre">'raise'</span></code> (default) to raise an error, <code class="docutils literal notranslate"><span class="pre">'warn'</span></code> to emit a
+warning, or <code class="docutils literal notranslate"><span class="pre">'ignore'</span></code> to ignore when entries in ch_names are not present in the raw instance.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.23.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">reorder_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_channels</span></code></dt><dd></dd>
+<dt><code class="xref py py-obj docutils literal notranslate"><span class="pre">pick_types</span></code></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.channels.channels.drop_channels</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="braindecode-preprocessing-filter">
+<h1>braindecode.preprocessing.Filter<a class="headerlink" href="#braindecode-preprocessing-filter" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Filter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">l_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filter_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">h_trans_bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fir'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iir_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zero'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'hamming'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fir_design</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'firwin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_by_annotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Filter" title="Link to this definition">#</a></dt>
+<dd><p>Filter a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>l_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the lower pass-band edge; for IIR filters, the lower
+cutoff frequency. If None the data are only low-passed.</p>
+</dd>
+<dt><strong>h_freq</strong><span class="classifier">float | None</span></dt><dd><p>For FIR filters, the upper pass-band edge; for IIR filters, the upper
+cutoff frequency. If None the data are only high-passed.</p>
+</dd>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all data channels. Note that channels
+in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>filter_length</strong><span class="classifier">str | int</span></dt><dd><p>Length of the FIR filter to use (if applicable):</p>
+<ul class="simple">
+<li><p><strong>‘auto’ (default)</strong>: The filter length is chosen based
+on the size of the transition regions (6.6 times the reciprocal
+of the shortest transition band for fir_window=’hamming’
+and fir_design=”firwin2”, and half that for “firwin”).</p></li>
+<li><p><strong>str</strong>: A human-readable time in
+units of “s” or “ms” (e.g., “10s” or “5500ms”) will be
+converted to that number of samples if <code class="docutils literal notranslate"><span class="pre">phase=&quot;zero&quot;</span></code>, or
+the shortest power-of-two length at least that duration for
+<code class="docutils literal notranslate"><span class="pre">phase=&quot;zero-double&quot;</span></code>.</p></li>
+<li><p><strong>int</strong>: Specified length in samples. For fir_design=”firwin”,
+this should not be used.</p></li>
+</ul>
+</dd>
+<dt><strong>l_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the low cut-off frequency in Hz
+(high pass or cutoff 1 in bandpass). Can be “auto”
+(default) to use a multiple of <code class="docutils literal notranslate"><span class="pre">l_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">l_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">l_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>h_trans_bandwidth</strong><span class="classifier">float | str</span></dt><dd><p>Width of the transition band at the high cut-off frequency in Hz
+(low pass or cutoff 2 in bandpass). Can be “auto”
+(default in 0.14) to use a multiple of <code class="docutils literal notranslate"><span class="pre">h_freq</span></code>:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">min</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">h_freq</span> <span class="o">*</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">2.</span><span class="p">),</span> <span class="n">info</span><span class="p">[</span><span class="s1">&#39;sfreq&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="mf">2.</span> <span class="o">-</span> <span class="n">h_freq</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly and method=’fir’.</p>
+</dd>
+<dt><strong>method</strong><span class="classifier">str</span></dt><dd><p>‘fir’ will use overlap-add FIR filtering, ‘iir’ will use IIR
+forward-backward filtering (via filtfilt).</p>
+</dd>
+<dt><strong>iir_params</strong><span class="classifier">dict | None</span></dt><dd><p>Dictionary of parameters to use for IIR filtering.
+If iir_params is None and method=”iir”, 4th order Butterworth will be used.
+For more information, see <a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter()</span></code></a>.</p>
+</dd>
+<dt><strong>phase</strong><span class="classifier">str</span></dt><dd><p>Phase of the filter.
+When <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>, symmetric linear-phase FIR filters are constructed,
+and if <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default), the delay of this filter is compensated
+for, making it non-causal. If <code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code>,
+then this filter is applied twice, once forward, and once backward
+(also making it non-causal). If <code class="docutils literal notranslate"><span class="pre">'minimum'</span></code>, then a minimum-phase filter
+will be constructed and applied, which is causal but has weaker stop-band
+suppression.
+When <code class="docutils literal notranslate"><span class="pre">method='iir'</span></code>, <code class="docutils literal notranslate"><span class="pre">phase='zero'</span></code> (default) or
+<code class="docutils literal notranslate"><span class="pre">phase='zero-double'</span></code> constructs and applies IIR filter twice, once
+forward, and once backward (making it non-causal) using filtfilt.
+If <code class="docutils literal notranslate"><span class="pre">phase='forward'</span></code>, it constructs and applies forward IIR filter using
+lfilter.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.13.</span></p>
+</div>
+</dd>
+<dt><strong>fir_window</strong><span class="classifier">str</span></dt><dd><p>The window to use in FIR design, can be “hamming” (default),
+“hann” (default in 0.13), or “blackman”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>fir_design</strong><span class="classifier">str</span></dt><dd><p>Can be “firwin” (default) to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin.html#scipy.signal.firwin" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin()</span></code></a>,
+or “firwin2” to use <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.firwin2.html#scipy.signal.firwin2" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.firwin2()</span></code></a>. “firwin” uses
+a time-domain design technique that generally gives improved
+attenuation using fewer samples than “firwin2”.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>skip_by_annotation</strong><span class="classifier">str | list of str</span></dt><dd><p>If a string (or list of str), any annotation segment that begins
+with the given string will not be included in filtering, and
+segments on either side of the given excluded annotated segment
+will be filtered separately (i.e., as independent signals).
+The default (<code class="docutils literal notranslate"><span class="pre">('edge',</span> <span class="pre">'bad_acq_skip')</span></code> will separately filter
+any segments that were concatenated by <a class="reference external" href="https://mne.tools/stable/generated/mne.concatenate_raws.html#mne.concatenate_raws" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.concatenate_raws()</span></code></a>
+or <a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.append" title="(in MNE v1.5)"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mne.io.Raw.append()</span></code></a>, or separated during acquisition.
+To disable, provide an empty list. Only used if <code class="docutils literal notranslate"><span class="pre">inst</span></code> is raw.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.16..</span></p>
+</div>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.</p>
+<p>Only used for <code class="docutils literal notranslate"><span class="pre">method='fir'</span></code>.</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Epochs, Evoked, or Raw</span></dt><dd><p>The filtered data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.Evoked.html#mne.Evoked.savgol_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.Evoked.savgol_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.notch_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.notch_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw.resample" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.io.Raw.resample</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.create_filter.html#mne.filter.create_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.create_filter</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.filter_data.html#mne.filter.filter_data" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.filter_data</span></code></a></dt><dd></dd>
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.filter.construct_iir_filter.html#mne.filter.construct_iir_filter" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.filter.construct_iir_filter</span></code></a></dt><dd></dd>
+</dl>
+</div>
+<p>See more details in mne.io.base.filter</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+<h1>braindecode.preprocessing.Pick<a class="headerlink" href="#braindecode-preprocessing-pick" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Pick">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Pick</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">picks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Pick" title="Link to this definition">#</a></dt>
+<dd><p>Pick a subset of channels.</p>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>picks</strong><span class="classifier">str | array-like | slice | None</span></dt><dd><p>Channels to include. Slices and lists of integers will be interpreted as
+channel indices. In lists, channel <em>type</em> strings (e.g., <code class="docutils literal notranslate"><span class="pre">['meg',</span>
+<span class="pre">'eeg']</span></code>) will pick channels of those types, channel <em>name</em> strings (e.g.,
+<code class="docutils literal notranslate"><span class="pre">['MEG0111',</span> <span class="pre">'MEG2623']</span></code> will pick the given channels. Can also be the
+string values “all” to pick all channels, or “data” to pick <a class="reference external" href="https://mne.tools/stable/glossary.html#term-data-channels" title="(in MNE v1.5)"><span class="xref std std-term">data
+channels</span></a>. None (default) will pick all channels. Note that channels in
+<code class="docutils literal notranslate"><span class="pre">info['bads']</span></code> <em>will be included</em> if their names or indices are
+explicitly provided.</p>
+</dd>
+<dt><strong>exclude</strong><span class="classifier">list | str</span></dt><dd><p>Set of channels to exclude, only used when picking based on
+types (e.g., exclude=”bads” when picks=”meg”).</p>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.24.0.</span></p>
+</div>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw, Epochs, or Evoked</span></dt><dd><p>The modified instance.</p>
+</dd>
+<dt>See more details in mne.channels.channels.pick</dt><dd></dd>
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diff --git a/stable/generated/braindecode.preprocessing.Preprocessor.html b/stable/generated/braindecode.preprocessing.Preprocessor.html
index 0ab79402..3f59ab38 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
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   <section id="braindecode-preprocessing-preprocessor">
-<h1>braindecode.preprocessing.Preprocessor<a class="headerlink" href="#braindecode-preprocessing-preprocessor" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.Preprocessor<a class="headerlink" href="#braindecode-preprocessing-preprocessor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#Preprocessor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Preprocessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_on_array</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L27-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor" title="Link to this definition">#</a></dt>
 <dd><p>Preprocessor for an MNE Raw or Epochs object.</p>
 <p>Applies the provided preprocessing function to the data of a Raw or Epochs
 object.
@@ -501,11 +599,11 @@ <h1>braindecode.preprocessing.Preprocessor<a class="headerlink" href="#braindeco
 If <cite>apply_on_array</cite> is False, the callable must directly modify the Raw or
 Epochs object (e.g., by calling its method(s) or modifying its attributes).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
+<li><p><strong>fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>callable</em>) – If str, the Raw/Epochs object must have a method with that name.
 If callable, directly apply the callable to the object.</p></li>
-<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
+<li><p><strong>apply_on_array</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Ignored if <cite>fn</cite> is not a callable. If True, the <cite>apply_function</cite> of Raw
 and Epochs object will be used to run <cite>fn</cite> on the underlying arrays
 directly. If False, <cite>fn</cite> must directly modify the Raw or Epochs object.</p></li>
 <li><p><strong>kwargs</strong> – Keyword arguments to be forwarded to the MNE function.</p></li>
@@ -515,182 +613,159 @@ <h1>braindecode.preprocessing.Preprocessor<a class="headerlink" href="#braindeco
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.preprocessing.Preprocessor.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#Preprocessor.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor.apply" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw_or_epochs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L66-L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Preprocessor.apply" title="Link to this definition">#</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-preprocessor">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.Preprocessor</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocessor" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.Preprocessor</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocessor" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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+  <section id="braindecode-preprocessing-resample">
+<h1>braindecode.preprocessing.Resample<a class="headerlink" href="#braindecode-preprocessing-resample" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.Resample">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">Resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">npad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'boxcar'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect_limited'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.Resample" title="Link to this definition">#</a></dt>
+<dd><p>Resample an array.</p>
+<blockquote>
+<div><p>Operates along the last dimension of the array.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>x</strong><span class="classifier">ndarray</span></dt><dd><p>Signal to resample.</p>
+</dd>
+<dt><strong>up</strong><span class="classifier">float</span></dt><dd><p>Factor to upsample by.</p>
+</dd>
+<dt><strong>down</strong><span class="classifier">float</span></dt><dd><p>Factor to downsample by.</p>
+</dd>
+<dt><strong>npad</strong><span class="classifier">int | str</span></dt><dd><p>Amount to pad the start and end of the data.
+Can also be “auto” to use a padding that will result in
+a power-of-two size (can be much faster).</p>
+</dd>
+<dt><strong>axis</strong><span class="classifier">int</span></dt><dd><p>Axis along which to resample (default is the last axis).</p>
+</dd>
+<dt><strong>window</strong><span class="classifier">str | tuple</span></dt><dd><p>Frequency-domain window to use in resampling.
+See <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html#scipy.signal.resample" title="(in SciPy v1.11.3)"><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.signal.resample()</span></code></a>.</p>
+</dd>
+<dt><strong>n_jobs</strong><span class="classifier">int | str</span></dt><dd><p>Number of jobs to run in parallel. Can be ‘cuda’ if <code class="docutils literal notranslate"><span class="pre">cupy</span></code>
+is installed properly.</p>
+</dd>
+<dt><strong>pad</strong><span class="classifier">str</span></dt><dd><p>The type of padding to use. Supports all <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.pad.html#numpy.pad" title="(in NumPy v1.26)"><code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.pad()</span></code></a> <code class="docutils literal notranslate"><span class="pre">mode</span></code>
+options. Can also be <code class="docutils literal notranslate"><span class="pre">&quot;reflect_limited&quot;</span></code>, which pads with a
+reflected version of each vector mirrored on the first and last values
+of the vector, followed by zeros.
+The default is <code class="docutils literal notranslate"><span class="pre">'reflect_limited'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.15.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>y</strong><span class="classifier">array</span></dt><dd><p>The x array resampled.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p>See more details in mne.filter.resample</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
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+  <section id="braindecode-preprocessing-seteegreference">
+<h1>braindecode.preprocessing.SetEEGReference<a class="headerlink" href="#braindecode-preprocessing-seteegreference" title="Link to this heading">#</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="braindecode.preprocessing.SetEEGReference">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">SetEEGReference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'average'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">projection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">joint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/mne_preprocess.py"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.SetEEGReference" title="Link to this definition">#</a></dt>
+<dd><p>Specify which reference to use for EEG data.</p>
+<blockquote>
+<div><p>Use this function to explicitly specify the desired reference for EEG.
+This can be either an existing electrode or a new virtual channel.
+This function will re-reference the data according to the desired
+reference.</p>
+</div></blockquote>
+<dl class="field-list">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl>
+<dt><strong>ref_channels</strong><span class="classifier">list of str | str</span></dt><dd><p>Can be:</p>
+<ul class="simple">
+<li><p>The name(s) of the channel(s) used to construct the reference.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'average'</span></code> to apply an average reference (default)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">'REST'</span></code> to use the Reference Electrode Standardization Technique
+infinity reference <a href="#id1"><span class="problematic" id="id2">:footcite:`Yao2001`</span></a>.</p></li>
+<li><p>An empty list, in which case MNE will not attempt any re-referencing of
+the data</p></li>
+</ul>
+</dd>
+<dt><strong>projection</strong><span class="classifier">bool</span></dt><dd><p>If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> this argument specifies if the
+average reference should be computed as a projection (True) or not
+(False; default). If <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>, the average reference is
+added as a projection and is not applied to the data (it can be
+applied afterwards with the <code class="docutils literal notranslate"><span class="pre">apply_proj</span></code> method). If
+<code class="docutils literal notranslate"><span class="pre">projection=False</span></code>, the average reference is directly applied to
+the data. If <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> is not <code class="docutils literal notranslate"><span class="pre">'average'</span></code>, <code class="docutils literal notranslate"><span class="pre">projection</span></code>
+must be set to <code class="docutils literal notranslate"><span class="pre">False</span></code> (the default in this case).</p>
+</dd>
+<dt><strong>ch_type</strong><span class="classifier">list of str | str</span></dt><dd><p>The name of the channel type to apply the reference to.
+Valid channel types are <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, <code class="docutils literal notranslate"><span class="pre">'eeg'</span></code>, <code class="docutils literal notranslate"><span class="pre">'ecog'</span></code>, <code class="docutils literal notranslate"><span class="pre">'seeg'</span></code>,
+<code class="docutils literal notranslate"><span class="pre">'dbs'</span></code>. If <code class="docutils literal notranslate"><span class="pre">'auto'</span></code>, the first channel type of eeg, ecog, seeg or dbs
+that is found (in that order) will be selected.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.19.</span></p>
+</div>
+<div class="versionchanged">
+<p><span class="versionmodified changed">Changed in version 1.2: </span><code class="docutils literal notranslate"><span class="pre">list-of-str</span></code> is now supported with <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+</div>
+</dd>
+<dt><strong>forward</strong><span class="classifier">instance of Forward | None</span></dt><dd><p>Forward solution to use. Only used with <code class="docutils literal notranslate"><span class="pre">ref_channels='REST'</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.21.</span></p>
+</div>
+</dd>
+<dt><strong>joint</strong><span class="classifier">bool</span></dt><dd><p>How to handle list-of-str <code class="docutils literal notranslate"><span class="pre">ch_type</span></code>. If False (default), one projector
+is created per channel type. If True, one projector is created across
+all channel types. This is only used when <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 1.2.</span></p>
+</div>
+</dd>
+<dt><strong>verbose</strong><span class="classifier">bool | str | int | None</span></dt><dd><p>Control verbosity of the logging output. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, use the default
+verbosity level. See the <a class="reference external" href="https://docs.python.org/3/tutorial/stdlib2.html#tut-logging" title="(in Python v3.12)"><span class="xref std std-ref">logging documentation</span></a> and
+<a class="reference external" href="https://mne.tools/stable/generated/mne.verbose.html#mne.verbose" title="(in MNE v1.5)"><code class="xref py py-func docutils literal notranslate"><span class="pre">mne.verbose()</span></code></a> for details. Should only be passed as a keyword
+argument.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>inst</strong><span class="classifier">instance of Raw | Epochs | Evoked</span></dt><dd><p>Data with EEG channels re-referenced. If <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>
+and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code> a projection will be added instead of
+directly re-referencing the data.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<div class="admonition seealso">
+<p class="admonition-title">See also</p>
+<dl class="simple">
+<dt><a class="reference external" href="https://mne.tools/stable/generated/mne.set_bipolar_reference.html#mne.set_bipolar_reference" title="(in MNE v1.5)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mne.set_bipolar_reference</span></code></a></dt><dd><p>Convenience function for creating bipolar references.</p>
+</dd>
+</dl>
+</div>
+<p class="rubric">Notes</p>
+<p>Some common referencing schemes and the corresponding value for the
+<code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> parameter:</p>
+<ul class="simple">
+<li><dl class="simple">
+<dt>Average reference:</dt><dd><p>A new virtual reference electrode is created by averaging the current
+EEG signal by setting <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code>. Bad EEG channels are
+automatically excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>A single electrode:</dt><dd><p>Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list containing the name of the channel that
+will act as the new reference, for example <code class="docutils literal notranslate"><span class="pre">ref_channels=['Cz']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>The mean of multiple electrodes:</dt><dd><p>A new virtual reference electrode is created by computing the average
+of the current EEG signal recorded from two or more selected channels.
+Set <code class="docutils literal notranslate"><span class="pre">ref_channels</span></code> to a list of channel names, indicating which
+channels to use. For example, to apply an average mastoid reference,
+when using the 10-20 naming scheme, set <code class="docutils literal notranslate"><span class="pre">ref_channels=['M1',</span> <span class="pre">'M2']</span></code>.</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>REST</dt><dd><p>The given EEG electrodes are referenced to a point at infinity using the
+lead fields in <code class="docutils literal notranslate"><span class="pre">forward</span></code>, which helps standardize the signals.</p>
+</dd>
+</dl>
+</li>
+</ul>
+<ol class="arabic simple">
+<li><p>If a reference is requested that is not the average reference, this
+function removes any pre-existing average reference projections.</p></li>
+<li><p>During source localization, the EEG signal should have an average
+reference.</p></li>
+<li><p>In order to apply a reference, the data must be preloaded. This is not
+necessary if <code class="docutils literal notranslate"><span class="pre">ref_channels='average'</span></code> and <code class="docutils literal notranslate"><span class="pre">projection=True</span></code>.</p></li>
+<li><p>For an average or REST reference, bad EEG channels are automatically
+excluded if they are properly set in <code class="docutils literal notranslate"><span class="pre">info['bads']</span></code>.</p></li>
+</ol>
+<div class="versionadded">
+<p><span class="versionmodified added">New in version 0.9.0.</span></p>
+</div>
+<p>See more details in mne.channels.channels.set_eeg_reference</p>
+</dd></dl>
+
+<div style='clear:both'></div></section>
+
+
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,213 +530,214 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-preprocessing-create-fixed-length-windows">
-<h1>braindecode.preprocessing.create_fixed_length_windows<a class="headerlink" href="#braindecode-preprocessing-create-fixed-length-windows" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.create_fixed_length_windows<a class="headerlink" href="#braindecode-preprocessing-create-fixed-length-windows" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.create_fixed_length_windows">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_fixed_length_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/windowers.html#create_fixed_length_windows"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_fixed_length_windows" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_fixed_length_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets_from</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'metadata'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L146-L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_fixed_length_windows" title="Link to this definition">#</a></dt>
 <dd><p>Windower that creates sliding windows.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>concat_ds</strong> (<em>ConcatDataset</em>) – A concat of base datasets each holding raw and description.</p></li>
-<li><p><strong>start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Start offset from beginning of recording in samples.</p></li>
-<li><p><strong>stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Stop offset from beginning of recording in samples. If None, set to be
+<li><p><strong>start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from beginning of recording in samples.</p></li>
+<li><p><strong>stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stop offset from beginning of recording in samples. If None, set to be
 the end of the recording.</p></li>
-<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Window size in samples. If None, set to be the maximum possible window size, ie length of
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size in samples. If None, set to be the maximum possible window size, ie length of
 the recording, once offsets are accounted for.</p></li>
-<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows in samples. If None, set to be equal to winddow_size_samples, so
 windows will not overlap.</p></li>
-<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> | </em><em>None</em>) – Whether or not have a last overlapping window, when windows do not
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><em>None</em>) – Whether or not have a last overlapping window, when windows do not
 equally divide the continuous signal. Must be set to a bool if window size and stride are
 not None.</p></li>
-<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to target value.</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, preload the data of the Epochs objects.</p></li>
-<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
-step allows identifying e.g., windows that fall outside of the
-continuous recording. It is suggested to run this step here as
-otherwise the BaseConcatDataset has to be updated as well.</p></li>
-<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.10)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects.</p></li>
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
 mne.Epochs.</p></li>
-<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
 rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
-<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
 rejection based on flatness is done. See mne.Epochs.</p></li>
-<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
 Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
-<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>windows_ds</strong> – Dataset containing the extracted windows.</p>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset" title="braindecode.datasets.WindowsDataset">WindowsDataset</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-create-fixed-length-windows">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-fixed-length-windows" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of multiple discrete targets per recording with the TUH EEG ..."><img alt="Multiple discrete targets with the TUH EEG Corpus" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="Benchmarking eager and lazy loading" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-fixed-length-windows" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of 1) eager loading, i.e..."><img alt="" src="../_images/sphx_glr_benchmark_lazy_eager_loading_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/benchmark_lazy_eager_loading.html#sphx-glr-auto-examples-datasets-io-benchmark-lazy-eager-loading-py"><span class="std std-ref">Benchmarking eager and lazy loading</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking eager and lazy loading</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="Welcome to this tutorial where we demonstrate how to work with multiple discrete  targets for e..."><img alt="" src="../_images/sphx_glr_plot_tuh_discrete_multitarget_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_tuh_discrete_multitarget.html#sphx-glr-auto-examples-datasets-io-plot-tuh-discrete-multitarget-py"><span class="std std-ref">Multiple discrete targets with the TUH EEG Corpus</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Multiple discrete targets with the TUH EEG Corpus</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
 </div></div><div style='clear:both'></div></section>
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-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+
 
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+          <div class="bd-content">
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   <section id="braindecode-preprocessing-create-windows-from-events">
-<h1>braindecode.preprocessing.create_windows_from_events<a class="headerlink" href="#braindecode-preprocessing-create-windows-from-events" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.create_windows_from_events<a class="headerlink" href="#braindecode-preprocessing-create-windows-from-events" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.create_windows_from_events">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/windowers.html#create_windows_from_events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_events" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_events</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_start_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial_stop_offset_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_stride_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last_window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">accepted_bads_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L29-L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_events" title="Link to this definition">#</a></dt>
 <dd><p>Create windows based on events in mne.Raw.</p>
 <p>This function extracts windows of size window_size_samples in the interval
 [trial onset + trial_start_offset_samples, trial onset + trial duration +
@@ -508,205 +606,187 @@ <h1>braindecode.preprocessing.create_windows_from_events<a class="headerlink" hr
 </pre></div>
 </div>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of base datasets each holding raw and description.</p></li>
-<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Start offset from original trial onsets, in samples. Defaults to zero.</p></li>
-<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Stop offset from original trial stop, in samples. Defaults to zero.</p></li>
-<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Window size. If None, the window size is inferred from the original
+<li><p><strong>trial_start_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Start offset from original trial onsets, in samples. Defaults to zero.</p></li>
+<li><p><strong>trial_stop_offset_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Stop offset from original trial stop, in samples. Defaults to zero.</p></li>
+<li><p><strong>window_size_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Window size. If None, the window size is inferred from the original
 trial size of the first trial and trial_start_offset_samples and
 trial_stop_offset_samples.</p></li>
-<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows, in samples. If None, the window stride is
+<li><p><strong>window_stride_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Stride between windows, in samples. If None, the window stride is
 inferred from the original trial size of the first trial and
 trial_start_offset_samples and trial_stop_offset_samples.</p></li>
-<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If False, an additional overlapping window that ends at
+<li><p><strong>drop_last_window</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If False, an additional overlapping window that ends at
 trial_stop_offset_samples will be extracted around each event when the
 last window does not end exactly at trial_stop_offset_samples.</p></li>
-<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to numerical target value.</p></li>
-<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, preload the data of the Epochs objects. This is useful to
+<li><p><strong>mapping</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em>(</em><em>str: int</em><em>)</em>) – Mapping from event description to numerical target value.</p></li>
+<li><p><strong>preload</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, preload the data of the Epochs objects. This is useful to
 reduce disk reading overhead when returning windows in a training
 scenario, however very large data might not fit into memory.</p></li>
-<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
+<li><p><strong>drop_bad_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, call <cite>.drop_bad()</cite> on the resulting mne.Epochs object. This
 step allows identifying e.g., windows that fall outside of the
 continuous recording. It is suggested to run this step here as otherwise
 the BaseConcatDataset has to be updated as well.</p></li>
-<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.10)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
+<li><p><strong>picks</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#slice" title="(in Python v3.12)"><em>slice</em></a><em> | </em><em>None</em>) – Channels to include. If None, all available channels are used. See
 mne.Epochs.</p></li>
-<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
+<li><p><strong>reject</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on peak-to-peak amplitude. If None, no
 rejection is done based on peak-to-peak amplitude. See mne.Epochs.</p></li>
-<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
+<li><p><strong>flat</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a><em> | </em><em>None</em>) – Epoch rejection parameters based on flatness of signals. If None, no
 rejection based on flatness is done. See mne.Epochs.</p></li>
-<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
+<li><p><strong>on_missing</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a>) – What to do if one or several event ids are not found in the recording.
 Valid keys are ‘error’ | ‘warning’ | ‘ignore’. See mne.Epochs.</p></li>
-<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials with inconsistent length in a raw. If
+<li><p><strong>accepted_bads_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – Acceptable proportion of trials with inconsistent length in a raw. If
 the number of trials whose length is exceeded by the window size is
 smaller than this, then only the corresponding trials are dropped, but
 the computation continues. Otherwise, an error is raised. Defaults to
 0.0 (raise an error).</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
-<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of jobs to use to parallelize the windowing.</p></li>
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Control verbosity of the logging output when calling mne.Epochs.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>windows_ds</strong> – Dataset containing the extracted windows.</p>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>windows_datasets</strong> – Concatenated datasets of WindowsDataset containing the extracted windows.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.WindowsDataset.html#braindecode.datasets.WindowsDataset" title="braindecode.datasets.WindowsDataset">WindowsDataset</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-create-windows-from-events">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-events" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show multiple ways of how to split datasets."><img alt="Split Dataset Example" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_split_dataset.html#sphx-glr-auto-examples-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-events" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
   <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we aim to show multiple ways of how you can split your datasets for training, ..."><img alt="" src="../_images/sphx_glr_plot_split_dataset_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_split_dataset.html#sphx-glr-auto-examples-datasets-io-plot-split-dataset-py"><span class="std std-ref">Split Dataset Example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Split Dataset Example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
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diff --git a/stable/generated/braindecode.preprocessing.create_windows_from_target_channels.html b/stable/generated/braindecode.preprocessing.create_windows_from_target_channels.html
index 5dcdb203..05463a08 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,155 +530,163 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
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   <section id="braindecode-preprocessing-create-windows-from-target-channels">
-<h1>braindecode.preprocessing.create_windows_from_target_channels<a class="headerlink" href="#braindecode-preprocessing-create-windows-from-target-channels" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.create_windows_from_target_channels<a class="headerlink" href="#braindecode-preprocessing-create-windows-from-target-channels" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.create_windows_from_target_channels">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_target_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_bad_windows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/windowers.html#create_windows_from_target_channels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_target_channels" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">create_windows_from_target_channels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">picks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reject</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_target_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/windowers.py#L426-L434"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.create_windows_from_target_channels" title="Link to this definition">#</a></dt>
 <dd></dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-create-windows-from-target-channels">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-target-channels" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-create-windows-from-target-channels" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples-using-braindecode-preprocessing-create-windows-from-target-channels">Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels</span></code></a></li>
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+<form class="bd-search d-flex align-items-center"
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+         placeholder="Search the docs ..."
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-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -403,10 +470,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -459,55 +531,81 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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+
 
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+          <div class="bd-content">
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+<div>
+  
   <section id="braindecode-preprocessing-exponential-moving-demean">
-<h1>braindecode.preprocessing.exponential_moving_demean<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-demean" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.exponential_moving_demean<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-demean" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_demean">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_demean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#exponential_moving_demean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_demean" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_demean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L314-L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_demean" title="Link to this definition">#</a></dt>
 <dd><p>Perform exponential moving demeanining.</p>
 <p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
 <span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
 <p>Deman the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
 <span class="math notranslate nohighlight">\(x'_t=(x_t - m_t)\)</span>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
-<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – </p></li>
-<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Demean data before to this index with regular demeaning.</p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Demean data before to this index with regular demeaning.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>demeaned</strong> – Demeaned data.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
 </dd>
 </dl>
@@ -516,102 +614,93 @@ <h1>braindecode.preprocessing.exponential_moving_demean<a class="headerlink" hre
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -403,10 +470,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -459,38 +531,64 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-preprocessing-exponential-moving-standardize">
-<h1>braindecode.preprocessing.exponential_moving_standardize<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-standardize" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.exponential_moving_standardize<a class="headerlink" href="#braindecode-preprocessing-exponential-moving-standardize" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.exponential_moving_standardize">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#exponential_moving_standardize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_standardize" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">exponential_moving_standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor_new</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_block_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L264-L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.exponential_moving_standardize" title="Link to this definition">#</a></dt>
 <dd><p>Perform exponential moving standardization.</p>
 <p>Compute the exponental moving mean <span class="math notranslate nohighlight">\(m_t\)</span> at time <cite>t</cite> as
 <span class="math notranslate nohighlight">\(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\)</span>.</p>
@@ -499,159 +597,144 @@ <h1>braindecode.preprocessing.exponential_moving_standardize<a class="headerlink
 <p>Finally, standardize the data point <span class="math notranslate nohighlight">\(x_t\)</span> at time <cite>t</cite> as:
 <span class="math notranslate nohighlight">\(x'_t=(x_t - m_t) / max(\sqrt{-&gt;v_t}, eps)\)</span>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>data</strong> (<em>np.ndarray</em><em> (</em><em>n_channels</em><em>, </em><em>n_times</em><em>)</em>) – </p></li>
-<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – </p></li>
-<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Standardize data before to this index with regular standardization.</p></li>
-<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a>) – Stabilizer for division by zero variance.</p></li>
+<li><p><strong>factor_new</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – </p></li>
+<li><p><strong>init_block_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Standardize data before to this index with regular standardization.</p></li>
+<li><p><strong>eps</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a>) – Stabilizer for division by zero variance.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>standardized</strong> – Standardized data.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>np.ndarray (n_channels, n_times)</p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-exponential-moving-standardize">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.exponential_moving_standardize</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-exponential-moving-standardize" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.exponential_moving_standardize</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-exponential-moving-standardize" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
   <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
 </div></div><div style='clear:both'></div></section>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 </ul>
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 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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   <section id="braindecode-preprocessing-filterbank">
-<h1>braindecode.preprocessing.filterbank<a class="headerlink" href="#braindecode-preprocessing-filterbank" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.filterbank<a class="headerlink" href="#braindecode-preprocessing-filterbank" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.filterbank">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">filterbank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_bands</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_original_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by_frequency_band</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">mne_filter_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#filterbank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.filterbank" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">filterbank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">raw</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_bands</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_original_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by_frequency_band</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">mne_filter_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L349-L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.filterbank" title="Link to this definition">#</a></dt>
 <dd><p>Applies multiple bandpass filters to the signals in raw. The raw will be
 modified in-place and number of channels in raw will be updated to
 len(frequency_bands) * len(raw.ch_names) (-len(raw.ch_names) if
 drop_original_signals).</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.2)"><em>mne.io.Raw</em></a>) – The raw signals to be filtered.</p></li>
-<li><p><strong>frequency_bands</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>)</em>) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</p></li>
-<li><p><strong>drop_original_signals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to drop the original unfiltered signals</p></li>
-<li><p><strong>order_by_frequency_band</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True will return channels odered by frequency bands, so if there
+<li><p><strong>raw</strong> (<a class="reference external" href="https://mne.tools/stable/generated/mne.io.Raw.html#mne.io.Raw" title="(in MNE v1.5)"><em>mne.io.Raw</em></a>) – The raw signals to be filtered.</p></li>
+<li><p><strong>frequency_bands</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>)</em>) – The frequency bands to be filtered for (e.g. [(4, 8), (8, 13)]).</p></li>
+<li><p><strong>drop_original_signals</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to drop the original unfiltered signals</p></li>
+<li><p><strong>order_by_frequency_band</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True will return channels ordered by frequency bands, so if there
 are channels Cz, O1 and filterbank ranges [(4,8), (8,13)], returned
 channels will be [Cz_4-8, O1_4-8, Cz_8-13, O1_8-13]. If False, order
 will be [Cz_4-8, Cz_8-13, O1_4-8, O1_8-13].</p></li>
-<li><p><strong>mne_filter_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.10)"><em>dict</em></a>) – Keyword arguments for filtering supported by mne.io.Raw.filter().
+<li><p><strong>mne_filter_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Keyword arguments for filtering supported by mne.io.Raw.filter().
 Please refer to mne for a detailed explanation.</p></li>
 </ul>
 </dd>
@@ -514,102 +612,93 @@ <h1>braindecode.preprocessing.filterbank<a class="headerlink" href="#braindecode
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-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
-  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
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+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
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+          id="__secondary"/>
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+  
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+<form class="bd-search d-flex align-items-center"
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+      method="get">
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+         name="q"
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+         placeholder="Search the docs ..."
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+        <div class="navbar-item"><nav class="navbar-nav">
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+     role="heading"
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+    Site Navigation
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+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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-</div>
-      </div>
+  </div>
+`);
+</script></div>
       
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+    
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-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
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+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
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+  
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-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
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+                      </a>
+                    </li>
                 
 
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-                        Get Started
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
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+                    <li class="nav-item current active">
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+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
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+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
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+</nav></div>
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
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+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
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+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
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+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
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-
   
-  <div class="sidebar-start-items sidebar-primary__section">
-    <div class="sidebar-start-items__item"><nav class="bd-links" id="bd-docs-nav" aria-label="Section navigation">
-  <p class="bd-links__title" role="heading" aria-level="1">
-    Section Navigation
-  </p>
-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,223 +530,235 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
 </ul>
-
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-</nav>
+</div>
+</nav></div>
     </div>
-  </div>
   
-
   
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
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+  <div class="sidebar-primary-items__end sidebar-primary__section">
   </div>
+  
+  <div id="rtd-footer-container"></div>
+
 
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-      <main class="bd-main">
+      
+      <main id="main-content" class="bd-main">
         
         
-        <div class="bd-content">
-          <div class="bd-article-container">
-            
-            <div class="bd-header-article">
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   <section id="braindecode-preprocessing-preprocess">
-<h1>braindecode.preprocessing.preprocess<a class="headerlink" href="#braindecode-preprocessing-preprocess" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.preprocessing.preprocess<a class="headerlink" href="#braindecode-preprocessing-preprocess" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.preprocessing.preprocess">
-<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">preprocess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/preprocessing/preprocess.html#preprocess"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.preprocessing.</span></span><span class="sig-name descname"><span class="pre">preprocess</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concat_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/preprocessing/preprocess.py#L88-L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.preprocessing.preprocess" title="Link to this definition">#</a></dt>
 <dd><p>Apply preprocessors to a concat dataset.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>concat_ds</strong> (<a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>BaseConcatDataset</em></a>) – A concat of BaseDataset or WindowsDataset datasets to be preprocessed.</p></li>
-<li><p><strong>preprocessors</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em>(</em><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><em>Preprocessor</em></a><em>)</em>) – List of Preprocessor objects to apply to the dataset.</p></li>
-<li><p><strong>save_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> | </em><em>None</em>) – If a string, the preprocessed data will be saved under the specified
+<li><p><strong>preprocessors</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em>(</em><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><em>Preprocessor</em></a><em>)</em>) – List of Preprocessor objects to apply to the dataset.</p></li>
+<li><p><strong>save_dir</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> | </em><em>None</em>) – If a string, the preprocessed data will be saved under the specified
 directory and the datasets in <code class="docutils literal notranslate"><span class="pre">concat_ds</span></code> will be reloaded with
 <cite>preload=False</cite>.</p></li>
-<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – When <cite>save_dir</cite> is provided, controls whether to delete the old
+<li><p><strong>overwrite</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – When <cite>save_dir</cite> is provided, controls whether to delete the old
 subdirectories that will be written to under <cite>save_dir</cite>. If False and
 the corresponding subdirectories already exist, a <code class="docutils literal notranslate"><span class="pre">FileExistsError</span></code>
 will be raised.</p></li>
-<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Number of jobs for parallel execution.</p></li>
+<li><p><strong>n_jobs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Number of jobs for parallel execution. See <cite>joblib.Parallel</cite> for
+a more detailed explanation.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p>Preprocessed dataset.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p><a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset">BaseConcatDataset</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <section id="examples-using-braindecode-preprocessing-preprocess">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocess" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="Load and save dataset example" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_load_save_datasets.html#sphx-glr-auto-examples-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="Benchmarking preprocessing with parallelization and serialization" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess</span></code><a class="headerlink" href="#examples-using-braindecode-preprocessing-preprocess" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we compare the execution time and memory requirements of preprocessing data wi..."><img alt="" src="../_images/sphx_glr_plot_benchmark_preprocessing_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_benchmark_preprocessing.html#sphx-glr-auto-examples-datasets-io-plot-benchmark-preprocessing-py"><span class="std std-ref">Benchmarking preprocessing with parallelization and serialization</span></a></p>
   <div class="sphx-glr-thumbnail-title">Benchmarking preprocessing with parallelization and serialization</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="MOABB Dataset Example" src="../_images/sphx_glr_plot_dataset_example_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_dataset_example.html#sphx-glr-auto-examples-plot-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to load and save braindecode datasets."><img alt="" src="../_images/sphx_glr_plot_load_save_datasets_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_load_save_datasets.html#sphx-glr-auto-examples-datasets-io-plot-load-save-datasets-py"><span class="std std-ref">Load and save dataset example</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Load and save dataset example</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we show how to fetch and prepare a MOABB dataset for usage with Braindecode."><img alt="" src="../_images/sphx_glr_plot_moabb_dataset_example_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/datasets_io/plot_moabb_dataset_example.html#sphx-glr-auto-examples-datasets-io-plot-moabb-dataset-example-py"><span class="std std-ref">MOABB Dataset Example</span></a></p>
   <div class="sphx-glr-thumbnail-title">MOABB Dataset Example</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="Process a big data EEG resource (TUH EEG Corpus)" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model</span></a></p>
-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we showcase usage of the Temple University Hospital EEG Corpus (https://www.is..."><img alt="" src="../_images/sphx_glr_plot_tuh_eeg_corpus_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_tuh_eeg_corpus.html#sphx-glr-auto-examples-applied-examples-plot-tuh-eeg-corpus-py"><span class="std std-ref">Process a big data EEG resource (TUH EEG Corpus)</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Process a big data EEG resource (TUH EEG Corpus)</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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 <ul class="current nav bd-sidenav">
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">braindecode.regressor.EEGRegressor</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,60 +530,86 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-regressor-eegregressor">
-<h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-regressor-eegregressor" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-regressor-eegregressor" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.regressor.</span></span><span class="sig-name descname"><span class="pre">EEGRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.regressor.</span></span><span class="sig-name descname"><span class="pre">EEGRegressor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cropped</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterator_train__drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L19-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor" title="Link to this definition">#</a></dt>
 <dd><p>Regressor that calls loss function directly.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>module</strong> (<em>torch module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
+<li><p><strong>module</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>torch Module</em><em> (</em><em>class</em><em> or </em><em>instance</em><em>)</em>) – A PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></a>. In general, the
 uninstantiated class should be passed, although instantiated
 modules will also work.</p></li>
 <li><p><strong>criterion</strong> (<em>torch criterion</em><em> (</em><em>class</em><em>, </em><em>default=torch.nn.MSELoss</em><em>)</em>) – Mean squared error loss.</p></li>
 <li><p><strong>optimizer</strong> (<em>torch optim</em><em> (</em><em>class</em><em>, </em><em>default=torch.optim.SGD</em><em>)</em>) – The uninitialized optimizer (update rule) used to optimize the
 module</p></li>
-<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
+<li><p><strong>lr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em> (</em><em>default=0.01</em><em>)</em>) – Learning rate passed to the optimizer. You may use <code class="docutils literal notranslate"><span class="pre">lr</span></code> instead
 of using <code class="docutils literal notranslate"><span class="pre">optimizer__lr</span></code>, which would result in the same outcome.</p></li>
-<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
+<li><p><strong>max_epochs</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=10</em><em>)</em>) – The number of epochs to train for each <code class="docutils literal notranslate"><span class="pre">fit</span></code> call. Note that you
 may keyboard-interrupt training at any time.</p></li>
-<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=128</em><em>)</em>) – Mini-batch size. Use this instead of setting
 <code class="docutils literal notranslate"><span class="pre">iterator_train__batch_size</span></code> and <code class="docutils literal notranslate"><span class="pre">iterator_test__batch_size</span></code>,
 which would result in the same outcome. If <code class="docutils literal notranslate"><span class="pre">batch_size</span></code> is -1,
 a single batch with all the data will be used during training
 and validation.</p></li>
-<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+<li><p><strong>iterator_train</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
 training data.</p></li>
-<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
+<li><p><strong>iterator_valid</strong> (<em>torch DataLoader</em>) – The default PyTorch <a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> used for
 validation and test data, i.e. during inference.</p></li>
 <li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – The dataset is necessary for the incoming data to work with
 pytorch’s <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>. It has to implement the <code class="docutils literal notranslate"><span class="pre">__len__</span></code> and
@@ -522,14 +620,14 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 X and y by prefixing them with <code class="docutils literal notranslate"><span class="pre">dataset__</span></code>. It is also possible
 to pass an initialzed <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>, in which case no additional
 arguments may be passed.</p></li>
-<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If None, there is no train/validation split. Else, train_split
+<li><p><strong>train_split</strong> (<em>None</em><em> or </em><em>callable</em><em> (</em><em>default=skorch.dataset.ValidSplit</em><em>(</em><em>5</em><em>)</em><em>)</em>) – <p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, there is no train/validation split. Else, <code class="docutils literal notranslate"><span class="pre">train_split</span></code>
 should be a function or callable that is called with X and y
 data and should return the tuple <code class="docutils literal notranslate"><span class="pre">dataset_train,</span> <span class="pre">dataset_valid</span></code>.
-The validation data may be None.</p>
+The validation data may be <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks=None</span></code>, only use default callbacks,
 those returned by <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks=&quot;disable&quot;</span></code>, disable all callbacks, i.e. do not run
-any of the callbacks.</p>
+any of the callbacks, not even the default callbacks.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">callbacks</span></code> is a list of callbacks, use those callbacks in
 addition to the default callbacks. Each callback should be an
 instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">Callback</span></code>.</p>
@@ -545,8 +643,8 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 </p></li>
 <li><p><strong>predict_nonlinearity</strong> (<em>callable</em><em>, </em><em>None</em><em>, or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – <p>The nonlinearity to be applied to the prediction. When set to
 ‘auto’, infers the correct nonlinearity based on the criterion
-(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
-<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v1.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
+(softmax for <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> and sigmoid for
+<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="(in PyTorch v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a>). If it cannot be inferred
 or if the parameter is None, just use the identity
 function. Don’t pass a lambda function if you want the net to be
 pickleable.</p>
@@ -566,24 +664,48 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 not anywhere else – notably, the loss is unaffected by this
 nonlinearity.</p>
 </p></li>
-<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
+<li><p><strong>warm_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether each fit call should lead to a re-initialization of the
 module (cold start) or whether the module should be trained
 further (warm start).</p></li>
-<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
+<li><p><strong>verbose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> (</em><em>default=1</em><em>)</em>) – This parameter controls how much print output is generated by
 the net and its callbacks. By setting this value to 0, e.g. the
 summary scores at the end of each epoch are no longer printed.
 This can be useful when running a hyperparameter search. The
 summary scores are always logged in the history attribute,
 regardless of the verbose setting.</p></li>
-<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v1.12)"><em>torch.device</em></a><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’, data in torch
-tensors will be pushed to cuda tensors before being sent to the
-module. If set to None, then all compute devices will be left
-unmodified.</p></li>
-<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
+<li><p><strong>device</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><a class="reference external" href="https://pytorch.org/docs/stable/tensor_attributes.html#torch.device" title="(in PyTorch v2.1)"><em>torch.device</em></a><em>, or </em><em>None</em><em> (</em><em>default='cpu'</em><em>)</em>) – The compute device to be used. If set to ‘cuda’ in order to use
+GPU acceleration, data in torch tensors will be pushed to cuda
+tensors before being sent to the module. If set to None, then
+all compute devices will be left unmodified.</p></li>
+<li><p><strong>compile</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, compile all modules using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>. For this
+to work, the installed torch version has to support <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code>.
+Compiled modules should work identically to non-compiled modules but
+should run faster on new GPU architectures (Volta and Ampere for
+instance).
+Additional arguments for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> can be passed using the dunder
+notation, e.g. when initializing the net with <code class="docutils literal notranslate"><span class="pre">compile__dynamic=True</span></code>,
+<code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> will be called with <code class="docutils literal notranslate"><span class="pre">dynamic=True</span></code>.</p></li>
+<li><p><strong>use_caching</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> or </em><em>'auto'</em><em> (</em><em>default='auto'</em><em>)</em>) – Optionally override the caching behavior of scoring callbacks. Callbacks
+such as <code class="xref py py-class docutils literal notranslate"><span class="pre">EpochScoring</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchScoring</span></code> allow to cache
+the inference call to save time when calculating scores during training at
+the expense of memory. In certain situations, e.g. when memory is tight,
+you may want to disable caching. As it is cumbersome to change the setting
+on each callback individually, this parameter allows to override their
+behavior globally.
+By default (<code class="docutils literal notranslate"><span class="pre">'auto'</span></code>), the callbacks will determine if caching is used
+or not. If this argument is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, caching will be disabled on
+all callbacks. If set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, caching will be enabled on all
+callbacks.
+Implementation note: It is the job of the callbacks to honor this setting.</p></li>
+<li><p><strong>module</strong> – Either the name of one of the braindecode models (see
+<code class="xref py py-obj docutils literal notranslate"><span class="pre">braindecode.models.util.models_dict</span></code>) or directly a PyTorch module.
+When passing directly a torch module, uninstantiated class should be preferred,
+although instantiated modules will also work.</p></li>
+<li><p><strong>cropped</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Defines whether torch model passed to this class is cropped or not.
 Currently used for callbacks definition.</p></li>
-<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
+<li><p><strong>callbacks</strong> (<em>None</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>strings</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>Callback instances</em><em> (</em><em>default=None</em><em>)</em>) – More callbacks, in addition to those returned by
 <code class="docutils literal notranslate"><span class="pre">get_default_callbacks</span></code>. Each callback should inherit from
-<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.12)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
+<a class="reference external" href="https://skorch.readthedocs.io/en/stable/callbacks.html#skorch.callbacks.Callback" title="(in skorch v0.15)"><code class="xref py py-class docutils literal notranslate"><span class="pre">skorch.callbacks.Callback</span></code></a>. If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, callbacks can be a
 list of strings specifying <cite>sklearn</cite> scoring functions (for scoring
 functions names see: <a class="reference external" href="https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter">https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter</a>)
 or a list of callbacks where the callback names are inferred from the
@@ -595,58 +717,59 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 callbacks (e.g., for the callback with name <code class="docutils literal notranslate"><span class="pre">'print_log'</span></code>, use
 <code class="docutils literal notranslate"><span class="pre">net.set_params(callbacks__print_log__keys_ignored=['epoch',</span>
 <span class="pre">'train_loss'])</span></code>).</p></li>
-<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
+<li><p><strong>iterator_train__shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Defines whether train dataset will be shuffled. As skorch does not
 shuffle the train dataset by default this one overwrites this option.</p></li>
-<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
+<li><p><strong>aggregate_predictions</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether to average cropped predictions to obtain window predictions. Used only in the
 cropped mode.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.prefixes_">
-<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.prefixes_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">prefixes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.prefixes_" title="Link to this definition">#</a></dt>
 <dd><p>Contains the prefixes to special parameters. E.g., since there
-is the <code class="docutils literal notranslate"><span class="pre">'module'</span></code> prefix, it is possible to set parameters like
-so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>.</p>
+is the <code class="docutils literal notranslate"><span class="pre">'optimizer'</span></code> prefix, it is possible to set parameters like
+so: <code class="docutils literal notranslate"><span class="pre">NeuralNet(...,</span> <span class="pre">optimizer__momentum=0.95)</span></code>. Some prefixes are
+populated dynamically, based on what modules and criteria are defined.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">
-<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">cuda_dependent_attributes_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_" title="Link to this definition">#</a></dt>
 <dd><p>Contains a list of all attribute prefixes whose values depend on a
 CUDA device. If a <code class="docutils literal notranslate"><span class="pre">NeuralNet</span></code> trained with a CUDA-enabled device
 is unpickled on a machine without CUDA or with CUDA disabled, the
 listed attributes are mapped to CPU.  Expand this list if you
 want to add other cuda-dependent attributes.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.initialized_">
-<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.initialized_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">initialized_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.initialized_" title="Link to this definition">#</a></dt>
 <dd><p>Whether the <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuralNet</span></code> was initialized.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)">bool</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.module_">
-<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.module_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">module_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.module_" title="Link to this definition">#</a></dt>
 <dd><p>The instantiated module.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>torch module (instance)</p>
 </dd>
 </dl>
@@ -654,10 +777,10 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.criterion_">
-<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.criterion_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">criterion_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.criterion_" title="Link to this definition">#</a></dt>
 <dd><p>The instantiated criterion.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>torch criterion (instance)</p>
 </dd>
 </dl>
@@ -665,51 +788,51 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.callbacks_">
-<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.callbacks_" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">callbacks_</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor.callbacks_" title="Link to this definition">#</a></dt>
 <dd><p>The complete (i.e. default and other), initialized callbacks, in
 a tuple with unique names.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of tuples</p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of tuples</p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._modules">
-<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._modules" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_modules</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._modules" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all modules that are torch modules. This list is
 collected dynamically when the net is initialized. Typically, there is no
 reason for a user to modify this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._criteria">
-<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._criteria" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_criteria</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._criteria" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all criteria that are torch modules. This list is
 collected dynamically when the net is initialized. Typically, there is no
 reason for a user to modify this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor._optimizers">
-<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._optimizers" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">_optimizers</span></span><a class="headerlink" href="#braindecode.regressor.EEGRegressor._optimizers" title="Link to this definition">#</a></dt>
 <dd><p>List of names of all optimizers. This list is collected dynamically when
 the net is initialized. Typically, there is no reason for a user to modify
 this list.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)">str</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -717,52 +840,32 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.fit">
-<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.fit" title="Permalink to this definition">#</a></dt>
-<dd><p>See <code class="docutils literal notranslate"><span class="pre">NeuralNet.fit</span></code>.</p>
-<p>In contrast to <code class="docutils literal notranslate"><span class="pre">NeuralNet.fit</span></code>, <code class="docutils literal notranslate"><span class="pre">y</span></code> is non-optional to
-avoid mistakenly forgetting about <code class="docutils literal notranslate"><span class="pre">y</span></code>. However, <code class="docutils literal notranslate"><span class="pre">y</span></code> can be
-set to <code class="docutils literal notranslate"><span class="pre">None</span></code> in case it is derived dynamically from
-<code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.get_iterator">
-<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.get_iterator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.get_iterator" title="Permalink to this definition">#</a></dt>
-<dd><p>Get an iterator that allows to loop over the batches of the
-given data.</p>
-<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
-<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
-<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
-data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
-<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L171-L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.fit" title="Link to this definition">#</a></dt>
+<dd><p>Initialize and fit the module.</p>
+<p>If the module was already initialized, by calling fit, the
+module will be re-initialized (unless <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> is True).
+If possible, signal-related parameters are inferred from the
+data and passed to the module at initialisation.
+Depending on the type of input passed, the following parameters
+are inferred:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>mne.Epochs: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code>, <code class="docutils literal notranslate"><span class="pre">chs_info</span></code>,
+<code class="docutils literal notranslate"><span class="pre">sfreq</span></code>, <code class="docutils literal notranslate"><span class="pre">input_window_seconds</span></code></p></li>
+<li><p>numpy array: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>WindowsDataset with <code class="docutils literal notranslate"><span class="pre">targets_from='metadata'</span></code>
+(or BaseConcatDataset of such datasets): <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code>, <code class="docutils literal notranslate"><span class="pre">n_outputs</span></code></p></li>
+<li><p>other Dataset: <code class="docutils literal notranslate"><span class="pre">n_times</span></code>, <code class="docutils literal notranslate"><span class="pre">n_chans</span></code></p></li>
+<li><p>other types: no parameters are inferred.</p></li>
 </ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>An instantiated iterator that allows to loop over the
-mini-batches.</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>iterator</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.get_loss">
-<span class="sig-name descname"><span class="pre">get_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.get_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.get_loss" title="Permalink to this definition">#</a></dt>
-<dd><p>Return the loss for this batch by calling NeuralNet get_loss.</p>
+</div></blockquote>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>y_pred</strong> (<em>torch tensor</em>) – Predicted target values</p></li>
-<li><p><strong>y_true</strong> (<em>torch tensor</em>) – True target values.</p></li>
 <li><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
 <blockquote>
 <div><ul>
+<li><p>mne.Epochs</p></li>
 <li><p>numpy arrays</p></li>
 <li><p>torch tensors</p></li>
 <li><p>pandas DataFrame or Series</p></li>
@@ -775,26 +878,45 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 <p>If this doesn’t work with your data, you have to pass a
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> that can deal with the data.</p>
 </p></li>
-<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether train mode should be used or not.</p></li>
+<li><p><strong>y</strong> (<em>target data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – The same data types as for <code class="docutils literal notranslate"><span class="pre">X</span></code> are supported. If your X is
+a Dataset that contains the target, <code class="docutils literal notranslate"><span class="pre">y</span></code> may be set to
+None.</p></li>
+<li><p><strong>**fit_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><em>dict</em></a>) – Additional parameters passed to the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method of
+the module and to the <code class="docutils literal notranslate"><span class="pre">self.train_split</span></code> call.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)">float</a></p>
-</dd>
 </dl>
 </dd></dl>
 
 <dl class="py method">
-<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.on_batch_end">
-<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.on_batch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.on_batch_end" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
+<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.get_iterator">
+<span class="sig-name descname"><span class="pre">get_iterator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L75-L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.get_iterator" title="Link to this definition">#</a></dt>
+<dd><p>Get an iterator that allows to loop over the batches of the
+given data.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">self.iterator_train__batch_size</span></code> and/or
+<code class="docutils literal notranslate"><span class="pre">self.iterator_test__batch_size</span></code> are not set, use
+<code class="docutils literal notranslate"><span class="pre">self.batch_size</span></code> instead.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataset</strong> (<em>torch Dataset</em><em> (</em><em>default=skorch.dataset.Dataset</em><em>)</em>) – Usually, <code class="docutils literal notranslate"><span class="pre">self.dataset</span></code>, initialized with the corresponding
+data, is passed to <code class="docutils literal notranslate"><span class="pre">get_iterator</span></code>.</p></li>
+<li><p><strong>training</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=False</em><em>)</em>) – Whether to use <code class="docutils literal notranslate"><span class="pre">iterator_train</span></code> or <code class="docutils literal notranslate"><span class="pre">iterator_test</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instantiated iterator that allows to loop over the
+mini-batches.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>iterator</p>
+</dd>
+</dl>
+</dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_proba">
-<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_proba" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L82-L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_proba" title="Link to this definition">#</a></dt>
 <dd><p>Return the output of the module’s forward method as a numpy
 array. In case of cropped decoding returns averaged values for
 each trial.</p>
@@ -805,7 +927,7 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 is needed, consider using <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code>
 instead.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>X</strong> (<em>input data</em><em>, </em><em>compatible with skorch.dataset.Dataset</em>) – <p>By default, you should be able to pass:</p>
 <blockquote>
 <div><ul class="simple">
@@ -830,10 +952,10 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 <cite>predict</cite> method instead of <cite>predict_proba</cite>.</p>
 </div>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
 <dd class="field-odd"><p><strong>y_proba</strong></p>
 </dd>
-<dt class="field-even">Return type</dt>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
 <dd class="field-even"><p>numpy ndarray</p>
 </dd>
 </dl>
@@ -841,17 +963,17 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_trials">
-<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.predict_trials"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_trials" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/regressor.py#L132-L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_trials" title="Link to this definition">#</a></dt>
 <dd><p>Create trialwise predictions and optionally also return trialwise
 labels from cropped dataset.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>X</strong> (<a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
-<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
 the number of predictions depend on the chosen window size and the
@@ -865,258 +987,113 @@ <h1>braindecode.regressor.EEGRegressor<a class="headerlink" href="#braindecode-r
 </dl>
 </dd></dl>
 
-<dl class="py method">
-<dt class="sig sig-object py" id="braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys">
-<span class="sig-name descname"><span class="pre">predict_with_window_inds_and_ys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/regressor.html#EEGRegressor.predict_with_window_inds_and_ys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys" title="Permalink to this definition">#</a></dt>
-<dd></dd></dl>
-
 </dd></dl>
 
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     <ul class="visible nav section-nav flex-column">
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     EEGRegressor
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.prefixes_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.prefixes_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.cuda_dependent_attributes_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.initialized_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.initialized_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.module_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.module_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.criterion_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.criterion_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.callbacks_">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.callbacks_
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._modules">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor._modules
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._criteria">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor._criteria
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._optimizers">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor._optimizers
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.fit">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.fit()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.get_iterator">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.get_iterator()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.get_loss">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.get_loss()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.on_batch_end">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.on_batch_end()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_proba">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.predict_proba()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_trials">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.predict_trials()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       EEGRegressor.predict_with_window_inds_and_ys()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor"><code class="docutils literal notranslate"><span class="pre">EEGRegressor</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.prefixes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.prefixes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.cuda_dependent_attributes_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.cuda_dependent_attributes_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.initialized_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.initialized_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.module_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.module_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.criterion_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.criterion_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.callbacks_"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.callbacks_</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._modules"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._modules</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._criteria"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._criteria</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor._optimizers"><code class="docutils literal notranslate"><span class="pre">EEGRegressor._optimizers</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.fit"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.fit()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.get_iterator"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.get_iterator()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_proba"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_proba()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.regressor.EEGRegressor.predict_trials"><code class="docutils literal notranslate"><span class="pre">EEGRegressor.predict_trials()</span></code></a></li>
 </ul>
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diff --git a/stable/generated/braindecode.samplers.BalancedSequenceSampler.html b/stable/generated/braindecode.samplers.BalancedSequenceSampler.html
index d673d6e8..581c6d4b 100644
--- a/stable/generated/braindecode.samplers.BalancedSequenceSampler.html
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+  <script src="../_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
+
+    <script src="../_static/documentation_options.js?v=aad85e6b"></script>
+    <script src="../_static/doctools.js?v=888ff710"></script>
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+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'generated/braindecode.samplers.BalancedSequenceSampler';</script>
     <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.14.3';
         DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = false;
         </script>
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+    <link rel="canonical" href="braindecode.org/index.html" />
+    
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+
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-  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="180" data-default-mode="light">
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
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+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
+  </button>
 
   
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
   <div class="search-button__wrapper">
     <div class="search-button__overlay"></div>
     <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="../search.html" method="get">
+<form class="bd-search d-flex align-items-center"
+      action="../search.html"
+      method="get">
   <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
+  <input type="search"
+         class="form-control"
+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
   <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
-</form>
-    </div>
+</form></div>
   </div>
-
   
-  <nav class="bd-header navbar navbar-expand-lg bd-navbar" id="navbar-main"><div class="bd-header__inner bd-page-width">
+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
   <label class="sidebar-toggle primary-toggle" for="__primary">
-      <span class="fa-solid fa-bars"></span>
+    <span class="fa-solid fa-bars"></span>
   </label>
-  <div id="navbar-start">
-    
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
     
+      <div class="navbar-item">
   
 
-
 <a class="navbar-brand logo" href="../index.html">
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-  
   
   
   
   
   
-
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-    <img src="../_static/braindecode_small.svg" class="logo__image only-light" alt="Logo image">
-    <img src="../_static/braindecode_small.svg" class="logo__image only-dark" alt="Logo image">
+    
+    
+    
+    <img src="../_static/braindecode_symbol.png" class="logo__image only-light" alt="Braindecode Logo"/>
+    <script>document.write(`<img src="../_static/braindecode_symbol.png" class="logo__image only-dark" alt="Braindecode Logo"/>`);</script>
   
   
-</a>
+</a></div>
     
   </div>
-
   
   <div class="col-lg-9 navbar-header-items">
-    <div id="navbar-center" class="mr-auto">
+    
+    <div class="me-auto navbar-header-items__center">
       
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
-        
-                <li class="nav-item">
-                    <a class="nav-link" href="../install.html">
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
                         Install
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-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="../starting.html">
-                        Get Started
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item current active">
-                    <a class="nav-link" href="../api.html">
+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="../api.html">
                         API Reference
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-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
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-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="../help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
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-            
-    </ul>
-</nav>
-      </div>
+  </ul>
+</nav></div>
       
     </div>
-
-    <div id="navbar-end">
-      <div class="navbar-end-item navbar-end__search-button-container">
-        
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-      </div>
+    
+    
+    <div class="navbar-header-items__end">
       
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
-    <a class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></a>
-</span>
-      </div>
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+        </div>
+      
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
       
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
-      </div>
+  </div>
+`);
+</script></div>
       
     </div>
+    
   </div>
-
-
   
-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-  </div>
+  
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
+  
 
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
+    <label class="sidebar-toggle secondary-toggle" for="__secondary" tabindex="0">
       <span class="fa-solid fa-outdent"></span>
-  </label>
+    </label>
   
-
 </div>
-  </nav>
-  
 
+    </nav>
+  
   <div class="bd-container">
     <div class="bd-container__inner bd-page-width">
       
       <div class="bd-sidebar-primary bd-sidebar">
         
+
   
   <div class="sidebar-header-items sidebar-primary__section">
     
     
       <div class="sidebar-header-items__center">
-      
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
         
-                <li class="nav-item">
-                    <a class="nav-link" href="../install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../install/install.html">
                         Install
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../starting.html">
-                        Get Started
-                    </a>
-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="../cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item current active">
-                    <a class="nav-link" href="../api.html">
+                    <li class="nav-item current active">
+                      <a class="nav-link nav-internal" href="../api.html">
                         API Reference
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
-                </button>
-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="../help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="../whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="../whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
-      
+  </ul>
+</nav></div>
+        
       </div>
     
-
     
     
-    <div class="sidebar-header-items__end">
-      
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
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-</span>
-      </div>
-      
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
+  </div>
+`);
+</script></div>
+        
       </div>
-      
-    </div>
     
   </div>
-
   
-  <div class="sidebar-start-items sidebar-primary__section">
-    <div class="sidebar-start-items__item"><nav class="bd-links" id="bd-docs-nav" aria-label="Section navigation">
-  <p class="bd-links__title" role="heading" aria-level="1">
-    Section Navigation
-  </p>
-  <div class="bd-toc-item navbar-nav">
-    <ul class="nav bd-sidenav">
+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
+     aria-label="Section Navigation">
+  <p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
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   <section id="braindecode-samplers-balancedsequencesampler">
-<h1>braindecode.samplers.BalancedSequenceSampler<a class="headerlink" href="#braindecode-samplers-balancedsequencesampler" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.samplers.BalancedSequenceSampler<a class="headerlink" href="#braindecode-samplers-balancedsequencesampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">BalancedSequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sequences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#BalancedSequenceSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">BalancedSequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_sequences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L174-L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler" title="Link to this definition">#</a></dt>
 <dd><p>Balanced sampling of sequences of consecutive windows with categorical
 targets.</p>
 <p>Balanced sampling of sequences inspired by the approach of <a class="reference internal" href="#r3d821f159c06-perslev2021" id="id1">[Perslev2021]</a>:
@@ -498,36 +596,37 @@ <h1>braindecode.samplers.BalancedSequenceSampler<a class="headerlink" href="#bra
 3. Sample a window of the corresponding class in the selected recording.
 4. Extract a sequence of windows around the sampled window.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.
 Must contain a column <cite>target</cite> with categorical targets.</p></li>
-<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
-<li><p><strong>n_sequences</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of sequences to sample.</p></li>
-<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_sequences</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of sequences to sample.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r3d821f159c06-perslev2021"><span class="brackets"><a class="fn-backref" href="#id1">Perslev2021</a></span></dt>
-<dd><p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,
+<div role="list" class="citation-list">
+<div class="citation" id="r3d821f159c06-perslev2021" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Perslev2021</a><span class="fn-bracket">]</span></span>
+<p>Perslev M, Darkner S, Kempfner L, Nikolic M, Jennum PJ,
 Igel C. U-Sleep: resilient high-frequency sleep staging. npj Digit.
 Med. 4, 72 (2021).
 <a class="github reference external" href="https://github.com/perslev/U-Time/blob/master/utime/models/usleep.py">perslev/U-Time</a></p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.samplers.BalancedSequenceSampler.sample_class">
-<span class="sig-name descname"><span class="pre">sample_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#BalancedSequenceSampler.sample_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler.sample_class" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L210-L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.BalancedSequenceSampler.sample_class" title="Link to this definition">#</a></dt>
 <dd><p>Return a random class.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>rec_ind</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Index to the recording to sample from. If None, the recording will
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>rec_ind</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Index to the recording to sample from. If None, the recording will
 be uniformly sampled across available recordings.</p>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><em>int</em> – Sampled class.</p></li>
 <li><p><em>int</em> – Index to the recording the class was sampled from.</p></li>
@@ -542,113 +641,96 @@ <h1>braindecode.samplers.BalancedSequenceSampler<a class="headerlink" href="#bra
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-  <a class="reference internal nav-link" href="#braindecode.samplers.BalancedSequenceSampler">
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.BalancedSequenceSampler"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.BalancedSequenceSampler.sample_class"><code class="docutils literal notranslate"><span class="pre">BalancedSequenceSampler.sample_class()</span></code></a></li>
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diff --git a/stable/generated/braindecode.samplers.RecordingSampler.html b/stable/generated/braindecode.samplers.RecordingSampler.html
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+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,41 +530,67 @@
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   <section id="braindecode-samplers-recordingsampler">
-<h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecode-samplers-recordingsampler" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecode-samplers-recordingsampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RecordingSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#RecordingSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RecordingSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L15-L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler" title="Link to this definition">#</a></dt>
 <dd><p>Base sampler simplifying sampling from recordings.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – <p>DataFrame with at least one of {subject, session, run} columns for each
 window in the BaseConcatDataset to sample examples from. Normally
@@ -507,18 +605,18 @@ <h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecod
 3                  3              1500             2000      -1        4  session_T  run_0
 4                  4              2000             2500      -1        4  session_T  run_0</p>
 </p></li>
-<li><p><strong>random_state</strong> (<em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+<li><p><strong>random_state</strong> (<em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.info">
-<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.info" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.info" title="Link to this definition">#</a></dt>
 <dd><p>Series with MultiIndex index which contains the subject, session, run
 and window indices information in an easily accessible structure for
 quick sampling of windows.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>pd.DataFrame</p>
 </dd>
 </dl>
@@ -526,11 +624,11 @@ <h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecod
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.n_recordings">
-<span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.n_recordings" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">n_recordings</span></span><a class="headerlink" href="#braindecode.samplers.RecordingSampler.n_recordings" title="Link to this definition">#</a></dt>
 <dd><p>Number of recordings available.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)">int</a></p>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -538,180 +636,130 @@ <h1>braindecode.samplers.RecordingSampler<a class="headerlink" href="#braindecod
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_recording">
-<span class="sig-name descname"><span class="pre">sample_recording</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#RecordingSampler.sample_recording"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_recording" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample_recording</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L86-L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_recording" title="Link to this definition">#</a></dt>
 <dd><p>Return a random recording index.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.samplers.RecordingSampler.sample_window">
-<span class="sig-name descname"><span class="pre">sample_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#RecordingSampler.sample_window"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_window" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">sample_window</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rec_ind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L92-L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RecordingSampler.sample_window" title="Link to this definition">#</a></dt>
 <dd><p>Return a specific window.</p>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-samplers-recordingsampler">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RecordingSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-recordingsampler" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RecordingSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-recordingsampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler">
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     RecordingSampler
-    </span>
-   </code>
-  </a>
-  <ul class="nav section-nav flex-column">
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.info">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       RecordingSampler.info
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.n_recordings">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       RecordingSampler.n_recordings
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_recording">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       RecordingSampler.sample_recording()
-      </span>
-     </code>
-    </a>
-   </li>
-   <li class="toc-h3 nav-item toc-entry">
-    <a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_window">
-     <code class="docutils literal notranslate">
-      <span class="pre">
-       RecordingSampler.sample_window()
-      </span>
-     </code>
-    </a>
-   </li>
-  </ul>
- </li>
- <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#examples-using-braindecode-samplers-recordingsampler">
-   Examples using
-   <code class="docutils literal notranslate">
-    <span class="pre">
-     braindecode.samplers.RecordingSampler
-    </span>
-   </code>
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler"><code class="docutils literal notranslate"><span class="pre">RecordingSampler</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.info"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.info</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.n_recordings"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.n_recordings</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_recording"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_recording()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.samplers.RecordingSampler.sample_window"><code class="docutils literal notranslate"><span class="pre">RecordingSampler.sample_window()</span></code></a></li>
 </ul>
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diff --git a/stable/generated/braindecode.samplers.RelativePositioningSampler.html b/stable/generated/braindecode.samplers.RelativePositioningSampler.html
index 2d60666b..3811c7f9 100644
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   <section id="braindecode-samplers-relativepositioningsampler">
-<h1>braindecode.samplers.RelativePositioningSampler<a class="headerlink" href="#braindecode-samplers-relativepositioningsampler" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.samplers.RelativePositioningSampler<a class="headerlink" href="#braindecode-samplers-relativepositioningsampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RelativePositioningSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">same_rec_neg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/ssl.html#RelativePositioningSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">RelativePositioningSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_pos</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_neg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_examples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">same_rec_neg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L14-L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler" title="Link to this definition">#</a></dt>
 <dd><p>Sample examples for the relative positioning task from <a class="reference internal" href="#r0467437a2408-banville2020" id="id1">[Banville2020]</a>.</p>
 <p>Sample examples as tuples of two window indices, with a label indicating
 whether the windows are close or far, as defined by tau_pos and tau_neg.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
-<li><p><strong>tau_pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Size of the positive context, in samples. A positive pair contains two
+<li><p><strong>tau_pos</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the positive context, in samples. A positive pair contains two
 windows x1 and x2 which are separated by at most <cite>tau_pos</cite> samples.</p></li>
-<li><p><strong>tau_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Size of the negative context, in samples. A negative pair contains two
+<li><p><strong>tau_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Size of the negative context, in samples. A negative pair contains two
 windows x1 and x2 which are separated by at least <cite>tau_neg</cite> samples and
 at most <cite>tau_max</cite> samples. Ignored if <cite>same_rec_neg</cite> is False.</p></li>
-<li><p><strong>n_examples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of pairs to extract.</p></li>
-<li><p><strong>tau_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – See <cite>tau_neg</cite>.</p></li>
-<li><p><strong>same_rec_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, sample negative pairs from within the same recording. If
+<li><p><strong>n_examples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of pairs to extract.</p></li>
+<li><p><strong>tau_max</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – See <cite>tau_neg</cite>.</p></li>
+<li><p><strong>same_rec_neg</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample negative pairs from within the same recording. If
 False, sample negative pairs from two different recordings.</p></li>
-<li><p><strong>random_state</strong> (<em>None</em><em> | </em><em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Random state.</p></li>
+<li><p><strong>random_state</strong> (<em>None</em><em> | </em><em>np.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random state.</p></li>
 </ul>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r0467437a2408-banville2020"><span class="brackets"><a class="fn-backref" href="#id1">Banville2020</a></span></dt>
-<dd><p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,
+<div role="list" class="citation-list">
+<div class="citation" id="r0467437a2408-banville2020" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">Banville2020</a><span class="fn-bracket">]</span></span>
+<p>Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. A.,
 &amp; Gramfort, A. (2020). Uncovering the structure of clinical EEG
 signals with self-supervised learning.
 arXiv preprint arXiv:2007.16104.</p>
-</dd>
-</dl>
+</div>
+</div>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.samplers.RelativePositioningSampler.presample">
-<span class="sig-name descname"><span class="pre">presample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/ssl.html#RelativePositioningSampler.presample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler.presample" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">presample</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/ssl.py#L94-L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.RelativePositioningSampler.presample" title="Link to this definition">#</a></dt>
 <dd><p>Presample examples.</p>
 <p>Once presampled, the examples are the same from one epoch to another.</p>
 </dd></dl>
@@ -530,131 +629,105 @@ <h1>braindecode.samplers.RelativePositioningSampler<a class="headerlink" href="#
 </dd></dl>
 
 <section id="examples-using-braindecode-samplers-relativepositioningsampler">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RelativePositioningSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-relativepositioningsampler" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.RelativePositioningSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-relativepositioningsampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
   <div class="sphx-glr-thumbnail-title">Self-supervised learning on EEG with relative positioning</div>
 </div></div><div style='clear:both'></div></section>
 </section>
 
 
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diff --git a/stable/generated/braindecode.samplers.SequenceSampler.html b/stable/generated/braindecode.samplers.SequenceSampler.html
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,55 +530,83 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-samplers-sequencesampler">
-<h1>braindecode.samplers.SequenceSampler<a class="headerlink" href="#braindecode-samplers-sequencesampler" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.samplers.SequenceSampler<a class="headerlink" href="#braindecode-samplers-sequencesampler" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">SequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/samplers/base.html#SequenceSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.SequenceSampler" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.samplers.</span></span><span class="sig-name descname"><span class="pre">SequenceSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_windows_stride</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/samplers/base.py#L109-L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.samplers.SequenceSampler" title="Link to this definition">#</a></dt>
 <dd><p>Sample sequences of consecutive windows.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>metadata</strong> (<em>pd.DataFrame</em>) – See RecordingSampler.</p></li>
-<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
-<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of windows between two consecutive sequences.</p></li>
-<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
+<li><p><strong>n_windows</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of consecutive windows in a sequence.</p></li>
+<li><p><strong>n_windows_stride</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of windows between two consecutive sequences.</p></li>
+<li><p><strong>random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, sample sequences randomly. If False, sample sequences in
+order.</p></li>
+<li><p><strong>random_state</strong> (<em>np.random.RandomState</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em> | </em><em>None</em>) – Random state.</p></li>
 </ul>
 </dd>
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.info">
-<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.info" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">info</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.info" title="Link to this definition">#</a></dt>
 <dd><p>See RecordingSampler.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>pd.DataFrame</p>
 </dd>
 </dl>
@@ -514,11 +614,11 @@ <h1>braindecode.samplers.SequenceSampler<a class="headerlink" href="#braindecode
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="braindecode.samplers.SequenceSampler.file_ids">
-<span class="sig-name descname"><span class="pre">file_ids</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.file_ids" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">file_ids</span></span><a class="headerlink" href="#braindecode.samplers.SequenceSampler.file_ids" title="Link to this definition">#</a></dt>
 <dd><p>Array of shape (n_sequences,) that indicates from which file each
 sequence comes from. Useful e.g. to do self-ensembling.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Type</dt>
+<dt class="field-odd">Type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>np.ndarray of ints</p>
 </dd>
 </dl>
@@ -527,146 +627,112 @@ <h1>braindecode.samplers.SequenceSampler<a class="headerlink" href="#braindecode
 </dd></dl>
 
 <section id="examples-using-braindecode-samplers-sequencesampler">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-sequencesampler" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code><a class="headerlink" href="#examples-using-braindecode-samplers-sequencesampler" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
   <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,51 +530,77 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-training-croppedloss">
-<h1>braindecode.training.CroppedLoss<a class="headerlink" href="#braindecode-training-croppedloss" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.CroppedLoss<a class="headerlink" href="#braindecode-training-croppedloss" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.training.CroppedLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/losses.html#CroppedLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L11-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss" title="Link to this definition">#</a></dt>
 <dd><p>Compute Loss after averaging predictions across time.
 Assumes predictions are in shape:
 n_batch size x n_classes x n_predictions (in time)</p>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.CroppedLoss.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/losses.html#CroppedLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L20-L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedLoss.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
-<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
 </ul>
 </dd>
 </dl>
@@ -511,134 +609,108 @@ <h1>braindecode.training.CroppedLoss<a class="headerlink" href="#braindecode-tra
 </dd></dl>
 
 <section id="examples-using-braindecode-training-croppedloss">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedloss" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedloss" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,175 +530,175 @@
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   <section id="braindecode-training-croppedtimeseriesepochscoring">
-<h1>braindecode.training.CroppedTimeSeriesEpochScoring<a class="headerlink" href="#braindecode-training-croppedtimeseriesepochscoring" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.CroppedTimeSeriesEpochScoring<a class="headerlink" href="#braindecode-training-croppedtimeseriesepochscoring" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTimeSeriesEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#CroppedTimeSeriesEpochScoring"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTimeSeriesEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L229-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring" title="Link to this definition">#</a></dt>
 <dd><p>Class to compute scores for trials from a model that predicts (super)crops with
 time series target.</p>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">
-<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#CroppedTimeSeriesEpochScoring.on_epoch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L234-L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
 <dd><p>Called at the end of each epoch.</p>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-training-croppedtimeseriesepochscoring">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTimeSeriesEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtimeseriesepochscoring" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTimeSeriesEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtimeseriesepochscoring" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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+<ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,189 +530,181 @@
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   <section id="braindecode-training-croppedtrialepochscoring">
-<h1>braindecode.training.CroppedTrialEpochScoring<a class="headerlink" href="#braindecode-training-croppedtrialepochscoring" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.CroppedTrialEpochScoring<a class="headerlink" href="#braindecode-training-croppedtrialepochscoring" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTrialEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#CroppedTrialEpochScoring"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">CroppedTrialEpochScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_train=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_caching=True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L116-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring" title="Link to this definition">#</a></dt>
 <dd><p>Class to compute scores for trials from a model that predicts (super)crops.</p>
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_batch_end">
-<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#CroppedTrialEpochScoring.on_batch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">on_batch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L150-L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end" title="Link to this definition">#</a></dt>
 <dd><p>Called at the end of each batch.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.CroppedTrialEpochScoring.on_epoch_end">
-<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#CroppedTrialEpochScoring.on_epoch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L160-L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end" title="Link to this definition">#</a></dt>
 <dd><p>Called at the end of each epoch.</p>
 </dd></dl>
 
 </dd></dl>
 
 <section id="examples-using-braindecode-training-croppedtrialepochscoring">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTrialEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtrialepochscoring" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.CroppedTrialEpochScoring</span></code><a class="headerlink" href="#examples-using-braindecode-training-croppedtrialepochscoring" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
 </div></div><div style='clear:both'></div></section>
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+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_batch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_batch_end()</span></code></a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.CroppedTrialEpochScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">CroppedTrialEpochScoring.on_epoch_end()</span></code></a></li>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
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@@ -458,52 +530,78 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-training-postepochtrainscoring">
-<h1>braindecode.training.PostEpochTrainScoring<a class="headerlink" href="#braindecode-training-postepochtrainscoring" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.PostEpochTrainScoring<a class="headerlink" href="#braindecode-training-postepochtrainscoring" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">PostEpochTrainScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#PostEpochTrainScoring"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">PostEpochTrainScoring</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scoring</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_extractor=&lt;function</span> <span class="pre">to_numpy&gt;</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L312-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring" title="Link to this definition">#</a></dt>
 <dd><p>Epoch Scoring class that recomputes predictions after the epoch
 on the training in validation mode.</p>
 <p>Note: For unknown reasons, this affects global random generator and
 therefore all results may change slightly if you add this scoring callback.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>scoring</strong> (<em>None</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, or </em><em>callable</em><em> (</em><em>default=None</em><em>)</em>) – If None, use the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of the model. If str, it
+<li><p><strong>scoring</strong> (<em>None</em><em>, </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, or </em><em>callable</em><em> (</em><em>default=None</em><em>)</em>) – If None, use the <code class="docutils literal notranslate"><span class="pre">score</span></code> method of the model. If str, it
 should be a valid sklearn scorer (e.g. “f1”, “accuracy”). If a
 callable, it should have the signature (model, X, y), and it
 should return a scalar. This works analogously to the
 <code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter in sklearn’s <code class="docutils literal notranslate"><span class="pre">GridSearchCV</span></code> et al.</p></li>
-<li><p><strong>lower_is_better</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether lower scores should be considered better or worse.</p></li>
-<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em> or </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – If not an explicit string, tries to infer the name from the
+<li><p><strong>lower_is_better</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=True</em><em>)</em>) – Whether lower scores should be considered better or worse.</p></li>
+<li><p><strong>name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em> or </em><em>None</em><em> (</em><em>default=None</em><em>)</em>) – If not an explicit string, tries to infer the name from the
 <code class="docutils literal notranslate"><span class="pre">scoring</span></code> argument.</p></li>
 <li><p><strong>target_extractor</strong> (<em>callable</em><em> (</em><em>default=to_numpy</em><em>)</em>) – This is called on y before it is passed to scoring.</p></li>
 </ul>
@@ -512,7 +610,7 @@ <h1>braindecode.training.PostEpochTrainScoring<a class="headerlink" href="#brain
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.PostEpochTrainScoring.on_epoch_end">
-<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#PostEpochTrainScoring.on_epoch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_train</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_valid</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L353-L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end" title="Link to this definition">#</a></dt>
 <dd><p>Called at the end of each epoch.</p>
 </dd></dl>
 
@@ -521,113 +619,96 @@ <h1>braindecode.training.PostEpochTrainScoring<a class="headerlink" href="#brain
 <div style='clear:both'></div></section>
 
 
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-      </span>
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- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.PostEpochTrainScoring"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring</span></code></a><ul class="nav section-nav flex-column">
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#braindecode.training.PostEpochTrainScoring.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PostEpochTrainScoring.on_epoch_end()</span></code></a></li>
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diff --git a/stable/generated/braindecode.training.TimeSeriesLoss.html b/stable/generated/braindecode.training.TimeSeriesLoss.html
index 6f7f4112..2ac273af 100644
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+    <div class="sidebar-primary-items__start sidebar-primary__section">
+        <div class="sidebar-primary-item"><nav class="bd-docs-nav bd-links"
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,38 +530,64 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-training-timeseriesloss">
-<h1>braindecode.training.TimeSeriesLoss<a class="headerlink" href="#braindecode-training-timeseriesloss" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.TimeSeriesLoss<a class="headerlink" href="#braindecode-training-timeseriesloss" title="Link to this heading">#</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/losses.html#TimeSeriesLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss" title="Permalink to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">TimeSeriesLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_function</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L35-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss" title="Link to this definition">#</a></dt>
 <dd><p>Compute Loss between timeseries targets and predictions.
 Assumes predictions are in shape:
 n_batch size x n_classes x n_predictions (in time)
@@ -500,13 +598,13 @@ <h1>braindecode.training.TimeSeriesLoss<a class="headerlink" href="#braindecode-
 <p class="rubric">Methods</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="braindecode.training.TimeSeriesLoss.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/losses.html#TimeSeriesLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss.forward" title="Permalink to this definition">#</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L48-L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.TimeSeriesLoss.forward" title="Link to this definition">#</a></dt>
 <dd><p>Forward pass.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
-<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Model’s prediction with shape (batch_size, n_classes, n_times).</p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Target labels with shape (batch_size, n_classes, n_times).</p></li>
 </ul>
 </dd>
 </dl>
@@ -515,131 +613,105 @@ <h1>braindecode.training.TimeSeriesLoss<a class="headerlink" href="#braindecode-
 </dd></dl>
 
 <section id="examples-using-braindecode-training-timeseriesloss">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.TimeSeriesLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-timeseriesloss" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.training.TimeSeriesLoss</span></code><a class="headerlink" href="#examples-using-braindecode-training-timeseriesloss" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
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diff --git a/stable/generated/braindecode.training.mixup_criterion.html b/stable/generated/braindecode.training.mixup_criterion.html
index 556c2e22..1fbedc37 100644
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.classifier.EEGClassifier.html">braindecode.classifier.EEGClassifier</a></li>
 </ul>
 <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.regressor.EEGRegressor.html">braindecode.regressor.EEGRegressor</a></li>
 </ul>
 <ul class="nav bd-sidenav">
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,168 +530,187 @@
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.compute_amplitude_gradients.html">braindecode.visualization.compute_amplitude_gradients</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-training-mixup-criterion">
-<h1>braindecode.training.mixup_criterion<a class="headerlink" href="#braindecode-training-mixup-criterion" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.mixup_criterion<a class="headerlink" href="#braindecode-training-mixup-criterion" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.training.mixup_criterion">
-<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">mixup_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/losses.html#mixup_criterion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.mixup_criterion" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">mixup_criterion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/losses.py#L69-L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.mixup_criterion" title="Link to this definition">#</a></dt>
 <dd><p>Implements loss for Mixup for EEG data. See <a class="reference internal" href="#r54dff61a9a59-1" id="id1">[1]</a>.</p>
 <p>Implementation based on <a class="reference internal" href="#r54dff61a9a59-2" id="id2">[2]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – Predictions from the model.</p></li>
-<li><p><strong>target</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v1.12)"><em>torch.Tensor</em></a>) – For predictions without mixup, the targets as a tensor. If mixup has
+<li><p><strong>preds</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – Predictions from the model.</p></li>
+<li><p><strong>target</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch v2.1)"><em>torch.Tensor</em></a>) – For predictions without mixup, the targets as a tensor. If mixup has
 been applied, a list containing the targets of the two mixed
 samples and the mixing coefficients as tensors.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>loss</strong> – The loss value.</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)">float</a></p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></p>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r54dff61a9a59-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
+<div role="list" class="citation-list">
+<div class="citation" id="r54dff61a9a59-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
 mixup: Beyond Empirical Risk Minimization
 Online: <a class="reference external" href="https://arxiv.org/abs/1710.09412">https://arxiv.org/abs/1710.09412</a></p>
-</dd>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
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@@ -458,51 +530,77 @@
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   <section id="braindecode-training-predict-trials">
-<h1>braindecode.training.predict_trials<a class="headerlink" href="#braindecode-training-predict-trials" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.predict_trials<a class="headerlink" href="#braindecode-training-predict-trials" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.training.predict_trials">
-<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#predict_trials"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.predict_trials" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">predict_trials</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L403-L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.predict_trials" title="Link to this definition">#</a></dt>
 <dd><p>Create trialwise predictions and optionally also return trialwise
 labels from cropped dataset given module.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v1.12)"><em>torch.nn.Module</em></a>) – A pytorch model implementing forward.</p></li>
+<li><p><strong>module</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module" title="(in PyTorch v2.1)"><em>torch.nn.Module</em></a>) – A pytorch model implementing forward.</p></li>
 <li><p><strong>dataset</strong> (<a class="reference internal" href="braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><em>braindecode.datasets.BaseConcatDataset</em></a>) – A braindecode dataset to be predicted.</p></li>
-<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
-<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – The batch size used to iterate the dataset.</p></li>
-<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Number of workers used in DataLoader to iterate the dataset.</p></li>
+<li><p><strong>return_targets</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – If True, additionally returns the trial targets.</p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – The batch size used to iterate the dataset.</p></li>
+<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Number of workers used in DataLoader to iterate the dataset.</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><ul class="simple">
 <li><p><strong>trial_predictions</strong> (<em>np.ndarray</em>) – 3-dimensional array (n_trials x n_classes x n_predictions), where
 the number of predictions depend on the chosen window size and the
@@ -519,102 +617,93 @@ <h1>braindecode.training.predict_trials<a class="headerlink" href="#braindecode-
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diff --git a/stable/generated/braindecode.training.trial_preds_from_window_preds.html b/stable/generated/braindecode.training.trial_preds_from_window_preds.html
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   <section id="braindecode-training-trial-preds-from-window-preds">
-<h1>braindecode.training.trial_preds_from_window_preds<a class="headerlink" href="#braindecode-training-trial-preds-from-window-preds" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.training.trial_preds_from_window_preds<a class="headerlink" href="#braindecode-training-trial-preds-from-window-preds" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.training.trial_preds_from_window_preds">
-<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">trial_preds_from_window_preds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_window_in_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_stop_in_trials</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/training/scoring.html#trial_preds_from_window_preds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.trial_preds_from_window_preds" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.training.</span></span><span class="sig-name descname"><span class="pre">trial_preds_from_window_preds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_window_in_trials</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">i_stop_in_trials</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/training/scoring.py#L21-L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.training.trial_preds_from_window_preds" title="Link to this definition">#</a></dt>
 <dd><p>Assigning window predictions to trials  while removing duplicate
 predictions.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>preds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a><em> of </em><em>ndarrays</em><em> (</em><em>atleast 2darrays</em><em>)</em>) – <dl class="simple">
+<li><p><strong>preds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a><em> of </em><em>ndarrays</em><em> (</em><em>at least 2darrays</em><em>)</em>) – <dl class="simple">
 <dt>List of window predictions, in each window prediction</dt><dd><p>time is in axis=1</p>
 </dd>
 </dl>
 </p></li>
-<li><p><strong>i_window_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – Index/number of window in trial</p></li>
-<li><p><strong>i_stop_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)"><em>list</em></a>) – stop position of window in trial</p></li>
+<li><p><strong>i_window_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – Index/number of window in trial</p></li>
+<li><p><strong>i_stop_in_trials</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><em>list</em></a>) – stop position of window in trial</p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>preds_per_trial</strong> – Predictions in each trial, duplicates removed</p>
 </dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.10)">list</a> of ndarrays</p>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a> of ndarrays</p>
 </dd>
 </dl>
 </dd></dl>
@@ -516,102 +614,93 @@ <h1>braindecode.training.trial_preds_from_window_preds<a class="headerlink" href
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diff --git a/stable/generated/braindecode.util.set_random_seeds.html b/stable/generated/braindecode.util.set_random_seeds.html
index 80db1f38..a385015d 100644
--- a/stable/generated/braindecode.util.set_random_seeds.html
+++ b/stable/generated/braindecode.util.set_random_seeds.html
@@ -1,362 +1,429 @@
 
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-    <title>braindecode.util.set_random_seeds &#8212; Braindecode 0.7 documentation</title>
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+  <div class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav">
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGModuleMixin.html">braindecode.models.EEGModuleMixin</a></li>
+</ul>
+<ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.ShallowFBCSPNet.html">braindecode.models.ShallowFBCSPNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.Deep4Net.html">braindecode.models.Deep4Net</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.DeepSleepNet.html">braindecode.models.DeepSleepNet</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionMI.html">braindecode.models.EEGInceptionMI</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv1.html">braindecode.models.EEGNetv1</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGNetv4.html">braindecode.models.EEGNetv4</a></li>
@@ -402,10 +469,15 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Resample.html">braindecode.preprocessing.Resample</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.DropChannels.html">braindecode.preprocessing.DropChannels</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.SetEEGReference.html">braindecode.preprocessing.SetEEGReference</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Pick.html">braindecode.preprocessing.Pick</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Crop.html">braindecode.preprocessing.Crop</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.datautil.save_concat_dataset.html">braindecode.datautil.save_concat_dataset</a></li>
@@ -458,47 +530,73 @@
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 <li class="toctree-l1"><a class="reference internal" href="braindecode.visualization.plot_confusion_matrix.html">braindecode.visualization.plot_confusion_matrix</a></li>
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   <section id="braindecode-util-set-random-seeds">
-<h1>braindecode.util.set_random_seeds<a class="headerlink" href="#braindecode-util-set-random-seeds" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.util.set_random_seeds<a class="headerlink" href="#braindecode-util-set-random-seeds" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.util.set_random_seeds">
-<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">set_random_seeds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudnn_benchmark</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/util.html#set_random_seeds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.set_random_seeds" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.util.</span></span><span class="sig-name descname"><span class="pre">set_random_seeds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudnn_benchmark</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/util.py#L17-L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.util.set_random_seeds" title="Link to this definition">#</a></dt>
 <dd><p>Set seeds for python random module numpy.random and torch.</p>
 <p>For more details about reproducibility in pytorch see
 <a class="reference external" href="https://pytorch.org/docs/stable/notes/randomness.html">https://pytorch.org/docs/stable/notes/randomness.html</a></p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – Random seed.</p></li>
-<li><p><strong>cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – Whether to set cuda seed with torch.</p></li>
-<li><p><strong>cudnn_benchmark</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em> (</em><em>default=None</em><em>)</em>) – Whether pytorch will use cudnn benchmark. When set to <cite>None</cite> it will not modify
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a>) – Random seed.</p></li>
+<li><p><strong>cuda</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a>) – Whether to set cuda seed with torch.</p></li>
+<li><p><strong>cudnn_benchmark</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em> (</em><em>default=None</em><em>)</em>) – Whether pytorch will use cudnn benchmark. When set to <cite>None</cite> it will not modify
 torch.backends.cudnn.benchmark (displays warning in the case of possible lack of
 reproducibility). When set to True, results may not be reproducible (no warning displayed).
 When set to False it may slow down computations.</p></li>
@@ -514,156 +612,141 @@ <h1>braindecode.util.set_random_seeds<a class="headerlink" href="#braindecode-ut
 </dd></dl>
 
 <section id="examples-using-braindecode-util-set-random-seeds">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.util.set_random_seeds</span></code><a class="headerlink" href="#examples-using-braindecode-util-set-random-seeds" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Regression example on fake data"><img alt="Regression example on fake data" src="../_images/sphx_glr_plot_regression_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_regression.html#sphx-glr-auto-examples-plot-regression-py"><span class="std std-ref">Regression example on fake data</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Regression example on fake data</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to tune hyperparameters with scikit-learn (GridSearchCV) in the set..."><img alt="Hyperparameter tuning with scikit-learn" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.util.set_random_seeds</span></code><a class="headerlink" href="#examples-using-braindecode-util-set-random-seeds" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_how_train_test_and_tune.html#sphx-glr-auto-examples-model-building-plot-how-train-test-and-tune-py"><span class="std std-ref">How to train, test and tune your model?</span></a></p>
+  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model?</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="The braindecode provides some compatibility with scikit-learn. This allows us to use scikit-lea..."><img alt="" src="../_images/sphx_glr_plot_hyperparameter_tuning_with_scikit-learn_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.html#sphx-glr-auto-examples-model-building-plot-hyperparameter-tuning-with-scikit-learn-py"><span class="std std-ref">Hyperparameter tuning with scikit-learn</span></a></p>
   <div class="sphx-glr-thumbnail-title">Hyperparameter tuning with scikit-learn</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using U-Sleep network" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="Data Augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation.html#sphx-glr-auto-examples-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to create a CNN regressor from a CNN classifier by removing softmax func..."><img alt="" src="../_images/sphx_glr_plot_regression_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_regression.html#sphx-glr-auto-examples-model-building-plot-regression-py"><span class="std std-ref">Convolutional neural network regression model on fake data.</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Convolutional neural network regression model on fake data.</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train a Braindecode model with PyTorch. The data preparation and..."><img alt="" src="../_images/sphx_glr_plot_train_in_pure_pytorch_and_pytorch_lightning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.html#sphx-glr-auto-examples-model-building-plot-train-in-pure-pytorch-and-pytorch-lightning-py"><span class="std std-ref">Training a Braindecode model in PyTorch</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Training a Braindecode model in PyTorch</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-advanced-training-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train EEG deep models with data augmentation. It follows the trial-w..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-py"><span class="std std-ref">Data Augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Data Augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="Sleep staging on the Sleep Physionet dataset using Eldele2021" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="Sleep staging on the Sleep Physionet dataset using Chambon2018 network" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="Searching the best data augmentation on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_data_augmentation_search.html#sphx-glr-auto-examples-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to search data augmentations using braindecode. Indeed, it is known tha..."><img alt="" src="../_images/sphx_glr_plot_data_augmentation_search_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_data_augmentation_search.html#sphx-glr-auto-examples-advanced-training-plot-data-augmentation-search-py"><span class="std std-ref">Searching the best data augmentation on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Searching the best data augmentation on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_4_ecog_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-4-ecog-cropped-py"><span class="std std-ref">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="Self-supervised learning on EEG with relative positioning" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_relative_positioning.html#sphx-glr-auto-examples-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows how to train a neural network with self-supervision on sleep EEG data. We fo..."><img alt="" src="../_images/sphx_glr_plot_relative_positioning_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/advanced_training/plot_relative_positioning.html#sphx-glr-auto-examples-advanced-training-plot-relative-positioning-py"><span class="std std-ref">Self-supervised learning on EEG with relative positioning</span></a></p>
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-</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to properly train, tune and test your deep learning models with Bra..."><img alt="How to train, test and tune your model" src="../_images/sphx_glr_plot_how_train_test_and_tune_thumb.png" />
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-  <div class="sphx-glr-thumbnail-title">How to train, test and tune your model</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode on ECoG BCI..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_bcic_iv_4_ecog_trial.html#sphx-glr-auto-examples-applied-examples-plot-bcic-iv-4-ecog-trial-py"><span class="std std-ref">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Fingers flexion decoding on BCIC IV 4 ECoG Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
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@@ -402,10 +469,15 @@
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.preprocessing.Filter.html">braindecode.preprocessing.Filter</a></li>
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@@ -458,139 +530,156 @@
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   <section id="braindecode-visualization-compute-amplitude-gradients">
-<h1>braindecode.visualization.compute_amplitude_gradients<a class="headerlink" href="#braindecode-visualization-compute-amplitude-gradients" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.visualization.compute_amplitude_gradients<a class="headerlink" href="#braindecode-visualization-compute-amplitude-gradients" title="Link to this heading">#</a></h1>
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 <dt class="sig sig-object py" id="braindecode.visualization.compute_amplitude_gradients">
-<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">compute_amplitude_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/visualization/gradients.html#compute_amplitude_gradients"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.compute_amplitude_gradients" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">compute_amplitude_gradients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/gradients.py#L10-L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.compute_amplitude_gradients" title="Link to this definition">#</a></dt>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGConformer.html">braindecode.models.EEGConformer</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInception.html">braindecode.models.EEGInception</a></li>
+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGInceptionERP.html">braindecode.models.EEGInceptionERP</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="braindecode.models.ATCNet.html">braindecode.models.ATCNet</a></li>
 <li class="toctree-l1"><a class="reference internal" href="braindecode.models.EEGITNet.html">braindecode.models.EEGITNet</a></li>
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@@ -402,10 +469,15 @@
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@@ -458,197 +530,215 @@
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   <section id="braindecode-visualization-plot-confusion-matrix">
-<h1>braindecode.visualization.plot_confusion_matrix<a class="headerlink" href="#braindecode-visualization-plot-confusion-matrix" title="Permalink to this heading">#</a></h1>
+<h1>braindecode.visualization.plot_confusion_matrix<a class="headerlink" href="#braindecode-visualization-plot-confusion-matrix" title="Link to this heading">#</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="braindecode.visualization.plot_confusion_matrix">
-<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">plot_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colormap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textcolor='black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontweight='normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_row_labels=90</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_col_labels=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_f1_score=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_axes=(0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_precision=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names_fontsize=12</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/braindecode/visualization/confusion_matrices.html#plot_confusion_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.plot_confusion_matrix" title="Permalink to this definition">#</a></dt>
+<span class="sig-prename descclassname"><span class="pre">braindecode.visualization.</span></span><span class="sig-name descname"><span class="pre">plot_confusion_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confusion_mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colormap=&lt;matplotlib.colors.LinearSegmentedColormap</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">textcolor='black'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmin=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vmax=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fontweight='normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_row_labels=90</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_col_labels=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_f1_score=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_axes=(0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">1)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotate_precision=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names_fontsize=12</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/braindecode/braindecode//blob/master/braindecode/visualization/confusion_matrices.py#L10-L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#braindecode.visualization.plot_confusion_matrix" title="Link to this definition">#</a></dt>
 <dd><p>Generates a confusion matrix with additional precision and sensitivity metrics as in <a class="reference internal" href="#r8046536b33dd-1" id="id1">[1]</a>.</p>
 <dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>confusion_mat</strong> (<em>2d numpy array</em>) – A confusion matrix, e.g. sklearn confusion matrix:
 <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html">https://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html</a></p></li>
 <li><p><strong>class_names</strong> (<em>array</em><em>, </em><em>optional</em>) – List of classes/targets.</p></li>
-<li><p><strong>figsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size of the generated confusion matrix figure.</p></li>
+<li><p><strong>figsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – Size of the generated confusion matrix figure.</p></li>
 <li><p><strong>colormap</strong> (<em>matplotlib cm colormap</em><em>, </em><em>optional</em>) – </p></li>
-<li><p><strong>textcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><em>optional</em>) – Color  of the text in the figure.</p></li>
-<li><p><strong>vmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
-<li><p><strong>vmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.10)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
-<li><p><strong>fontweight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.10)"><em>str</em></a><em>, </em><em>optional</em>) – Weight of the font in the figure:
+<li><p><strong>textcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Color  of the text in the figure.</p></li>
+<li><p><strong>vmin</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>vmax</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><em>float</em></a><em>, </em><em>optional</em>) – The data range that the colormap covers.</p></li>
+<li><p><strong>fontweight</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><em>str</em></a><em>, </em><em>optional</em>) – Weight of the font in the figure:
 [ ‘normal’ | ‘bold’ | ‘heavy’ | ‘light’ | ‘ultrabold’ | ‘ultralight’]</p></li>
-<li><p><strong>rotate_row_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the row labels</p></li>
-<li><p><strong>rotate_col_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the column labels</p></li>
-<li><p><strong>with_f1_score</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
-<li><p><strong>norm_axes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.10)"><em>tuple</em></a><em>, </em><em>optional</em>) – </p></li>
-<li><p><strong>rotate_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
-<li><p><strong>class_names_fontsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>rotate_row_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the row labels</p></li>
+<li><p><strong>rotate_col_labels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – The rotation angle of the column labels</p></li>
+<li><p><strong>with_f1_score</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>norm_axes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><em>tuple</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>rotate_precision</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><em>bool</em></a><em>, </em><em>optional</em>) – </p></li>
+<li><p><strong>class_names_fontsize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><em>int</em></a><em>, </em><em>optional</em>) – </p></li>
 </ul>
 </dd>
-<dt class="field-even">Returns</dt>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
 <dd class="field-even"><p><strong>fig</strong></p>
 </dd>
-<dt class="field-odd">Return type</dt>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
 <dd class="field-odd"><p>matplotlib figure</p>
 </dd>
 </dl>
 <p class="rubric">References</p>
-<dl class="citation">
-<dt class="label" id="r8046536b33dd-1"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
-<dd><p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
+<div role="list" class="citation-list">
+<div class="citation" id="r8046536b33dd-1" role="doc-biblioentry">
+<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
+<p>Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
 Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. &amp; Ball, T. (2017).
 Deep learning with convolutional neural networks for EEG decoding and
 visualization.
 Human Brain Mapping , Aug. 2017. Online: <a class="reference external" href="http://dx.doi.org/10.1002/hbm.23730">http://dx.doi.org/10.1002/hbm.23730</a></p>
-</dd>
-</dl>
+</div>
+</div>
 </dd></dl>
 
 <section id="examples-using-braindecode-visualization-plot-confusion-matrix">
-<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix</span></code><a class="headerlink" href="#examples-using-braindecode-visualization-plot-confusion-matrix" title="Permalink to this heading">#</a></h2>
-<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="Trialwise Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Trialwise Decoding on BCIC IV 2a Dataset</span></a></p>
-  <div class="sphx-glr-thumbnail-title">Trialwise Decoding on BCIC IV 2a Dataset</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="Cropped Decoding on BCIC IV 2a Dataset" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
-<p><a class="reference internal" href="../auto_examples/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
+<h2>Examples using <code class="docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix</span></code><a class="headerlink" href="#examples-using-braindecode-visualization-plot-confusion-matrix" title="Link to this heading">#</a></h2>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="Cropped Decoding on BCIC IV 2a Dataset"><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_cropped.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-cropped-py"><span class="std std-ref">Cropped Decoding on BCIC IV 2a Dataset</span></a></p>
   <div class="sphx-glr-thumbnail-title">Cropped Decoding on BCIC IV 2a Dataset</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows you how to train and test deep learning models with Braindecode in a classi..."><img alt="" src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/model_building/plot_bcic_iv_2a_moabb_trial.html#sphx-glr-auto-examples-model-building-plot-bcic-iv-2a-moabb-trial-py"><span class="std std-ref">Basic Brain Decoding on EEG Data</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Basic Brain Decoding on EEG Data</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_chambon2018_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_chambon2018.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-chambon2018-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Chambon2018 network</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We u..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_eldele2021_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_eldele2021.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-eldele2021-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using Eldele2021</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using Eldele2021</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This tutorial shows how to train and test a sleep staging neural network with Braindecode. We a..."><img alt="" src="../_images/sphx_glr_plot_sleep_staging_usleep_thumb.png" />
+<p><a class="reference internal" href="../auto_examples/applied_examples/plot_sleep_staging_usleep.html#sphx-glr-auto-examples-applied-examples-plot-sleep-staging-usleep-py"><span class="std std-ref">Sleep staging on the Sleep Physionet dataset using U-Sleep network</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Sleep staging on the Sleep Physionet dataset using U-Sleep network</div>
 </div></div><div style='clear:both'></div></section>
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-                    <a class="nav-link" href="cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="api.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
                         API Reference
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
-                </button>
-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
+  </ul>
+</nav></div>
       
     </div>
-
-    <div id="navbar-end">
-      <div class="navbar-end-item navbar-end__search-button-container">
-        
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-      </div>
+    
+    
+    <div class="navbar-header-items__end">
       
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
-    <a class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></a>
-</span>
-      </div>
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+        </div>
       
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
-      </div>
+  </div>
+`);
+</script></div>
       
     </div>
+    
   </div>
-
-
   
-  <div class="search-button-container--mobile">
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-  </div>
-
   
-  <label class="sidebar-toggle secondary-toggle" for="__secondary">
-      <span class="fa-solid fa-outdent"></span>
-  </label>
+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+    </div>
   
 
-</div>
-  </nav>
   
+</div>
 
+    </nav>
+  
   <div class="bd-container">
     <div class="bd-container__inner bd-page-width">
       
       <div class="bd-sidebar-primary bd-sidebar hide-on-wide">
         
+
   
   <div class="sidebar-header-items sidebar-primary__section">
     
     
       <div class="sidebar-header-items__center">
-      
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
         
-                <li class="nav-item">
-                    <a class="nav-link" href="install.html">
+          <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
                         Install
-                    </a>
-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="starting.html">
-                        Get Started
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="api.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
                         API Reference
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
-                </button>
-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
-      
+  </ul>
+</nav></div>
+        
       </div>
     
-
     
     
-    <div class="sidebar-header-items__end">
-      
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
-    <a class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></a>
-</span>
-      </div>
-      
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
+  </div>
+`);
+</script></div>
+        
       </div>
-      
-    </div>
     
   </div>
-
   
-
   
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
-    </div>
+  <div class="sidebar-primary-items__end sidebar-primary__section">
   </div>
+  
+  <div id="rtd-footer-container"></div>
+
 
       </div>
-      <main class="bd-main">
+      
+      <main id="main-content" class="bd-main">
         
         
-        <div class="bd-content">
-          <div class="bd-article-container">
-            
-            <div class="bd-header-article">
-                
-            </div>
-            
-            
-            <article class="bd-article" role="main">
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article"></div>
               
+              
+<div>
+  
 
 <h1 id="index">Index</h1>
 
@@ -418,6 +474,8 @@ <h2 id="A">A</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet">ATCNet (class in braindecode.models)</a>
+</li>
       <li><a href="generated/braindecode.augmentation.AugmentedDataLoader.html#braindecode.augmentation.AugmentedDataLoader">AugmentedDataLoader (class in braindecode.augmentation)</a>
 </li>
   </ul></td>
@@ -499,6 +557,8 @@ <h2 id="C">C</h2>
         <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.criterion_">(braindecode.regressor.EEGRegressor attribute)</a>
 </li>
       </ul></li>
+      <li><a href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop">Crop (class in braindecode.preprocessing)</a>
+</li>
       <li><a href="generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss">CroppedLoss (class in braindecode.training)</a>
 </li>
       <li><a href="generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring">CroppedTimeSeriesEpochScoring (class in braindecode.training)</a>
@@ -518,10 +578,14 @@ <h2 id="D">D</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net">Deep4Net (class in braindecode.models)</a>
+</li>
+      <li><a href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet">DeepSleepNet (class in braindecode.models)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4.download">download() (braindecode.datasets.BCICompetitionIVDataset4 static method)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels">DropChannels (class in braindecode.preprocessing)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -530,15 +594,23 @@ <h2 id="E">E</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier">EEGClassifier (class in braindecode.classifier)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer">EEGConformer (class in braindecode.models)</a>
 </li>
       <li><a href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception">EEGInception (class in braindecode.models)</a>
 </li>
-      <li><a href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet">EEGITNet (class in braindecode.models)</a>
+      <li><a href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP">EEGInceptionERP (class in braindecode.models)</a>
 </li>
-      <li><a href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1">EEGNetv1 (class in braindecode.models)</a>
+      <li><a href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI">EEGInceptionMI (class in braindecode.models)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet">EEGITNet (class in braindecode.models)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin">EEGModuleMixin (class in braindecode.models)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGNetv1.html#braindecode.models.EEGNetv1">EEGNetv1 (class in braindecode.models)</a>
+</li>
       <li><a href="generated/braindecode.models.EEGNetv4.html#braindecode.models.EEGNetv4">EEGNetv4 (class in braindecode.models)</a>
 </li>
       <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor">EEGRegressor (class in braindecode.regressor)</a>
@@ -556,6 +628,8 @@ <h2 id="F">F</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler.file_ids">file_ids (braindecode.samplers.SequenceSampler attribute)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter">Filter (class in braindecode.preprocessing)</a>
 </li>
       <li><a href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank">filterbank() (in module braindecode.preprocessing)</a>
 </li>
@@ -565,6 +639,14 @@ <h2 id="F">F</h2>
 
       <ul>
         <li><a href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform.forward">(braindecode.augmentation.Transform method)</a>
+</li>
+        <li><a href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet.forward">(braindecode.models.ATCNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet.forward">(braindecode.models.DeepSleepNet method)</a>
+</li>
+        <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer.forward">(braindecode.models.EEGConformer method)</a>
+</li>
+        <li><a href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI.forward">(braindecode.models.EEGInceptionMI method)</a>
 </li>
         <li><a href="generated/braindecode.models.HybridNet.html#braindecode.models.HybridNet.forward">(braindecode.models.HybridNet method)</a>
 </li>
@@ -633,6 +715,8 @@ <h2 id="G">G</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer.get_fc_size">get_fc_size() (braindecode.models.EEGConformer method)</a>
+</li>
       <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_iterator">get_iterator() (braindecode.classifier.EEGClassifier method)</a>
 
       <ul>
@@ -640,14 +724,16 @@ <h2 id="G">G</h2>
 </li>
       </ul></li>
       <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.get_loss">get_loss() (braindecode.classifier.EEGClassifier method)</a>
+</li>
+      <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata">get_metadata() (braindecode.datasets.BaseConcatDataset method)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.get_output_shape">get_output_shape() (braindecode.models.EEGModuleMixin method)</a>
 
       <ul>
-        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.get_loss">(braindecode.regressor.EEGRegressor method)</a>
+        <li><a href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape">(in module braindecode.models)</a>
 </li>
       </ul></li>
-      <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.get_metadata">get_metadata() (braindecode.datasets.BaseConcatDataset method)</a>
-</li>
-      <li><a href="generated/braindecode.models.get_output_shape.html#braindecode.models.get_output_shape">get_output_shape() (in module braindecode.models)</a>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.get_torchinfo_statistics">get_torchinfo_statistics() (braindecode.models.EEGModuleMixin method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -692,6 +778,10 @@ <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.datautil.load_concat_dataset.html#braindecode.datautil.load_concat_dataset">load_concat_dataset() (in module braindecode.datautil)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.load_state_dict">load_state_dict() (braindecode.models.EEGModuleMixin method)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -736,14 +826,8 @@ <h2 id="N">N</h2>
 <h2 id="O">O</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.on_batch_end">on_batch_end() (braindecode.classifier.EEGClassifier method)</a>
-
-      <ul>
-        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.on_batch_end">(braindecode.regressor.EEGRegressor method)</a>
+      <li><a href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end">on_batch_end() (braindecode.training.CroppedTrialEpochScoring method)</a>
 </li>
-        <li><a href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end">(braindecode.training.CroppedTrialEpochScoring method)</a>
-</li>
-      </ul></li>
       <li><a href="generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end">on_epoch_end() (braindecode.training.CroppedTimeSeriesEpochScoring method)</a>
 
       <ul>
@@ -786,6 +870,8 @@ <h2 id="O">O</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick">Pick (class in braindecode.preprocessing)</a>
+</li>
       <li><a href="generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix">plot_confusion_matrix() (in module braindecode.visualization)</a>
 </li>
       <li><a href="generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring">PostEpochTrainScoring (class in braindecode.training)</a>
@@ -808,12 +894,6 @@ <h2 id="P">P</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.predict_with_window_inds_and_ys">predict_with_window_inds_and_ys() (braindecode.classifier.EEGClassifier method)</a>
-
-      <ul>
-        <li><a href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys">(braindecode.regressor.EEGRegressor method)</a>
-</li>
-      </ul></li>
       <li><a href="generated/braindecode.classifier.EEGClassifier.html#braindecode.classifier.EEGClassifier.prefixes_">prefixes_ (braindecode.classifier.EEGClassifier attribute)</a>
 
       <ul>
@@ -837,6 +917,8 @@ <h2 id="R">R</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler">RelativePositioningSampler (class in braindecode.samplers)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample">Resample (class in braindecode.preprocessing)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -853,8 +935,6 @@ <h2 id="S">S</h2>
       <li><a href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset.save">save() (braindecode.datasets.BaseConcatDataset method)</a>
 </li>
       <li><a href="generated/braindecode.datautil.save_concat_dataset.html#braindecode.datautil.save_concat_dataset">save_concat_dataset() (in module braindecode.datautil)</a>
-</li>
-      <li><a href="generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale">scale() (in module braindecode.preprocessing)</a>
 </li>
       <li><a href="generated/braindecode.augmentation.functional.sensors_rotation.html#braindecode.augmentation.functional.sensors_rotation">sensors_rotation() (in module braindecode.augmentation.functional)</a>
 </li>
@@ -879,6 +959,8 @@ <h2 id="S">S</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.util.set_random_seeds.html#braindecode.util.set_random_seeds">set_random_seeds() (in module braindecode.util)</a>
+</li>
+      <li><a href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference">SetEEGReference (class in braindecode.preprocessing)</a>
 </li>
       <li><a href="generated/braindecode.models.ShallowFBCSPNet.html#braindecode.models.ShallowFBCSPNet">ShallowFBCSPNet (class in braindecode.models)</a>
 </li>
@@ -919,6 +1001,8 @@ <h2 id="T">T</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss">TimeSeriesLoss (class in braindecode.training)</a>
+</li>
+      <li><a href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin.to_dense_prediction_model">to_dense_prediction_model() (braindecode.models.EEGModuleMixin method)</a>
 </li>
       <li><a href="generated/braindecode.augmentation.Transform.html#braindecode.augmentation.Transform">Transform (class in braindecode.augmentation)</a>
 </li>
@@ -949,67 +1033,61 @@ <h2 id="W">W</h2>
 
 
 
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
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-<div class="toc-item">
-  
-<div id="searchbox"></div>
-</div>
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 </div>
 
-<div class="toc-item">
-  
+              
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+              
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+                  <!-- Previous / next buttons -->
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+            
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+          </footer>
         
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-
   
     
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+  <script src="_static/scripts/bootstrap.js?digest=365ca57ee442770a23c6"></script>
+<script src="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6"></script>
 
-  <footer class="bd-footer"><div class="bd-footer__inner container">
-  
-  <div class="footer-item">
+    <script src="https://braindecode.org/versionwarning.js"></script>
     
-<p class="copyright">
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+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
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+        <div class="footer-item"><p class="text-center text-muted small">&copy; Copyright 2018–2023, Braindecode Developers.</p></div>
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+      
+    </div>
   
 </div>
+
   </footer>
   </body>
 </html>
\ No newline at end of file
diff --git a/stable/help.html b/stable/help.html
index c0b9c1e1..76952551 100644
--- a/stable/help.html
+++ b/stable/help.html
@@ -1,379 +1,465 @@
 
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-<html lang="en">
+
+<html lang="en" data-theme="light">
+
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-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Frequently Asked Questions (FAQ) &#8212; Braindecode 0.7 documentation</title>
-  <script>
+    <title>Frequently Asked Questions (FAQ) &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
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+  <link href="_static/styles/theme.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="_static/styles/bootstrap.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
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+  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
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+  <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
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+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
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+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
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+        DOCUMENTATION_OPTIONS.theme_version = '0.14.3';
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-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = false;
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+         placeholder="Search the docs ..."
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+      
+    </div>
+  
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+</div>
+              
               
+<div>
+  
   <section id="frequently-asked-questions-faq">
-<span id="faq"></span><h1>Frequently Asked Questions (FAQ)<a class="headerlink" href="#frequently-asked-questions-faq" title="Permalink to this heading">#</a></h1>
+<span id="faq"></span><h1>Frequently Asked Questions (FAQ)<a class="headerlink" href="#frequently-asked-questions-faq" title="Link to this heading">#</a></h1>
 <section id="how-do-i-cite-braindecode">
-<h2>How do I cite Braindecode?<a class="headerlink" href="#how-do-i-cite-braindecode" title="Permalink to this heading">#</a></h2>
+<h2>How do I cite Braindecode?<a class="headerlink" href="#how-do-i-cite-braindecode" title="Link to this heading">#</a></h2>
 <p>See  <a class="reference internal" href="cite.html#cite"><span class="std std-ref">How to cite Braindecode</span></a>.</p>
 </section>
 <section id="help-i-can-t-get-python-and-braindecode-working">
-<h2>Help! I can’t get Python and Braindecode working!<a class="headerlink" href="#help-i-can-t-get-python-and-braindecode-working" title="Permalink to this heading">#</a></h2>
+<h2>Help! I can’t get Python and Braindecode working!<a class="headerlink" href="#help-i-can-t-get-python-and-braindecode-working" title="Link to this heading">#</a></h2>
 <p>Check out if your are running the most recent version of braindecode.</p>
 </section>
 <section id="i-still-can-t-get-it-to-work">
-<h2>I still can’t get it to work!<a class="headerlink" href="#i-still-can-t-get-it-to-work" title="Permalink to this heading">#</a></h2>
+<h2>I still can’t get it to work!<a class="headerlink" href="#i-still-can-t-get-it-to-work" title="Link to this heading">#</a></h2>
 <p>When you encounter an error message or unexpected results, it can be hard to
 tell whether it happened because of a bug in Braindecode, a mistake in user
 code, a corrupted data file, or irregularities in the data itself.</p>
@@ -384,23 +470,23 @@ <h2>I still can’t get it to work!<a class="headerlink" href="#i-still-can-t-ge
 direct you to the chat.</p>
 </section>
 <section id="i-think-i-found-a-bug-what-do-i-do">
-<h2>I think I found a bug, what do I do?<a class="headerlink" href="#i-think-i-found-a-bug-what-do-i-do" title="Permalink to this heading">#</a></h2>
+<h2>I think I found a bug, what do I do?<a class="headerlink" href="#i-think-i-found-a-bug-what-do-i-do" title="Link to this heading">#</a></h2>
 <p>If you’re pretty sure the problem you’ve encountered
 is a software bug (not bad data or user error):</p>
 <ul>
 <li><p>Make sure you’re using <a class="reference external" href="https://github.com/mne-tools/mne-python/releases/latest">the most current version</a>. You can check it locally
 at a shell prompt with:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>python -c <span class="s2">&quot;import braindecode; print(braindecode.__version__)&quot;</span>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>python<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import braindecode; print(braindecode.__version__)&quot;</span>
 </pre></div>
 </div>
 <p>which will also give you version info about braindecode.</p>
 </li>
 <li><p>If you’re already on the most current version, if possible try using
-<a class="reference external" href="https://mne.tools/stable/install/updating.html#installing-main" title="(in MNE v1.2)"><span class="xref std std-ref">the latest development version</span></a>, as the bug may
+<a class="reference external" href="https://mne.tools/stable/install/updating.html#installing-main" title="(in MNE v1.5)"><span class="xref std std-ref">the latest development version</span></a>, as the bug may
 have been fixed already since the latest release. If you can’t try the latest
 development version, search the GitHub issues page to see if the problem has
 already been reported and/or fixed.</p></li>
-<li><p>Try to replicate the problem with one of the <a class="reference external" href="https://skorch.readthedocs.io/en/stable/user/neuralnet.html#datasets" title="(in skorch v0.12)"><span class="xref std std-ref">MNE sample datasets</span></a>. If you can’t replicate it with a built-in dataset, provide a
+<li><p>Try to replicate the problem with one of the <a class="reference external" href="https://skorch.readthedocs.io/en/stable/user/neuralnet.html#datasets" title="(in skorch v0.15)"><span class="xref std std-ref">MNE sample datasets</span></a>. If you can’t replicate it with a built-in dataset, provide a
 link to a small, anonymized portion of your data that does yield the error.</p></li>
 </ul>
 <p>If the problem persists, <a class="reference external" href="https://github.com/braindecode/braindecode/issues/new">open a new issue</a>
@@ -412,123 +498,102 @@ <h2>I think I found a bug, what do I do?<a class="headerlink" href="#i-think-i-f
 and replicate the bug on their own computers.</p>
 </section>
 <section id="references">
-<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2>
+<h2>References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
 </section>
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-  <a class="reference internal nav-link" href="#help-i-can-t-get-python-and-braindecode-working">
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-  <a class="reference internal nav-link" href="#i-think-i-found-a-bug-what-do-i-do">
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-   References
-  </a>
- </li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-do-i-cite-braindecode">How do I cite Braindecode?</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#help-i-can-t-get-python-and-braindecode-working">Help! I can’t get Python and Braindecode working!</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#i-still-can-t-get-it-to-work">I still can’t get it to work!</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#i-think-i-found-a-bug-what-do-i-do">I think I found a bug, what do I do?</a></li>
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-  <footer class="bd-footer"><div class="bd-footer__inner container">
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+    <script src="https://braindecode.org/versionwarning.js"></script>
     
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+
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\ No newline at end of file
diff --git a/stable/index.html b/stable/index.html
index 6dee6581..d2745e5e 100644
--- a/stable/index.html
+++ b/stable/index.html
@@ -1,368 +1,424 @@
 
 <!DOCTYPE html>
 
-<html lang="en">
+
+<html lang="en" data-theme="light">
+
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Braindecode &#8212; Braindecode 0.7 documentation</title>
-  <script>
+    <title>Braindecode &#8212; Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
     document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
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+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
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+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+        </div>
       
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+<script>
+document.write(`
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+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-2"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-2"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-2">
+      <!-- dropdown will be populated by javascript on page load -->
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+  </div>
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+</script></div>
       
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+    
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+    <div class="navbar-persistent--mobile">
+ <script>
+ document.write(`
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+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
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+          <div class="navbar-item">
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+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
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+`);
+</script></div>
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+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
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-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
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   <style type="text/css">h1 {display:none;}</style><section id="braindecode-homepage">
-<h1>Braindecode Homepage<a class="headerlink" href="#braindecode-homepage" title="Permalink to this heading">#</a></h1>
+<h1>Braindecode Homepage<a class="headerlink" href="#braindecode-homepage" title="Link to this heading">#</a></h1>
 <img alt="Braindecode" class="logo mainlogo only-light align-center" src="_images/braindecode.svg" /><img alt="Braindecode" class="logo mainlogo only-dark align-center" src="_images/braindecode.svg" /><p class="h4 text-center font-weight-light my-4">Braindecode is an open-source Python toolbox for decoding raw electrophysiological brain
 data with deep learning models. It includes dataset fetchers, data preprocessing and
 visualization tools, as well as implementations of several deep learning
@@ -371,108 +427,158 @@ <h1>Braindecode Homepage<a class="headerlink" href="#braindecode-homepage" title
 deep learning researchers who want to work with neurophysiological data.</p>
 <div class="toctree-wrapper compound">
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+            result = await fetch(url);
+            data = await result.json();
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+            while (true) {
+              data = await getContribs(
+                `https://api.github.com/repos/braindecode/braindecode/contributors?per_page=100&page=${page}`
+              );
+              if (!data.length) {
+                break;
+              }
+              addCards(data, inner);
+              page++;
+            }
+            // finish
+            outer.append(title, inner);
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+        // Call the function after DOM is fully loaded
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diff --git a/stable/install.html b/stable/install.html
deleted file mode 100644
index a002e701..00000000
--- a/stable/install.html
+++ /dev/null
@@ -1,509 +0,0 @@
-
-<!DOCTYPE html>
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-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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-  <section id="install-via-pip-or-conda">
-<h1>Install via <code class="code docutils literal notranslate"><span class="pre">pip</span></code> or <code class="code docutils literal notranslate"><span class="pre">conda</span></code><a class="headerlink" href="#install-via-pip-or-conda" title="Permalink to this heading">#</a></h1>
-<p>Braindecode requires two libraries already installed before being installed, <code class="docutils literal notranslate"><span class="pre">PyTorch</span></code> and <code class="docutils literal notranslate"><span class="pre">moabb</span></code> if you want to use MOABB datasets utilities.</p>
-<section id="preparing-the-environment-for-braindecode-installation">
-<h2>Preparing the environment for braindecode installation<a class="headerlink" href="#preparing-the-environment-for-braindecode-installation" title="Permalink to this heading">#</a></h2>
-<p>We suggest installing the Pytorch into its own <code class="docutils literal notranslate"><span class="pre">conda</span></code> or <code class="docutils literal notranslate"><span class="pre">pip</span></code> environment.
-Installing the PyTorch library depends on the architecture and operational system you are running, as well as driver versions and optimizations for deep learning.</p>
-<p>Given this complexity, we recommend that you go directly to the <code class="docutils literal notranslate"><span class="pre">pytorch</span></code>
-page and follow the instructions in <a class="reference external" href="http://pytorch.org/">http://pytorch.org/</a>. You don’t need to install torchvision.</p>
-<p>To install moabb, run in your terminal:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>pip install moabb
-</pre></div>
-</div>
-</section>
-<section id="installing-the-braindecode-with-pip">
-<h2>Installing the braindecode with pip<a class="headerlink" href="#installing-the-braindecode-with-pip" title="Permalink to this heading">#</a></h2>
-<p>After preparing the environment for braindecode, you can install via <code class="code docutils literal notranslate"><span class="pre">pip</span></code>:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>pip install braindecode
-</pre></div>
-</div>
-<p>alternatively, you can install the latest version of braindecode via pip:</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">pip install -U https://api.github.com/repos/braindecode/braindecode/zipball/master</span>
-</pre></div>
-</div>
-</section>
-<section id="installing-braindecode-with-conda">
-<h2>Installing Braindecode with conda<a class="headerlink" href="#installing-braindecode-with-conda" title="Permalink to this heading">#</a></h2>
-<p>You can also create a conda environment for installing the library.
-This might help depending on how you are managing other libraries.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>curl -O https://raw.githubusercontent.com/braindecode/braindecode/master/environment.yml
-conda env create -f environment.yml
-conda activate braindecode
-</pre></div>
-</div>
-<p>This will create a new <code class="docutils literal notranslate"><span class="pre">conda</span></code> environment called <code class="docutils literal notranslate"><span class="pre">braindecode</span></code> (you can adjust
-this by passing a different name via <code class="docutils literal notranslate"><span class="pre">--name</span></code>).</p>
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diff --git a/stable/install/install.html b/stable/install/install.html
new file mode 100644
index 00000000..58b38ab0
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@@ -0,0 +1,580 @@
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+            <div class="bd-article-container">
+              
+              <div class="bd-header-article">
+<div class="header-article-items header-article__inner">
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+</nav>
+</div>
+      
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+  
+  
+</div>
+</div>
+              
+              
+<div>
+  
+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h1>
+<p>Braindecode is written in Python 3, specifically for version 3.8 or above.</p>
+<p>The package is distributed via Python package index (<a class="reference external" href="https://pypi.org/project/braincode">PyPI</a>), and you can access the
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+              
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+  
+  <section id="installing-from-pypi">
+<span id="install-pip"></span><h1>Installing from PyPI<a class="headerlink" href="#installing-from-pypi" title="Link to this heading">#</a></h1>
+<p>Braincode can be installed via pip from <a class="reference external" href="https://pypi.org/project/braindecode">PyPI</a>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We recommend the most updated version of pip to install from PyPI.</p>
+</div>
+<p>Below are the installation commands for the most common use cases.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">pip install braindecode</span>
+</pre></div>
+</div>
+<p>Braindecode can also be installed along MOABB to download open datasets:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>moabb
+</pre></div>
+</div>
+<p>To use the potential of the deep learning modules PyTorch with GPU, we recommend the following sequence before installing the braindecode:</p>
+<ol class="arabic simple">
+<li><p>Install the latest NVIDIA driver.</p></li>
+<li><p>Check <a class="reference external" href="https://pytorch.org/get-started/locally/">PyTorch Official Guide</a>, for the recommended CUDA versions. For the Pip package, the user must download the CUDA manually, install it on the system, and ensure CUDA_PATH is appropriately set and working!</p></li>
+<li><p>Continue to follow the guide and install PyTorch.</p></li>
+</ol>
+<p>See <a class="reference external" href="braindecode.faq">Troubleshooting</a> section if you have a problem.</p>
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+              
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+  
+  <section id="installing-from-sources">
+<span id="install-source"></span><h1>Installing from sources<a class="headerlink" href="#installing-from-sources" title="Link to this heading">#</a></h1>
+<p>If you want to test features under development or contribute to the library, or if you want to test the new tools that have been tested in braindecode and not released yet, this is the right tutorial for you!</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are only trying to install Braindecode, we recommend using the pip installation <a class="reference external" href="https://braindecode.org/braindecode/install/install_pip.html#install-pip">Installation</a> for details on that.</p>
+</div>
+<section id="clone-the-repository-from-github">
+<span id="system-level"></span><h2>Clone the repository from GitHub<a class="headerlink" href="#clone-the-repository-from-github" title="Link to this heading">#</a></h2>
+<p>The first thing you should do is clone the Braindecode repository to your computer and enter inside the repository.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/braindecode/braindecode<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>braindecode
+</pre></div>
+</div>
+<p>You should now be in the root directory of the Braindecode repository.</p>
+</section>
+<section id="installing-braindecode-from-the-source">
+<h2>Installing Braindecode from the source<a class="headerlink" href="#installing-braindecode-from-the-source" title="Link to this heading">#</a></h2>
+<p>If you want to only install Braindecode from source once and not do any development
+work, then the recommended way to build and install is to use <code class="docutils literal notranslate"><span class="pre">pip</span></code>:</p>
+<p>For the latest development version, directly from GitHub:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-U<span class="w"> </span>https://api.github.com/repos/braindecode/braindecode/zipball/master#egg<span class="o">=</span>braindecode
+</pre></div>
+</div>
+<p>If you have a local clone of the Braindecode git repository:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
+</pre></div>
+</div>
+<p>This will install Braindecode in editable mode, i.e., changes to the source code could be used
+directly in python.</p>
+<p>You could also install optional dependency, like to import datasets from MOABB.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="o">[</span>moabb<span class="o">]</span>
+</pre></div>
+</div>
+<p>There is also optional dependencies for unit testing and building documentation, you could install
+them if you want to contribute to Braindecode.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.<span class="o">[</span>moabb,tests,docs<span class="o">]</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="testing-if-your-installation-is-working">
+<h1>Testing if your installation is working<a class="headerlink" href="#testing-if-your-installation-is-working" title="Link to this heading">#</a></h1>
+<p>To verify that Braindecode is installed and running correctly, run the following command:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python -m &quot;import braindecode; braindecode.__version__&quot;</span>
+</pre></div>
+</div>
+<p>For more information, please see the <a class="reference external" href="https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md">contributors’ guidelines</a>.</p>
+</section>
+
+
+  
+</div>
+
+              
+              
+              
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+                <footer class="prev-next-footer">
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+    <i class="fa-solid fa-list"></i> On this page
+  </div>
+  <nav class="bd-toc-nav page-toc">
+    <ul class="visible nav section-nav flex-column">
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">Installing from sources</a><ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#clone-the-repository-from-github">Clone the repository from GitHub</a></li>
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#installing-braindecode-from-the-source">Installing Braindecode from the source</a></li>
+</ul>
+</li>
+<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#testing-if-your-installation-is-working">Testing if your installation is working</a></li>
+</ul>
+
+  </nav></div>
+
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+              
+            
+          </div>
+          <footer class="bd-footer-content">
+            
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+        
+      </main>
+    </div>
+  </div>
+  
+    
+  <!-- Scripts loaded after <body> so the DOM is not blocked -->
+  <script src="../_static/scripts/bootstrap.js?digest=365ca57ee442770a23c6"></script>
+<script src="../_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6"></script>
+
+    <script src="https://braindecode.org/versionwarning.js"></script>
+    
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+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item"><p class="text-center text-muted small">&copy; Copyright 2018–2023, Braindecode Developers.</p></div>
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+  
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+    <div class="footer-items__end">
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+  Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.14.3.
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\ No newline at end of file
diff --git a/stable/objects.inv b/stable/objects.inv
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-                <li class="nav-item">
-                    <a class="nav-link" href="help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
-      
+  </ul>
+</nav></div>
+        
       </div>
     
-
     
     
-    <div class="sidebar-header-items__end">
-      
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
-    <a class="theme-switch" data-mode="auto"><i class="fa-solid fa-circle-half-stroke"></i></a>
-</span>
-      </div>
-      
-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+      <div class="sidebar-header-items__end">
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+        
+          <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-3"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
+      data-bs-toggle="dropdown"
+      aria-haspopup="listbox"
+      aria-controls="pst-version-switcher-list-3"
+      aria-label="Version switcher list"
+    >
+      Choose version  <!-- this text may get changed later by javascript -->
+      <span class="caret"></span>
     </button>
-    <div class="version-switcher__menu dropdown-menu list-group-flush py-0">
-    <!-- dropdown will be populated by javascript on page load -->
+    <div id="pst-version-switcher-list-3"
+      class="version-switcher__menu dropdown-menu list-group-flush py-0"
+      role="listbox" aria-labelledby="pst-version-switcher-button-3">
+      <!-- dropdown will be populated by javascript on page load -->
     </div>
-</div>
+  </div>
+`);
+</script></div>
+        
       </div>
-      
-    </div>
     
   </div>
-
   
-
   
-  <div class="sidebar-end-items sidebar-primary__section">
-    <div class="sidebar-end-items__item">
-    </div>
+  <div class="sidebar-primary-items__end sidebar-primary__section">
   </div>
+  
+  <div id="rtd-footer-container"></div>
+
 
       </div>
-      <main class="bd-main">
+      
+      <main id="main-content" class="bd-main">
         
         
-        <div class="bd-content">
-          <div class="bd-article-container">
-            
-            <div class="bd-header-article">
-                
-            </div>
-            
-            
-            <article class="bd-article" role="main">
+          <div class="bd-content">
+            <div class="bd-article-container">
+              
+              <div class="bd-header-article"></div>
               
+              
+<div>
+  
 
    <h1>Python Module Index</h1>
 
@@ -385,67 +441,61 @@ <h1>Python Module Index</h1>
    </table>
 
 
-            </article>
-            
-            
-            
-            <footer class="bd-footer-article">
-                <!-- Previous / next buttons -->
-<div class='prev-next-area'>
-</div>
-            </footer>
-            
-          </div>
-          
-          
-          
-            <div class="bd-sidebar-secondary bd-toc">
-              
-<div class="toc-item">
-  
-<div id="searchbox"></div>
-</div>
-
-<div class="toc-item">
   
 </div>
 
-<div class="toc-item">
-  
+              
+              
+              
+              
+                <footer class="prev-next-footer">
+                  <!-- Previous / next buttons -->
+<div class="prev-next-area">
 </div>
-
+                </footer>
+              
             </div>
-          
-          
-        </div>
-        <footer class="bd-footer-content">
-          <div class="bd-footer-content__inner">
+            
+            
+              
             
           </div>
-        </footer>
+          <footer class="bd-footer-content">
+            
+          </footer>
         
       </main>
     </div>
   </div>
-
   
     
   <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script src="_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536"></script>
+  <script src="_static/scripts/bootstrap.js?digest=365ca57ee442770a23c6"></script>
+<script src="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6"></script>
 
-  <footer class="bd-footer"><div class="bd-footer__inner container">
-  
-  <div class="footer-item">
+    <script src="https://braindecode.org/versionwarning.js"></script>
     
-<p class="copyright">
-
-    &copy; Copyright 2018–2022, Braindecode Developers..<br>
-
-</p>
-
-  </div>
+  <footer class="bd-footer">
+<div class="bd-footer__inner bd-page-width">
+  
+    <div class="footer-items__start">
+      
+        <div class="footer-item"><p class="text-center text-muted small">&copy; Copyright 2018–2023, Braindecode Developers.</p></div>
+      
+    </div>
+  
+  
+  
+    <div class="footer-items__end">
+      
+        <div class="footer-item"><p class="theme-version">
+  Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.14.3.
+</p></div>
+      
+    </div>
   
 </div>
+
   </footer>
   </body>
 </html>
\ No newline at end of file
diff --git a/stable/search.html b/stable/search.html
index ff8d320c..bf3a0074 100644
--- a/stable/search.html
+++ b/stable/search.html
@@ -1,455 +1,509 @@
 
 <!DOCTYPE html>
 
-<html lang="en">
+
+<html lang="en" data-theme="light">
+
   <head>
     <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><title>Search - Braindecode 0.7 documentation</title>
-  <script>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" /><title>Search - Braindecode 0.8 documentation</title>
+  
+  
+  
+  <script data-cfasync="false">
     document.documentElement.dataset.mode = localStorage.getItem("mode") || "light";
     document.documentElement.dataset.theme = localStorage.getItem("theme") || "light";
   </script>
   
   <!-- Loaded before other Sphinx assets -->
-  <link href="_static/styles/theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
-<link href="_static/styles/pydata-sphinx-theme.css?digest=1e1de1a1873e13ef5536" rel="stylesheet">
+  <link href="_static/styles/theme.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="_static/styles/bootstrap.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+<link href="_static/styles/pydata-sphinx-theme.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
 
   
-  <link rel="stylesheet"
-    href="_static/vendor/fontawesome/6.1.2/css/all.min.css">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2">
-  <link rel="preload" as="font" type="font/woff2" crossorigin
-    href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2">
+  <link href="_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=365ca57ee442770a23c6" rel="stylesheet" />
+  <link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" />
+<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" />
 
-    <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
-    <link rel="stylesheet" type="text/css" href="_static/style.css" />
+    <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+    <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
+    <link rel="stylesheet" type="text/css" href="_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
+    <link rel="stylesheet" type="text/css" href="_static/style.css?v=09706775" />
   
   <!-- Pre-loaded scripts that we'll load fully later -->
-  <link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=1e1de1a1873e13ef5536">
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-    <script src="_static/jquery.js"></script>
-    <script src="_static/underscore.js"></script>
-    <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-    <script src="_static/doctools.js"></script>
-    <script src="_static/sphinx_highlight.js"></script>
+  <link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=365ca57ee442770a23c6" />
+<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6" />
+  <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
+
+    <script src="_static/documentation_options.js?v=aad85e6b"></script>
+    <script src="_static/doctools.js?v=888ff710"></script>
+    <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/design-tabs.js?v=36754332"></script>
+    <script async="async" src="https://www.googletagmanager.com/gtag/js?id=G-7Q43R82K6D"></script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
+    <script>
+                window.dataLayer = window.dataLayer || [];
+                function gtag(){ dataLayer.push(arguments); }
+                gtag('js', new Date());
+                gtag('config', 'G-7Q43R82K6D');
+            </script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'search';</script>
     <script>
+        DOCUMENTATION_OPTIONS.theme_version = '0.14.3';
         DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
+        DOCUMENTATION_OPTIONS.show_version_warning_banner = false;
         </script>
-<script src="_static/searchtools.js"></script>
-<script src="_static/language_data.js"></script>
-<script src="searchindex.js"></script>
+  <script src="_static/searchtools.js"></script>
+  <script src="_static/language_data.js"></script>
+  <script src="searchindex.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="#" />
-  <meta name="viewport" content="width=device-width, initial-scale=1" />
-  <meta name="docsearch:language" content="en">
+    <link rel="canonical" href="braindecode.org/index.html" />
+    
+  <meta name="viewport" content="width=device-width, initial-scale=1"/>
+  <meta name="docsearch:language" content="en"/>
+
   </head>
   
   
-  <body data-spy="scroll" data-target="#bd-toc-nav" data-offset="180" data-default-mode="light">
+  <body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="light">
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__primary" id="__primary">
-  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <a class="skip-link" href="#main-content">Skip to main content</a>
+  
+  <div id="pst-scroll-pixel-helper"></div>
 
   
-  <input type="checkbox" class="sidebar-toggle" name="__secondary" id="__secondary">
-  <label class="overlay overlay-secondary" for="__secondary"></label>
+  <button type="button" class="btn rounded-pill" id="pst-back-to-top">
+    <i class="fa-solid fa-arrow-up"></i>
+    Back to top
+  </button>
 
   
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__primary"
+          id="__primary"/>
+  <label class="overlay overlay-primary" for="__primary"></label>
+  
+  <input type="checkbox"
+          class="sidebar-toggle"
+          name="__secondary"
+          id="__secondary"/>
+  <label class="overlay overlay-secondary" for="__secondary"></label>
+  
   <div class="search-button__wrapper">
     <div class="search-button__overlay"></div>
     <div class="search-button__search-container">
-      
-<form class="bd-search d-flex align-items-center" action="#" method="get">
+<form class="bd-search d-flex align-items-center"
+      action="#"
+      method="get">
   <i class="fa-solid fa-magnifying-glass"></i>
-  <input type="search" class="form-control" name="q" id="search-input" placeholder="Search the docs ..." aria-label="Search the docs ..." autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false">
+  <input type="search"
+         class="form-control"
+         name="q"
+         id="search-input"
+         placeholder="Search the docs ..."
+         aria-label="Search the docs ..."
+         autocomplete="off"
+         autocorrect="off"
+         autocapitalize="off"
+         spellcheck="false"/>
   <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
-</form>
-    </div>
+</form></div>
   </div>
-
   
-  <nav class="bd-header navbar navbar-expand-lg bd-navbar" id="navbar-main"><div class="bd-header__inner bd-page-width">
+    <nav class="bd-header navbar navbar-expand-lg bd-navbar">
+<div class="bd-header__inner bd-page-width">
   <label class="sidebar-toggle primary-toggle" for="__primary">
-      <span class="fa-solid fa-bars"></span>
+    <span class="fa-solid fa-bars"></span>
   </label>
-  <div id="navbar-start">
-    
+  
+  
+  <div class="col-lg-3 navbar-header-items__start">
     
+      <div class="navbar-item">
   
 
-
 <a class="navbar-brand logo" href="index.html">
-
-  
-  
   
   
   
   
   
-
-  
-    <img src="_static/braindecode_small.svg" class="logo__image only-light" alt="Logo image">
-    <img src="_static/braindecode_small.svg" class="logo__image only-dark" alt="Logo image">
+    
+    
+    
+    <img src="_static/braindecode_symbol.png" class="logo__image only-light" alt="Braindecode Logo"/>
+    <script>document.write(`<img src="_static/braindecode_symbol.png" class="logo__image only-dark" alt="Braindecode Logo"/>`);</script>
   
   
-</a>
+</a></div>
     
   </div>
-
   
   <div class="col-lg-9 navbar-header-items">
-    <div id="navbar-center" class="mr-auto">
+    
+    <div class="me-auto navbar-header-items__center">
       
-      <div class="navbar-center-item">
-        <nav class="navbar-nav">
-    <p class="sidebar-header-items__title" role="heading" aria-level="1" aria-label="Site Navigation">
-        Site Navigation
-    </p>
-    <ul id="navbar-main-elements" class="navbar-nav">
-        
-                <li class="nav-item">
-                    <a class="nav-link" href="install.html">
+        <div class="navbar-item"><nav class="navbar-nav">
+  <p class="sidebar-header-items__title"
+     role="heading"
+     aria-level="1"
+     aria-label="Site Navigation">
+    Site Navigation
+  </p>
+  <ul class="bd-navbar-elements navbar-nav">
+    
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="install/install.html">
                         Install
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="starting.html">
-                        Get Started
-                    </a>
-                </li>
-                
-
-                <li class="nav-item">
-                    <a class="nav-link" href="cite.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="cite.html">
                         Cite
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="auto_examples/index.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="auto_examples/index.html">
                         Tutorial and Examples
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="api.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="api.html">
                         API Reference
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-            <div class="nav-item dropdown">
-                <button class="btn dropdown-toggle nav-item" type="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
-                    More
-                </button>
-                <div class="dropdown-menu">
-                    
-                <li class="nav-item">
-                    <a class="nav-link" href="help.html">
+
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="help.html">
                         Get help
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
 
-                <li class="nav-item">
-                    <a class="nav-link" href="whats_new.html">
+                    <li class="nav-item">
+                      <a class="nav-link nav-internal" href="whats_new.html">
                         What’s new
-                    </a>
-                </li>
+                      </a>
+                    </li>
                 
-                </div>
-            </div>
-            
-    </ul>
-</nav>
-      </div>
+  </ul>
+</nav></div>
       
     </div>
-
-    <div id="navbar-end">
-      <div class="navbar-end-item navbar-end__search-button-container">
-        
-<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" data-toggle="tooltip">
-  <i class="fa-solid fa-magnifying-glass"></i>
-</button>
-      </div>
+    
+    
+    <div class="navbar-header-items__end">
+      
+        <div class="navbar-item navbar-persistent--container">
+          
+ <script>
+ document.write(`
+   <button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <i class="fa-solid fa-magnifying-glass"></i>
+    <span class="search-button__default-text">Search</span>
+    <span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
+   </button>
+ `);
+ </script>
+        </div>
       
-      <div class="navbar-end-item">
-        <span class="theme-switch-button btn btn-sm btn-outline-primary navbar-btn rounded-circle" title="light/dark" data-toggle="tooltip">
-    <a class="theme-switch" data-mode="light"><i class="fa-solid fa-sun"></i></a>
-    <a class="theme-switch" data-mode="dark"><i class="fa-regular fa-moon"></i></a>
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-</span>
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-      <div class="navbar-end-item">
-        <div class="version-switcher__container dropdown">
-    <button type="button" class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle" data-toggle="dropdown">
-        0.7  <!-- this text may get changed later by javascript -->
-        <span class="caret"></span>
+        <div class="navbar-item">
+<script>
+document.write(`
+  <button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
+    <span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
+    <span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
+  </button>
+`);
+</script></div>
+      
+        <div class="navbar-item">
+<script>
+document.write(`
+  <div class="version-switcher__container dropdown">
+    <button id="pst-version-switcher-button-2"
+      type="button"
+      class="version-switcher__button btn btn-sm navbar-btn dropdown-toggle"
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diff --git a/stable/searchindex.js b/stable/searchindex.js
index 9be60147..09477f0c 100644
--- a/stable/searchindex.js
+++ b/stable/searchindex.js
@@ -1 +1 @@
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"generated/braindecode.augmentation.functional.sign_flip.rst", "generated/braindecode.augmentation.functional.smooth_time_mask.rst", "generated/braindecode.augmentation.functional.time_reverse.rst", "generated/braindecode.classifier.EEGClassifier.rst", "generated/braindecode.datasets.BCICompetitionIVDataset4.rst", "generated/braindecode.datasets.BNCI2014001.rst", "generated/braindecode.datasets.BaseConcatDataset.rst", "generated/braindecode.datasets.BaseDataset.rst", "generated/braindecode.datasets.HGD.rst", "generated/braindecode.datasets.MOABBDataset.rst", "generated/braindecode.datasets.SleepPhysionet.rst", "generated/braindecode.datasets.TUH.rst", "generated/braindecode.datasets.TUHAbnormal.rst", "generated/braindecode.datasets.WindowsDataset.rst", "generated/braindecode.datasets.create_from_X_y.rst", "generated/braindecode.datasets.create_from_mne_epochs.rst", "generated/braindecode.datasets.create_from_mne_raw.rst", "generated/braindecode.datautil.load_concat_dataset.rst", "generated/braindecode.datautil.save_concat_dataset.rst", "generated/braindecode.models.Deep4Net.rst", "generated/braindecode.models.EEGITNet.rst", "generated/braindecode.models.EEGInception.rst", "generated/braindecode.models.EEGNetv1.rst", "generated/braindecode.models.EEGNetv4.rst", "generated/braindecode.models.EEGResNet.rst", "generated/braindecode.models.HybridNet.rst", "generated/braindecode.models.ShallowFBCSPNet.rst", "generated/braindecode.models.SleepStagerBlanco2020.rst", "generated/braindecode.models.SleepStagerChambon2018.rst", "generated/braindecode.models.SleepStagerEldele2021.rst", "generated/braindecode.models.TCN.rst", "generated/braindecode.models.TIDNet.rst", "generated/braindecode.models.TimeDistributed.rst", "generated/braindecode.models.USleep.rst", "generated/braindecode.models.get_output_shape.rst", "generated/braindecode.preprocessing.Preprocessor.rst", "generated/braindecode.preprocessing.create_fixed_length_windows.rst", "generated/braindecode.preprocessing.create_windows_from_events.rst", "generated/braindecode.preprocessing.create_windows_from_target_channels.rst", "generated/braindecode.preprocessing.exponential_moving_demean.rst", "generated/braindecode.preprocessing.exponential_moving_standardize.rst", "generated/braindecode.preprocessing.filterbank.rst", "generated/braindecode.preprocessing.preprocess.rst", "generated/braindecode.preprocessing.scale.rst", "generated/braindecode.regressor.EEGRegressor.rst", "generated/braindecode.samplers.BalancedSequenceSampler.rst", "generated/braindecode.samplers.RecordingSampler.rst", "generated/braindecode.samplers.RelativePositioningSampler.rst", "generated/braindecode.samplers.SequenceSampler.rst", "generated/braindecode.training.CroppedLoss.rst", "generated/braindecode.training.CroppedTimeSeriesEpochScoring.rst", "generated/braindecode.training.CroppedTrialEpochScoring.rst", "generated/braindecode.training.PostEpochTrainScoring.rst", "generated/braindecode.training.TimeSeriesLoss.rst", "generated/braindecode.training.mixup_criterion.rst", "generated/braindecode.training.predict_trials.rst", "generated/braindecode.training.trial_preds_from_window_preds.rst", "generated/braindecode.util.set_random_seeds.rst", "generated/braindecode.visualization.compute_amplitude_gradients.rst", "generated/braindecode.visualization.plot_confusion_matrix.rst", "help.rst", "index.rst", "install.rst", "sg_api_usage.rst", "starting.rst", "whats_new.rst"], "titles": ["API Reference", "Benchmarking eager and lazy loading", "Examples", "Cropped Decoding on BCIC IV 2a Dataset", "Trialwise Decoding on BCIC IV 2a Dataset", "Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset", "Fingers flexion decoding on BCIC IV 4 ECoG Dataset", "Benchmarking preprocessing with parallelization and serialization", "Custom Dataset Example", "Data Augmentation on BCIC IV 2a Dataset", "Searching the best data augmentation on BCIC IV 2a Dataset", "MOABB Dataset Example", "How to train, test and tune your model", "Hyperparameter tuning with scikit-learn", "Load and save dataset example", "MNE Dataset Example", "Regression example on fake data", "Self-supervised learning on EEG with relative positioning", "Sleep staging on the Sleep Physionet dataset using Chambon2018 network", "Sleep staging on the Sleep Physionet dataset using Eldele2021", "Sleep staging on the Sleep Physionet dataset using U-Sleep network", "Split Dataset Example", "Multiple discrete targets with the TUH EEG Corpus", "Process a big data EEG resource (TUH EEG Corpus)", "Computation times", "How to cite Braindecode", "braindecode.augmentation.AugmentedDataLoader", "braindecode.augmentation.BandstopFilter", "braindecode.augmentation.ChannelsDropout", "braindecode.augmentation.ChannelsShuffle", "braindecode.augmentation.ChannelsSymmetry", "braindecode.augmentation.Compose", "braindecode.augmentation.FTSurrogate", "braindecode.augmentation.FrequencyShift", "braindecode.augmentation.GaussianNoise", "braindecode.augmentation.IdentityTransform", "braindecode.augmentation.Mixup", "braindecode.augmentation.SensorsRotation", "braindecode.augmentation.SensorsXRotation", "braindecode.augmentation.SensorsYRotation", "braindecode.augmentation.SensorsZRotation", "braindecode.augmentation.SignFlip", "braindecode.augmentation.SmoothTimeMask", "braindecode.augmentation.TimeReverse", "braindecode.augmentation.Transform", "braindecode.augmentation.functional.bandstop_filter", "braindecode.augmentation.functional.channels_dropout", "braindecode.augmentation.functional.channels_permute", "braindecode.augmentation.functional.channels_shuffle", "braindecode.augmentation.functional.frequency_shift", "braindecode.augmentation.functional.ft_surrogate", "braindecode.augmentation.functional.gaussian_noise", "braindecode.augmentation.functional.identity", "braindecode.augmentation.functional.mixup", "braindecode.augmentation.functional.sensors_rotation", "braindecode.augmentation.functional.sign_flip", "braindecode.augmentation.functional.smooth_time_mask", "braindecode.augmentation.functional.time_reverse", "braindecode.classifier.EEGClassifier", "braindecode.datasets.BCICompetitionIVDataset4", "braindecode.datasets.BNCI2014001", "braindecode.datasets.BaseConcatDataset", "braindecode.datasets.BaseDataset", "braindecode.datasets.HGD", "braindecode.datasets.MOABBDataset", "braindecode.datasets.SleepPhysionet", "braindecode.datasets.TUH", "braindecode.datasets.TUHAbnormal", "braindecode.datasets.WindowsDataset", "braindecode.datasets.create_from_X_y", "braindecode.datasets.create_from_mne_epochs", "braindecode.datasets.create_from_mne_raw", "braindecode.datautil.load_concat_dataset", "braindecode.datautil.save_concat_dataset", "braindecode.models.Deep4Net", "braindecode.models.EEGITNet", "braindecode.models.EEGInception", "braindecode.models.EEGNetv1", "braindecode.models.EEGNetv4", "braindecode.models.EEGResNet", "braindecode.models.HybridNet", "braindecode.models.ShallowFBCSPNet", "braindecode.models.SleepStagerBlanco2020", "braindecode.models.SleepStagerChambon2018", "braindecode.models.SleepStagerEldele2021", "braindecode.models.TCN", "braindecode.models.TIDNet", "braindecode.models.TimeDistributed", "braindecode.models.USleep", "braindecode.models.get_output_shape", "braindecode.preprocessing.Preprocessor", "braindecode.preprocessing.create_fixed_length_windows", "braindecode.preprocessing.create_windows_from_events", "braindecode.preprocessing.create_windows_from_target_channels", "braindecode.preprocessing.exponential_moving_demean", "braindecode.preprocessing.exponential_moving_standardize", "braindecode.preprocessing.filterbank", "braindecode.preprocessing.preprocess", "braindecode.preprocessing.scale", "braindecode.regressor.EEGRegressor", "braindecode.samplers.BalancedSequenceSampler", "braindecode.samplers.RecordingSampler", "braindecode.samplers.RelativePositioningSampler", "braindecode.samplers.SequenceSampler", "braindecode.training.CroppedLoss", "braindecode.training.CroppedTimeSeriesEpochScoring", "braindecode.training.CroppedTrialEpochScoring", "braindecode.training.PostEpochTrainScoring", "braindecode.training.TimeSeriesLoss", "braindecode.training.mixup_criterion", "braindecode.training.predict_trials", "braindecode.training.trial_preds_from_window_preds", "braindecode.util.set_random_seeds", "braindecode.visualization.compute_amplitude_gradients", "braindecode.visualization.plot_confusion_matrix", "Frequently Asked Questions (FAQ)", "Braindecode", "Install via <code class=\"code docutils literal notranslate\"><span class=\"pre\">pip</span></code> or <code class=\"code docutils literal notranslate\"><span class=\"pre\">conda</span></code>", "Unused API Entries", "Getting starting", "What\u2019s new"], "terms": {"thi": [0, 1, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 38, 39, 40, 58, 59, 60, 61, 63, 65, 66, 67, 71, 75, 76, 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"braindecode-augmentation-functional-ft-surrogate"]], "braindecode.augmentation.functional.gaussian_noise": [[51, "braindecode-augmentation-functional-gaussian-noise"]], "braindecode.augmentation.functional.identity": [[52, "braindecode-augmentation-functional-identity"]], "braindecode.augmentation.functional.mixup": [[53, "braindecode-augmentation-functional-mixup"]], "braindecode.augmentation.functional.sensors_rotation": [[54, "braindecode-augmentation-functional-sensors-rotation"]], "braindecode.augmentation.functional.sign_flip": [[55, "braindecode-augmentation-functional-sign-flip"]], "braindecode.augmentation.functional.smooth_time_mask": [[56, "braindecode-augmentation-functional-smooth-time-mask"]], "braindecode.augmentation.functional.time_reverse": [[57, "braindecode-augmentation-functional-time-reverse"]], "braindecode.classifier.EEGClassifier": [[58, "braindecode-classifier-eegclassifier"]], "braindecode.datasets.BCICompetitionIVDataset4": [[59, "braindecode-datasets-bcicompetitionivdataset4"]], "Examples using braindecode.datasets.BCICompetitionIVDataset4": [[59, "examples-using-braindecode-datasets-bcicompetitionivdataset4"]], "braindecode.datasets.BNCI2014001": [[60, "braindecode-datasets-bnci2014001"]], "braindecode.datasets.BaseConcatDataset": [[61, "braindecode-datasets-baseconcatdataset"]], "Examples using braindecode.datasets.BaseConcatDataset": [[61, "examples-using-braindecode-datasets-baseconcatdataset"]], "braindecode.datasets.BaseDataset": [[62, "braindecode-datasets-basedataset"]], "Examples using braindecode.datasets.BaseDataset": [[62, "examples-using-braindecode-datasets-basedataset"]], "braindecode.datasets.HGD": [[63, "braindecode-datasets-hgd"]], "braindecode.datasets.MOABBDataset": [[64, "braindecode-datasets-moabbdataset"]], "Examples using braindecode.datasets.MOABBDataset": [[64, "examples-using-braindecode-datasets-moabbdataset"]], "braindecode.datasets.SleepPhysionet": [[65, 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"braindecode.models.EEGNetv4": [[78, "braindecode-models-eegnetv4"]], "braindecode.models.EEGResNet": [[79, "braindecode-models-eegresnet"]], "braindecode.models.HybridNet": [[80, "braindecode-models-hybridnet"]], "braindecode.models.ShallowFBCSPNet": [[81, "braindecode-models-shallowfbcspnet"]], "Examples using braindecode.models.ShallowFBCSPNet": [[81, "examples-using-braindecode-models-shallowfbcspnet"]], "braindecode.models.SleepStagerBlanco2020": [[82, "braindecode-models-sleepstagerblanco2020"]], "braindecode.models.SleepStagerChambon2018": [[83, "braindecode-models-sleepstagerchambon2018"]], "Examples using braindecode.models.SleepStagerChambon2018": [[83, "examples-using-braindecode-models-sleepstagerchambon2018"]], "braindecode.models.SleepStagerEldele2021": [[84, "braindecode-models-sleepstagereldele2021"]], "Examples using braindecode.models.SleepStagerEldele2021": [[84, "examples-using-braindecode-models-sleepstagereldele2021"]], "braindecode.models.TCN": [[85, 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"Examples using braindecode.preprocessing.create_fixed_length_windows": [[91, "examples-using-braindecode-preprocessing-create-fixed-length-windows"]], "braindecode.preprocessing.create_windows_from_events": [[92, "braindecode-preprocessing-create-windows-from-events"]], "Examples using braindecode.preprocessing.create_windows_from_events": [[92, "examples-using-braindecode-preprocessing-create-windows-from-events"]], "braindecode.preprocessing.create_windows_from_target_channels": [[93, "braindecode-preprocessing-create-windows-from-target-channels"]], "Examples using braindecode.preprocessing.create_windows_from_target_channels": [[93, "examples-using-braindecode-preprocessing-create-windows-from-target-channels"]], "braindecode.preprocessing.exponential_moving_demean": [[94, "braindecode-preprocessing-exponential-moving-demean"]], "braindecode.preprocessing.exponential_moving_standardize": [[95, "braindecode-preprocessing-exponential-moving-standardize"]], "Examples using braindecode.preprocessing.exponential_moving_standardize": [[95, "examples-using-braindecode-preprocessing-exponential-moving-standardize"]], "braindecode.preprocessing.filterbank": [[96, "braindecode-preprocessing-filterbank"]], "braindecode.preprocessing.preprocess": [[97, "braindecode-preprocessing-preprocess"]], "Examples using braindecode.preprocessing.preprocess": [[97, "examples-using-braindecode-preprocessing-preprocess"]], "braindecode.preprocessing.scale": [[98, "braindecode-preprocessing-scale"]], "Examples using braindecode.preprocessing.scale": [[98, "examples-using-braindecode-preprocessing-scale"]], "braindecode.regressor.EEGRegressor": [[99, "braindecode-regressor-eegregressor"]], "braindecode.samplers.BalancedSequenceSampler": [[100, "braindecode-samplers-balancedsequencesampler"]], "braindecode.samplers.RecordingSampler": [[101, "braindecode-samplers-recordingsampler"]], "Examples using braindecode.samplers.RecordingSampler": [[101, 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"braindecode.training.CroppedTrialEpochScoring": [[106, "braindecode-training-croppedtrialepochscoring"]], "Examples using braindecode.training.CroppedTrialEpochScoring": [[106, "examples-using-braindecode-training-croppedtrialepochscoring"]], "braindecode.training.PostEpochTrainScoring": [[107, "braindecode-training-postepochtrainscoring"]], "braindecode.training.TimeSeriesLoss": [[108, "braindecode-training-timeseriesloss"]], "Examples using braindecode.training.TimeSeriesLoss": [[108, "examples-using-braindecode-training-timeseriesloss"]], "braindecode.training.mixup_criterion": [[109, "braindecode-training-mixup-criterion"]], "braindecode.training.predict_trials": [[110, "braindecode-training-predict-trials"]], "braindecode.training.trial_preds_from_window_preds": [[111, "braindecode-training-trial-preds-from-window-preds"]], "braindecode.util.set_random_seeds": [[112, "braindecode-util-set-random-seeds"]], "Examples using braindecode.util.set_random_seeds": [[112, "examples-using-braindecode-util-set-random-seeds"]], "braindecode.visualization.compute_amplitude_gradients": [[113, "braindecode-visualization-compute-amplitude-gradients"]], "braindecode.visualization.plot_confusion_matrix": [[114, "braindecode-visualization-plot-confusion-matrix"]], "Examples using braindecode.visualization.plot_confusion_matrix": [[114, "examples-using-braindecode-visualization-plot-confusion-matrix"]], "Frequently Asked Questions (FAQ)": [[115, "frequently-asked-questions-faq"]], "How do I cite Braindecode?": [[115, "how-do-i-cite-braindecode"]], "Help! I can\u2019t get Python and Braindecode working!": [[115, "help-i-can-t-get-python-and-braindecode-working"]], "I still can\u2019t get it to work!": [[115, "i-still-can-t-get-it-to-work"]], "I think I found a bug, what do I do?": [[115, "i-think-i-found-a-bug-what-do-i-do"]], "Braindecode Homepage": [[116, "braindecode-homepage"]], "Indices and tables": [[116, "indices-and-tables"]], "Install via pip or conda": [[117, "install-via-pip-or-conda"]], "Preparing the environment for braindecode installation": [[117, "preparing-the-environment-for-braindecode-installation"]], "Installing the braindecode with pip": [[117, "installing-the-braindecode-with-pip"]], "Installing Braindecode with conda": [[117, "installing-braindecode-with-conda"]], "Unused API Entries": [[118, "unused-api-entries"]], "Getting starting": [[119, "getting-starting"]], "What\u2019s new": [[120, "what-s-new"]], "Current 0.8 (dev0)": [[120, "current-0-8-dev0"]], "Enhancements": [[120, "enhancements"], [120, "id1"], [120, "id4"], [120, "id7"]], "Bugs": [[120, "bugs"], [120, "id2"], [120, "id5"], [120, "id8"]], "API changes": [[120, "api-changes"], [120, "id3"], [120, "id6"], [120, "id9"]], "Current 0.7 (10-2022)": [[120, "current-0-7-10-2022"]], "Version 0.6 (2021-12-06)": [[120, "version-0-6-2021-12-06"]], "Version 0.5.1 (2021-07-14)": [[120, "version-0-5-1-2021-07-14"]], "Authors": [[120, "authors"]]}, "indexentries": {"braindecode": [[0, "module-braindecode"]], "module": [[0, "module-braindecode"]], "augmenteddataloader (class in braindecode.augmentation)": [[26, "braindecode.augmentation.AugmentedDataLoader"]], "bandstopfilter (class in braindecode.augmentation)": [[27, "braindecode.augmentation.BandstopFilter"]], "get_augmentation_params() (braindecode.augmentation.bandstopfilter method)": [[27, "braindecode.augmentation.BandstopFilter.get_augmentation_params"]], "operation() (braindecode.augmentation.bandstopfilter static method)": [[27, "braindecode.augmentation.BandstopFilter.operation"]], "channelsdropout (class in braindecode.augmentation)": [[28, "braindecode.augmentation.ChannelsDropout"]], "get_augmentation_params() (braindecode.augmentation.channelsdropout method)": [[28, "braindecode.augmentation.ChannelsDropout.get_augmentation_params"]], "operation() (braindecode.augmentation.channelsdropout static method)": [[28, "braindecode.augmentation.ChannelsDropout.operation"]], "channelsshuffle (class in braindecode.augmentation)": [[29, "braindecode.augmentation.ChannelsShuffle"]], "get_augmentation_params() (braindecode.augmentation.channelsshuffle method)": [[29, "braindecode.augmentation.ChannelsShuffle.get_augmentation_params"]], "operation() (braindecode.augmentation.channelsshuffle static method)": [[29, "braindecode.augmentation.ChannelsShuffle.operation"]], "channelssymmetry (class in braindecode.augmentation)": [[30, "braindecode.augmentation.ChannelsSymmetry"]], "get_augmentation_params() (braindecode.augmentation.channelssymmetry method)": [[30, "braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"]], "operation() (braindecode.augmentation.channelssymmetry static method)": [[30, "braindecode.augmentation.ChannelsSymmetry.operation"]], "compose (class in braindecode.augmentation)": [[31, "braindecode.augmentation.Compose"]], "forward() (braindecode.augmentation.compose method)": [[31, "braindecode.augmentation.Compose.forward"]], "ftsurrogate (class in braindecode.augmentation)": [[32, "braindecode.augmentation.FTSurrogate"]], "get_augmentation_params() (braindecode.augmentation.ftsurrogate method)": [[32, "braindecode.augmentation.FTSurrogate.get_augmentation_params"]], "operation() (braindecode.augmentation.ftsurrogate static method)": [[32, "braindecode.augmentation.FTSurrogate.operation"]], "frequencyshift (class in braindecode.augmentation)": [[33, "braindecode.augmentation.FrequencyShift"]], "get_augmentation_params() (braindecode.augmentation.frequencyshift method)": [[33, "braindecode.augmentation.FrequencyShift.get_augmentation_params"]], "operation() (braindecode.augmentation.frequencyshift static method)": [[33, "braindecode.augmentation.FrequencyShift.operation"]], "gaussiannoise (class in braindecode.augmentation)": [[34, "braindecode.augmentation.GaussianNoise"]], "get_augmentation_params() (braindecode.augmentation.gaussiannoise method)": [[34, "braindecode.augmentation.GaussianNoise.get_augmentation_params"]], "operation() (braindecode.augmentation.gaussiannoise static method)": [[34, "braindecode.augmentation.GaussianNoise.operation"]], "identitytransform (class in braindecode.augmentation)": [[35, "braindecode.augmentation.IdentityTransform"]], "operation() (braindecode.augmentation.identitytransform static method)": [[35, "braindecode.augmentation.IdentityTransform.operation"]], "mixup (class in braindecode.augmentation)": [[36, "braindecode.augmentation.Mixup"]], "get_augmentation_params() (braindecode.augmentation.mixup method)": [[36, "braindecode.augmentation.Mixup.get_augmentation_params"]], "operation() (braindecode.augmentation.mixup static method)": [[36, "braindecode.augmentation.Mixup.operation"]], "sensorsrotation (class in braindecode.augmentation)": [[37, "braindecode.augmentation.SensorsRotation"]], "get_augmentation_params() (braindecode.augmentation.sensorsrotation method)": [[37, "braindecode.augmentation.SensorsRotation.get_augmentation_params"]], "operation() (braindecode.augmentation.sensorsrotation static method)": [[37, "braindecode.augmentation.SensorsRotation.operation"]], "sensorsxrotation (class in braindecode.augmentation)": [[38, "braindecode.augmentation.SensorsXRotation"]], "sensorsyrotation (class in braindecode.augmentation)": [[39, "braindecode.augmentation.SensorsYRotation"]], "sensorszrotation (class in braindecode.augmentation)": [[40, "braindecode.augmentation.SensorsZRotation"]], "signflip (class in braindecode.augmentation)": [[41, "braindecode.augmentation.SignFlip"]], "operation() (braindecode.augmentation.signflip static method)": [[41, "braindecode.augmentation.SignFlip.operation"]], "smoothtimemask (class in braindecode.augmentation)": [[42, "braindecode.augmentation.SmoothTimeMask"]], "get_augmentation_params() (braindecode.augmentation.smoothtimemask method)": [[42, "braindecode.augmentation.SmoothTimeMask.get_augmentation_params"]], "operation() (braindecode.augmentation.smoothtimemask static method)": [[42, "braindecode.augmentation.SmoothTimeMask.operation"]], "timereverse (class in braindecode.augmentation)": [[43, "braindecode.augmentation.TimeReverse"]], "operation() (braindecode.augmentation.timereverse static method)": [[43, "braindecode.augmentation.TimeReverse.operation"]], "transform (class in braindecode.augmentation)": [[44, "braindecode.augmentation.Transform"]], "forward() (braindecode.augmentation.transform method)": [[44, "braindecode.augmentation.Transform.forward"]], "get_augmentation_params() (braindecode.augmentation.transform method)": [[44, "braindecode.augmentation.Transform.get_augmentation_params"]], "bandstop_filter() (in module braindecode.augmentation.functional)": [[45, "braindecode.augmentation.functional.bandstop_filter"]], "channels_dropout() (in module braindecode.augmentation.functional)": [[46, "braindecode.augmentation.functional.channels_dropout"]], "channels_permute() (in module braindecode.augmentation.functional)": [[47, "braindecode.augmentation.functional.channels_permute"]], "channels_shuffle() (in module braindecode.augmentation.functional)": [[48, "braindecode.augmentation.functional.channels_shuffle"]], "frequency_shift() (in module braindecode.augmentation.functional)": [[49, "braindecode.augmentation.functional.frequency_shift"]], "ft_surrogate() (in module braindecode.augmentation.functional)": [[50, "braindecode.augmentation.functional.ft_surrogate"]], "gaussian_noise() (in module braindecode.augmentation.functional)": [[51, "braindecode.augmentation.functional.gaussian_noise"]], "identity() (in module braindecode.augmentation.functional)": [[52, "braindecode.augmentation.functional.identity"]], "mixup() (in module braindecode.augmentation.functional)": [[53, "braindecode.augmentation.functional.mixup"]], "sensors_rotation() (in module braindecode.augmentation.functional)": [[54, "braindecode.augmentation.functional.sensors_rotation"]], "sign_flip() (in module braindecode.augmentation.functional)": [[55, "braindecode.augmentation.functional.sign_flip"]], "smooth_time_mask() (in module braindecode.augmentation.functional)": [[56, "braindecode.augmentation.functional.smooth_time_mask"]], "time_reverse() (in module braindecode.augmentation.functional)": [[57, "braindecode.augmentation.functional.time_reverse"]], "eegclassifier (class in braindecode.classifier)": [[58, "braindecode.classifier.EEGClassifier"]], "_criteria (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier._criteria"]], "_modules (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier._modules"]], "_optimizers (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier._optimizers"]], "callbacks_ (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier.callbacks_"]], "classes_ (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier.classes_"]], "criterion_ (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier.criterion_"]], "cuda_dependent_attributes_ (braindecode.classifier.eegclassifier attribute)": [[58, "braindecode.classifier.EEGClassifier.cuda_dependent_attributes_"]], "get_iterator() (braindecode.classifier.eegclassifier method)": [[58, "braindecode.classifier.EEGClassifier.get_iterator"]], "get_loss() (braindecode.classifier.eegclassifier method)": [[58, 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"generated/braindecode.training.predict_trials.rst", "generated/braindecode.training.trial_preds_from_window_preds.rst", "generated/braindecode.util.set_random_seeds.rst", "generated/braindecode.visualization.compute_amplitude_gradients.rst", "generated/braindecode.visualization.plot_confusion_matrix.rst", "help.rst", "index.rst", "install/install.rst", "install/install_pip.rst", "install/install_source.rst", "whats_new.rst"], "titles": ["API Reference", "Advanced neural network training strategies", "Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset", "Data Augmentation on BCIC IV 2a Dataset", "Searching the best data augmentation on BCIC IV 2a Dataset", "Self-supervised learning on EEG with relative positioning", "Computation times", "Applied examples on real-world datasets", "Fingers flexion decoding on BCIC IV 4 ECoG Dataset", "Sleep staging on the Sleep Physionet dataset using Chambon2018 network", "Sleep staging on the Sleep Physionet dataset using Eldele2021", "Sleep staging on the Sleep Physionet dataset using U-Sleep network", "Process a big data EEG resource (TUH EEG Corpus)", "Computation times", "Benchmarking eager and lazy loading", "Loading and organizing data", "Benchmarking preprocessing with parallelization and serialization", "Custom Dataset Example", "Load and save dataset example", "MNE Dataset Example", "MOABB Dataset Example", "Split Dataset Example", "Multiple discrete targets with the TUH EEG Corpus", "Computation times", "Examples", "Basic model building and training", "Simple training on MNE epochs", "Cropped Decoding on BCIC IV 2a Dataset", "Basic Brain Decoding on EEG Data", "How to train, test and tune your model?", "Hyperparameter tuning with scikit-learn", "Convolutional neural network regression model on fake data.", "Training a Braindecode model in PyTorch", "Computation times", "How to cite Braindecode", "braindecode.augmentation.AugmentedDataLoader", "braindecode.augmentation.BandstopFilter", 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"braindecode.preprocessing.Resample", "braindecode.preprocessing.SetEEGReference", "braindecode.preprocessing.create_fixed_length_windows", "braindecode.preprocessing.create_windows_from_events", "braindecode.preprocessing.create_windows_from_target_channels", "braindecode.preprocessing.exponential_moving_demean", "braindecode.preprocessing.exponential_moving_standardize", "braindecode.preprocessing.filterbank", "braindecode.preprocessing.preprocess", "braindecode.regressor.EEGRegressor", "braindecode.samplers.BalancedSequenceSampler", "braindecode.samplers.RecordingSampler", "braindecode.samplers.RelativePositioningSampler", "braindecode.samplers.SequenceSampler", "braindecode.training.CroppedLoss", "braindecode.training.CroppedTimeSeriesEpochScoring", "braindecode.training.CroppedTrialEpochScoring", "braindecode.training.PostEpochTrainScoring", "braindecode.training.TimeSeriesLoss", "braindecode.training.mixup_criterion", "braindecode.training.predict_trials", 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"self-supervised-learning-on-eeg-with-relative-positioning"]], "Self-supervised learning": [[5, null]], "Loading the raw recordings": [[5, "loading-the-raw-recordings"]], "Preprocessing windows": [[5, "preprocessing-windows"]], "Splitting dataset into train, valid and test sets": [[5, "splitting-dataset-into-train-valid-and-test-sets"]], "Creating samplers": [[5, "creating-samplers"]], "Creating the model": [[5, "creating-the-model"]], "Visualizing the results": [[5, "visualizing-the-results"]], "Inspecting pretext task performance": [[5, "inspecting-pretext-task-performance"]], "Using the learned representation for sleep staging": [[5, "using-the-learned-representation-for-sleep-staging"]], "Conclusion": [[5, "conclusion"]], "Computation times": [[6, "computation-times"], [13, "computation-times"], [23, "computation-times"], [33, "computation-times"]], "Applied examples on real-world datasets": [[7, "applied-examples-on-real-world-datasets"], [24, "applied-examples-on-real-world-datasets"]], "Fingers flexion decoding on BCIC IV 4 ECoG Dataset": [[8, "fingers-flexion-decoding-on-bcic-iv-4-ecog-dataset"]], "Split dataset into train, valid, and test": [[8, "split-dataset-into-train-valid-and-test"]], "Sleep staging on the Sleep Physionet dataset using Chambon2018 network": [[9, "sleep-staging-on-the-sleep-physionet-dataset-using-chambon2018-network"]], "Extract windows": [[9, "extract-windows"], [10, "extract-windows"], [11, "extract-windows"]], "Window preprocessing": [[9, "window-preprocessing"], [10, "window-preprocessing"]], "Create sequence samplers": [[9, "create-sequence-samplers"], [10, "create-sequence-samplers"], [11, "create-sequence-samplers"]], "Sleep staging on the Sleep Physionet dataset using Eldele2021": [[10, "sleep-staging-on-the-sleep-physionet-dataset-using-eldele2021"]], "Sleep staging on the Sleep Physionet dataset using U-Sleep network": [[11, "sleep-staging-on-the-sleep-physionet-dataset-using-u-sleep-network"]], "Process a big data EEG resource (TUH EEG Corpus)": [[12, "process-a-big-data-eeg-resource-tuh-eeg-corpus"]], "Creating the dataset using TUHMock": [[12, "creating-the-dataset-using-tuhmock"]], "Selecting recordings": [[12, "selecting-recordings"]], "Combining preprocessing steps": [[12, "combining-preprocessing-steps"]], "Benchmarking eager and lazy loading": [[14, "benchmarking-eager-and-lazy-loading"]], "Loading and organizing data": [[15, "loading-and-organizing-data"], [24, "loading-and-organizing-data"]], "Benchmarking preprocessing with parallelization and serialization": [[16, "benchmarking-preprocessing-with-parallelization-and-serialization"]], "Custom Dataset Example": [[17, "custom-dataset-example"]], "Load and save dataset example": [[18, "load-and-save-dataset-example"]], "MNE Dataset Example": [[19, "mne-dataset-example"]], "MOABB Dataset Example": [[20, "moabb-dataset-example"]], "Split Dataset Example": [[21, "split-dataset-example"]], "Loading the dataset": [[21, "loading-the-dataset"], [28, "loading-the-dataset"]], "Splitting": [[21, "splitting"]], "By description information": [[21, "by-description-information"]], "By row index": [[21, "by-row-index"]], "Observation": [[21, "observation"]], "Multiple discrete targets with the TUH EEG Corpus": [[22, "multiple-discrete-targets-with-the-tuh-eeg-corpus"]], "Creating Temple University Hospital (TUH) EEG Corpus Dataset": [[22, "creating-temple-university-hospital-tuh-eeg-corpus-dataset"]], "Exploring Data": [[22, "exploring-data"]], "Creating Windows": [[22, "creating-windows"]], "Exploring Windows": [[22, "exploring-windows"]], "DataLoader for Model Training": [[22, "dataloader-for-model-training"]], "Exploring DataLoader": [[22, "exploring-dataloader"]], "Examples": [[24, "examples"]], "Basic model building and training": [[24, "basic-model-building-and-training"], [25, "basic-model-building-and-training"]], "Simple training on MNE epochs": [[26, "simple-training-on-mne-epochs"]], "Finding the model you want": [[26, "finding-the-model-you-want"]], "Exploring the braindecode online documentation": [[26, "exploring-the-braindecode-online-documentation"]], "Examining the model": [[26, "examining-the-model"]], "Loading your own data with MNE": [[26, "loading-your-own-data-with-mne"]], "Training your model (scikit-learn compatible)": [[26, "training-your-model-scikit-learn-compatible"]], "Cropped Decoding on BCIC IV 2a Dataset": [[27, "cropped-decoding-on-bcic-iv-2a-dataset"]], "Create model and compute windowing parameters": [[27, "create-model-and-compute-windowing-parameters"]], "Cut the data into windows": [[27, "cut-the-data-into-windows"]], "Split the dataset": [[27, "split-the-dataset"]], "Plot Confusion Matrix": [[27, "plot-confusion-matrix"]], "Basic Brain Decoding on EEG Data": [[28, "basic-brain-decoding-on-eeg-data"]], "Loading and preparing the data": [[28, "loading-and-preparing-the-data"]], "Extracting Compute Windows": [[28, "extracting-compute-windows"]], "Splitting the dataset into training and validation sets": [[28, "splitting-the-dataset-into-training-and-validation-sets"]], "Creating a model": [[28, "creating-a-model"]], "Model Training": [[28, "model-training"]], "Plotting Results": [[28, "plotting-results"]], "Plotting a  Confusion Matrix": [[28, "plotting-a-confusion-matrix"]], "How to train, test and tune your model?": [[29, "how-to-train-test-and-tune-your-model"]], "Why should I care about model evaluation?": [[29, "why-should-i-care-about-model-evaluation"], [32, "why-should-i-care-about-model-evaluation"]], "Loading and preprocessing of data, defining a model, etc.": [[29, "loading-and-preprocessing-of-data-defining-a-model-etc"]], "Loading data": [[29, "loading-data"]], "Preprocessing data": [[29, "preprocessing-data"]], "Extraction of the Windows": [[29, "extraction-of-the-windows"], [30, "extraction-of-the-windows"]], "How to train and evaluate your model": [[29, "how-to-train-and-evaluate-your-model"], [32, "how-to-train-and-evaluate-your-model"]], "Option 1: Simple Train-Test Split": [[29, "option-1-simple-train-test-split"]], "Let\u2019s visualize the first option with a util function.": [[29, "let-s-visualize-the-first-option-with-a-util-function"]], "Option 2: Train-Val-Test Split": [[29, "option-2-train-val-test-split"]], "Let\u2019s visualize the second option with a util function.": [[29, "let-s-visualize-the-second-option-with-a-util-function"]], "Option 3: k-Fold Cross Validation": [[29, "option-3-k-fold-cross-validation"]], "Let\u2019s visualize the third option with a util function.": [[29, "let-s-visualize-the-third-option-with-a-util-function"]], "How to tune your hyperparameters": [[29, "how-to-tune-your-hyperparameters"]], "Option 1: Train-Val-Test Split": [[29, "option-1-train-val-test-split"]], "Option 2: k-Fold Cross Validation": [[29, "option-2-k-fold-cross-validation"]], "Hyperparameter tuning with scikit-learn": [[30, "hyperparameter-tuning-with-scikit-learn"]], "Why do you need to tune the neural networks model?": [[30, null]], "Extraction of the Compute Windows": [[30, "extraction-of-the-compute-windows"]], "Plotting the results": [[30, "plotting-the-results"]], "Get the best hyperparameters": [[30, "get-the-best-hyperparameters"]], "Convolutional neural network regression model on fake data.": [[31, "convolutional-neural-network-regression-model-on-fake-data"]], "Fake regression data": [[31, "fake-regression-data"]], "Generating fake regression dataset": [[31, "generating-fake-regression-dataset"]], "Defining a CNN regression model": [[31, "defining-a-cnn-regression-model"]], "Choosing between GPU and CPU processors": [[31, "choosing-between-gpu-and-cpu-processors"]], "Data windowing": [[31, "data-windowing"]], "Model training": [[31, "model-training"]], "Model evaluation": [[31, "model-evaluation"]], "Model testing": [[31, "model-testing"]], "Training a Braindecode model in PyTorch": [[32, "training-a-braindecode-model-in-pytorch"]], "Loading, preprocessing, defining a model, etc.": [[32, "loading-preprocessing-defining-a-model-etc"]], "Loading the Dataset Structure": [[32, "loading-the-dataset-structure"]], "Preprocessing, the offline transformation of the raw dataset": [[32, "preprocessing-the-offline-transformation-of-the-raw-dataset"]], "Create Pytorch model": [[32, "create-pytorch-model"]], "Option 1: Pure PyTorch training loop": [[32, "option-1-pure-pytorch-training-loop"]], "Option 2: Train it with PyTorch Lightning": [[32, "option-2-train-it-with-pytorch-lightning"]], "How to cite Braindecode": [[34, "how-to-cite-braindecode"]], "Citing the Braindecode": [[34, "citing-the-braindecode"]], "Citing the MNE": [[34, "citing-the-mne"]], "Citing other algorithms": [[34, "citing-other-algorithms"]], "braindecode.augmentation.AugmentedDataLoader": [[35, "braindecode-augmentation-augmenteddataloader"]], "Examples using braindecode.augmentation.AugmentedDataLoader": [[35, "examples-using-braindecode-augmentation-augmenteddataloader"]], "braindecode.augmentation.BandstopFilter": [[36, "braindecode-augmentation-bandstopfilter"]], "braindecode.augmentation.ChannelsDropout": [[37, "braindecode-augmentation-channelsdropout"]], "Examples using braindecode.augmentation.ChannelsDropout": [[37, "examples-using-braindecode-augmentation-channelsdropout"]], "braindecode.augmentation.ChannelsShuffle": [[38, "braindecode-augmentation-channelsshuffle"]], "braindecode.augmentation.ChannelsSymmetry": [[39, "braindecode-augmentation-channelssymmetry"]], "braindecode.augmentation.Compose": [[40, "braindecode-augmentation-compose"]], "Examples using braindecode.augmentation.Compose": [[40, "examples-using-braindecode-augmentation-compose"]], "braindecode.augmentation.FTSurrogate": [[41, "braindecode-augmentation-ftsurrogate"]], "Examples using braindecode.augmentation.FTSurrogate": [[41, "examples-using-braindecode-augmentation-ftsurrogate"]], "braindecode.augmentation.FrequencyShift": [[42, "braindecode-augmentation-frequencyshift"]], "Examples using braindecode.augmentation.FrequencyShift": [[42, "examples-using-braindecode-augmentation-frequencyshift"]], "braindecode.augmentation.GaussianNoise": [[43, "braindecode-augmentation-gaussiannoise"]], "braindecode.augmentation.IdentityTransform": [[44, "braindecode-augmentation-identitytransform"]], "braindecode.augmentation.Mixup": [[45, "braindecode-augmentation-mixup"]], "braindecode.augmentation.SensorsRotation": [[46, "braindecode-augmentation-sensorsrotation"]], "braindecode.augmentation.SensorsXRotation": [[47, "braindecode-augmentation-sensorsxrotation"]], "braindecode.augmentation.SensorsYRotation": [[48, "braindecode-augmentation-sensorsyrotation"]], "braindecode.augmentation.SensorsZRotation": [[49, "braindecode-augmentation-sensorszrotation"]], "braindecode.augmentation.SignFlip": [[50, "braindecode-augmentation-signflip"]], "Examples using braindecode.augmentation.SignFlip": [[50, "examples-using-braindecode-augmentation-signflip"]], "braindecode.augmentation.SmoothTimeMask": [[51, "braindecode-augmentation-smoothtimemask"]], "Examples using braindecode.augmentation.SmoothTimeMask": [[51, "examples-using-braindecode-augmentation-smoothtimemask"]], "braindecode.augmentation.TimeReverse": [[52, "braindecode-augmentation-timereverse"]], "braindecode.augmentation.Transform": [[53, "braindecode-augmentation-transform"]], "Examples using braindecode.augmentation.Transform": [[53, "examples-using-braindecode-augmentation-transform"]], "braindecode.augmentation.functional.bandstop_filter": [[54, "braindecode-augmentation-functional-bandstop-filter"]], "braindecode.augmentation.functional.channels_dropout": [[55, "braindecode-augmentation-functional-channels-dropout"]], "braindecode.augmentation.functional.channels_permute": [[56, "braindecode-augmentation-functional-channels-permute"]], "braindecode.augmentation.functional.channels_shuffle": [[57, "braindecode-augmentation-functional-channels-shuffle"]], "braindecode.augmentation.functional.frequency_shift": [[58, "braindecode-augmentation-functional-frequency-shift"]], "braindecode.augmentation.functional.ft_surrogate": [[59, "braindecode-augmentation-functional-ft-surrogate"]], "braindecode.augmentation.functional.gaussian_noise": [[60, "braindecode-augmentation-functional-gaussian-noise"]], "braindecode.augmentation.functional.identity": [[61, "braindecode-augmentation-functional-identity"]], "braindecode.augmentation.functional.mixup": [[62, "braindecode-augmentation-functional-mixup"]], "braindecode.augmentation.functional.sensors_rotation": [[63, "braindecode-augmentation-functional-sensors-rotation"]], "braindecode.augmentation.functional.sign_flip": [[64, "braindecode-augmentation-functional-sign-flip"]], "braindecode.augmentation.functional.smooth_time_mask": [[65, "braindecode-augmentation-functional-smooth-time-mask"]], "braindecode.augmentation.functional.time_reverse": [[66, "braindecode-augmentation-functional-time-reverse"]], "braindecode.classifier.EEGClassifier": [[67, "braindecode-classifier-eegclassifier"]], "braindecode.datasets.BCICompetitionIVDataset4": [[68, "braindecode-datasets-bcicompetitionivdataset4"]], "Examples using braindecode.datasets.BCICompetitionIVDataset4": [[68, "examples-using-braindecode-datasets-bcicompetitionivdataset4"]], "braindecode.datasets.BNCI2014001": [[69, "braindecode-datasets-bnci2014001"]], "Dataset summary": [[69, null], [72, null]], "braindecode.datasets.BaseConcatDataset": [[70, "braindecode-datasets-baseconcatdataset"]], "Examples using braindecode.datasets.BaseConcatDataset": [[70, "examples-using-braindecode-datasets-baseconcatdataset"]], "braindecode.datasets.BaseDataset": [[71, "braindecode-datasets-basedataset"]], "Examples using braindecode.datasets.BaseDataset": [[71, "examples-using-braindecode-datasets-basedataset"]], "braindecode.datasets.HGD": [[72, "braindecode-datasets-hgd"]], "braindecode.datasets.MOABBDataset": [[73, "braindecode-datasets-moabbdataset"]], "Examples using braindecode.datasets.MOABBDataset": [[73, "examples-using-braindecode-datasets-moabbdataset"]], "braindecode.datasets.SleepPhysionet": [[74, "braindecode-datasets-sleepphysionet"]], "Examples using braindecode.datasets.SleepPhysionet": [[74, "examples-using-braindecode-datasets-sleepphysionet"]], "braindecode.datasets.TUH": [[75, "braindecode-datasets-tuh"]], "Examples using braindecode.datasets.TUH": [[75, "examples-using-braindecode-datasets-tuh"]], "braindecode.datasets.TUHAbnormal": [[76, "braindecode-datasets-tuhabnormal"]], "Examples using braindecode.datasets.TUHAbnormal": [[76, "examples-using-braindecode-datasets-tuhabnormal"]], "braindecode.datasets.WindowsDataset": [[77, "braindecode-datasets-windowsdataset"]], "braindecode.datasets.create_from_X_y": [[78, "braindecode-datasets-create-from-x-y"]], "Examples using braindecode.datasets.create_from_X_y": [[78, "examples-using-braindecode-datasets-create-from-x-y"]], "braindecode.datasets.create_from_mne_epochs": [[79, "braindecode-datasets-create-from-mne-epochs"]], "Examples using braindecode.datasets.create_from_mne_epochs": [[79, "examples-using-braindecode-datasets-create-from-mne-epochs"]], "braindecode.datasets.create_from_mne_raw": [[80, "braindecode-datasets-create-from-mne-raw"]], "Examples using braindecode.datasets.create_from_mne_raw": [[80, "examples-using-braindecode-datasets-create-from-mne-raw"]], "braindecode.datautil.load_concat_dataset": [[81, "braindecode-datautil-load-concat-dataset"]], "Examples using braindecode.datautil.load_concat_dataset": [[81, "examples-using-braindecode-datautil-load-concat-dataset"]], "braindecode.datautil.save_concat_dataset": [[82, "braindecode-datautil-save-concat-dataset"]], "braindecode.models.ATCNet": [[83, "braindecode-models-atcnet"]], "braindecode.models.Deep4Net": [[84, "braindecode-models-deep4net"]], "Examples using braindecode.models.Deep4Net": [[84, "examples-using-braindecode-models-deep4net"]], "braindecode.models.DeepSleepNet": [[85, "braindecode-models-deepsleepnet"]], "braindecode.models.EEGConformer": [[86, "braindecode-models-eegconformer"]], "braindecode.models.EEGITNet": [[87, "braindecode-models-eegitnet"]], "braindecode.models.EEGInception": [[88, "braindecode-models-eeginception"]], "braindecode.models.EEGInceptionERP": [[89, "braindecode-models-eeginceptionerp"]], "braindecode.models.EEGInceptionMI": [[90, "braindecode-models-eeginceptionmi"]], "braindecode.models.EEGModuleMixin": [[91, "braindecode-models-eegmodulemixin"]], "Examples using braindecode.models.EEGModuleMixin": [[91, "examples-using-braindecode-models-eegmodulemixin"]], "braindecode.models.EEGNetv1": [[92, "braindecode-models-eegnetv1"]], "braindecode.models.EEGNetv4": [[93, "braindecode-models-eegnetv4"]], "braindecode.models.EEGResNet": [[94, "braindecode-models-eegresnet"]], "braindecode.models.HybridNet": [[95, "braindecode-models-hybridnet"]], "braindecode.models.ShallowFBCSPNet": [[96, "braindecode-models-shallowfbcspnet"]], "Examples using braindecode.models.ShallowFBCSPNet": [[96, "examples-using-braindecode-models-shallowfbcspnet"]], "braindecode.models.SleepStagerBlanco2020": [[97, "braindecode-models-sleepstagerblanco2020"]], "braindecode.models.SleepStagerChambon2018": [[98, "braindecode-models-sleepstagerchambon2018"]], "Examples using braindecode.models.SleepStagerChambon2018": [[98, "examples-using-braindecode-models-sleepstagerchambon2018"]], "braindecode.models.SleepStagerEldele2021": [[99, "braindecode-models-sleepstagereldele2021"]], "Examples using braindecode.models.SleepStagerEldele2021": [[99, "examples-using-braindecode-models-sleepstagereldele2021"]], "braindecode.models.TCN": [[100, "braindecode-models-tcn"]], "braindecode.models.TIDNet": [[101, "braindecode-models-tidnet"]], "braindecode.models.TimeDistributed": [[102, "braindecode-models-timedistributed"]], "Examples using braindecode.models.TimeDistributed": [[102, "examples-using-braindecode-models-timedistributed"]], "braindecode.models.USleep": [[103, "braindecode-models-usleep"]], "Examples using braindecode.models.USleep": [[103, "examples-using-braindecode-models-usleep"]], "braindecode.models.get_output_shape": [[104, "braindecode-models-get-output-shape"]], "Examples using braindecode.models.get_output_shape": [[104, "examples-using-braindecode-models-get-output-shape"]], "braindecode.preprocessing.Crop": [[105, "braindecode-preprocessing-crop"]], "braindecode.preprocessing.DropChannels": [[106, "braindecode-preprocessing-dropchannels"]], "braindecode.preprocessing.Filter": [[107, "braindecode-preprocessing-filter"]], "braindecode.preprocessing.Pick": [[108, "braindecode-preprocessing-pick"]], "braindecode.preprocessing.Preprocessor": [[109, "braindecode-preprocessing-preprocessor"]], "Examples using braindecode.preprocessing.Preprocessor": [[109, "examples-using-braindecode-preprocessing-preprocessor"]], "braindecode.preprocessing.Resample": [[110, "braindecode-preprocessing-resample"]], "braindecode.preprocessing.SetEEGReference": [[111, "braindecode-preprocessing-seteegreference"]], "braindecode.preprocessing.create_fixed_length_windows": [[112, "braindecode-preprocessing-create-fixed-length-windows"]], "Examples using braindecode.preprocessing.create_fixed_length_windows": [[112, "examples-using-braindecode-preprocessing-create-fixed-length-windows"]], "braindecode.preprocessing.create_windows_from_events": [[113, "braindecode-preprocessing-create-windows-from-events"]], "Examples using braindecode.preprocessing.create_windows_from_events": [[113, "examples-using-braindecode-preprocessing-create-windows-from-events"]], "braindecode.preprocessing.create_windows_from_target_channels": [[114, "braindecode-preprocessing-create-windows-from-target-channels"]], "Examples using braindecode.preprocessing.create_windows_from_target_channels": [[114, "examples-using-braindecode-preprocessing-create-windows-from-target-channels"]], "braindecode.preprocessing.exponential_moving_demean": [[115, "braindecode-preprocessing-exponential-moving-demean"]], "braindecode.preprocessing.exponential_moving_standardize": [[116, "braindecode-preprocessing-exponential-moving-standardize"]], "Examples using braindecode.preprocessing.exponential_moving_standardize": [[116, "examples-using-braindecode-preprocessing-exponential-moving-standardize"]], "braindecode.preprocessing.filterbank": [[117, "braindecode-preprocessing-filterbank"]], "braindecode.preprocessing.preprocess": [[118, "braindecode-preprocessing-preprocess"]], "Examples using braindecode.preprocessing.preprocess": [[118, 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diff --git a/stable/sg_api_usage.html b/stable/sg_api_usage.html
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-  <section id="unused-api-entries">
-<span id="sphx-glr-sg-api-usage"></span><h1>Unused API Entries<a class="headerlink" href="#unused-api-entries" title="Permalink to this heading">#</a></h1>
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-<li><p><a class="reference internal" href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter" title="braindecode.augmentation.BandstopFilter"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.augmentation.BandstopFilter</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter.get_augmentation_params" title="braindecode.augmentation.BandstopFilter.get_augmentation_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.BandstopFilter.get_augmentation_params()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.BandstopFilter.html#braindecode.augmentation.BandstopFilter.operation" title="braindecode.augmentation.BandstopFilter.operation"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.BandstopFilter.operation()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout.get_augmentation_params" title="braindecode.augmentation.ChannelsDropout.get_augmentation_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout.get_augmentation_params()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsDropout.html#braindecode.augmentation.ChannelsDropout.operation" title="braindecode.augmentation.ChannelsDropout.operation"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsDropout.operation()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle" title="braindecode.augmentation.ChannelsShuffle"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsShuffle</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle.get_augmentation_params" title="braindecode.augmentation.ChannelsShuffle.get_augmentation_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsShuffle.get_augmentation_params()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsShuffle.html#braindecode.augmentation.ChannelsShuffle.operation" title="braindecode.augmentation.ChannelsShuffle.operation"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsShuffle.operation()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry" title="braindecode.augmentation.ChannelsSymmetry"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsSymmetry</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry.get_augmentation_params" title="braindecode.augmentation.ChannelsSymmetry.get_augmentation_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsSymmetry.get_augmentation_params()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.ChannelsSymmetry.html#braindecode.augmentation.ChannelsSymmetry.operation" title="braindecode.augmentation.ChannelsSymmetry.operation"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.ChannelsSymmetry.operation()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.Compose.html#braindecode.augmentation.Compose.forward" title="braindecode.augmentation.Compose.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.Compose.forward()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate.get_augmentation_params" title="braindecode.augmentation.FTSurrogate.get_augmentation_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate.get_augmentation_params()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.augmentation.FTSurrogate.html#braindecode.augmentation.FTSurrogate.operation" title="braindecode.augmentation.FTSurrogate.operation"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.augmentation.FTSurrogate.operation()</span></code></a></p></li>
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-<li><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.on_batch_end" title="braindecode.regressor.EEGRegressor.on_batch_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.regressor.EEGRegressor.on_batch_end()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_proba" title="braindecode.regressor.EEGRegressor.predict_proba"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.regressor.EEGRegressor.predict_proba()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_trials" title="braindecode.regressor.EEGRegressor.predict_trials"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.regressor.EEGRegressor.predict_trials()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.regressor.EEGRegressor.html#braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys" title="braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.regressor.EEGRegressor.predict_with_window_inds_and_ys()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler" title="braindecode.samplers.BalancedSequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.BalancedSequenceSampler</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.samplers.BalancedSequenceSampler.html#braindecode.samplers.BalancedSequenceSampler.sample_class" title="braindecode.samplers.BalancedSequenceSampler.sample_class"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.samplers.BalancedSequenceSampler.sample_class()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_recording" title="braindecode.samplers.RecordingSampler.sample_recording"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.samplers.RecordingSampler.sample_recording()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.samplers.RecordingSampler.html#braindecode.samplers.RecordingSampler.sample_window" title="braindecode.samplers.RecordingSampler.sample_window"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.samplers.RecordingSampler.sample_window()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.samplers.RelativePositioningSampler.html#braindecode.samplers.RelativePositioningSampler.presample" title="braindecode.samplers.RelativePositioningSampler.presample"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.samplers.RelativePositioningSampler.presample()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.CroppedLoss.html#braindecode.training.CroppedLoss.forward" title="braindecode.training.CroppedLoss.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.CroppedLoss.forward()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.CroppedTimeSeriesEpochScoring.html#braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end" title="braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.CroppedTimeSeriesEpochScoring.on_epoch_end()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_batch_end" title="braindecode.training.CroppedTrialEpochScoring.on_batch_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.CroppedTrialEpochScoring.on_batch_end()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.CroppedTrialEpochScoring.html#braindecode.training.CroppedTrialEpochScoring.on_epoch_end" title="braindecode.training.CroppedTrialEpochScoring.on_epoch_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.CroppedTrialEpochScoring.on_epoch_end()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring" title="braindecode.training.PostEpochTrainScoring"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.training.PostEpochTrainScoring</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.PostEpochTrainScoring.html#braindecode.training.PostEpochTrainScoring.on_epoch_end" title="braindecode.training.PostEpochTrainScoring.on_epoch_end"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.PostEpochTrainScoring.on_epoch_end()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.TimeSeriesLoss.html#braindecode.training.TimeSeriesLoss.forward" title="braindecode.training.TimeSeriesLoss.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.training.TimeSeriesLoss.forward()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.mixup_criterion.html#braindecode.training.mixup_criterion" title="braindecode.training.mixup_criterion"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.training.mixup_criterion()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.predict_trials.html#braindecode.training.predict_trials" title="braindecode.training.predict_trials"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.training.predict_trials()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.training.trial_preds_from_window_preds.html#braindecode.training.trial_preds_from_window_preds" title="braindecode.training.trial_preds_from_window_preds"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.training.trial_preds_from_window_preds()</span></code></a></p></li>
-<li><p><a class="reference internal" href="generated/braindecode.visualization.compute_amplitude_gradients.html#braindecode.visualization.compute_amplitude_gradients" title="braindecode.visualization.compute_amplitude_gradients"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.visualization.compute_amplitude_gradients()</span></code></a></p></li>
-</ul>
-<p>API entries used: 29.68% (46/155)</p>
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diff --git a/stable/starting.html b/stable/starting.html
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-<li class="toctree-l1"><a class="reference internal" href="auto_examples/plot_bcic_iv_2a_moabb_cropped.html"> More data-efficient "cropped decoding"</a></li>
-<li class="toctree-l1"><a class="reference internal" href="auto_examples/plot_mne_dataset_example.html"> Your own datasets through MNE</a></li>
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-  <section id="getting-starting">
-<span id="help"></span><h1>Getting starting<a class="headerlink" href="#getting-starting" title="Permalink to this heading">#</a></h1>
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diff --git a/stable/whats_new.html b/stable/whats_new.html
index 17831422..6e7e35fb 100644
--- a/stable/whats_new.html
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+  
   <section id="what-s-new">
-<span id="whats-new"></span><h1>What’s new<a class="headerlink" href="#what-s-new" title="Permalink to this heading">#</a></h1>
-<section id="current-0-8-dev0">
-<h2>Current 0.8 (dev0)<a class="headerlink" href="#current-0-8-dev0" title="Permalink to this heading">#</a></h2>
+<span id="whats-new"></span><h1>What’s new<a class="headerlink" href="#what-s-new" title="Link to this heading">#</a></h1>
+<p>Please follow the examples below like <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code>, so the
+whats_new page will have a link to the function/class documentation.</p>
+<ul class="simple">
+<li><p>“Enhancements” for new features</p></li>
+<li><p>“Bugs” for bug fixes</p></li>
+<li><p>“API changes” for backward-incompatible changes</p></li>
+</ul>
+<section id="current-0-9-dev0">
+<span id="current"></span><h2>Current 0.9 (dev0)<a class="headerlink" href="#current-0-9-dev0" title="Link to this heading">#</a></h2>
 <section id="enhancements">
-<h3>Enhancements<a class="headerlink" href="#enhancements" title="Permalink to this heading">#</a></h3>
+<h3>Enhancements<a class="headerlink" href="#enhancements" title="Link to this heading">#</a></h3>
 </section>
 <section id="bugs">
-<h3>Bugs<a class="headerlink" href="#bugs" title="Permalink to this heading">#</a></h3>
+<h3>Bugs<a class="headerlink" href="#bugs" title="Link to this heading">#</a></h3>
 </section>
 <section id="api-changes">
-<h3>API changes<a class="headerlink" href="#api-changes" title="Permalink to this heading">#</a></h3>
+<h3>API changes<a class="headerlink" href="#api-changes" title="Link to this heading">#</a></h3>
 </section>
 </section>
-<section id="current-0-7-10-2022">
-<span id="changes-0-7-0"></span><h2>Current 0.7 (10-2022)<a class="headerlink" href="#current-0-7-10-2022" title="Permalink to this heading">#</a></h2>
+<section id="current-0-8-11-2022">
+<span id="changes-0-8-0"></span><h2>Current 0.8 (11-2022)<a class="headerlink" href="#current-0-8-11-2022" title="Link to this heading">#</a></h2>
 <section id="id1">
-<h3>Enhancements<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h3>
+<h3>Enhancements<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.EEGInceptionMI.html#braindecode.models.EEGInceptionMI" title="braindecode.models.EEGInceptionMI"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInceptionMI</span></code></a> network for motor imagery (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/428">#428</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.ATCNet.html#braindecode.models.ATCNet" title="braindecode.models.ATCNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.ATCNet</span></code></a> network for motor imagery (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/429">#429</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding to <a class="reference internal" href="generated/braindecode.datasets.TUH.html#braindecode.datasets.TUH" title="braindecode.datasets.tuh.TUH"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.tuh.TUH</span></code></a> compatibility with version 3.0 of TUH dataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/431">#431</a> by <a class="reference external" href="https://github.com/MohammadJavadD">Mohammad Javad D</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>, <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>, <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>, <a class="reference external" href="https://github.com/dengemann">Denis A. Engemann</a> and <a class="reference external" href="https://github.com/ostormer">Oskar Størmer</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.DeepSleepNet.html#braindecode.models.DeepSleepNet" title="braindecode.models.DeepSleepNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.DeepSleepNet</span></code></a> network for sleep staging (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/417">#417</a> by <a class="reference external" href="https://github.com/Tgnassou">Theo Gnassounou</a>)</p></li>
+<li><p>Adding <a class="reference internal" href="generated/braindecode.models.EEGConformer.html#braindecode.models.EEGConformer" title="braindecode.models.EEGConformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGConformer</span></code></a> network (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/454">#454</a> by <a class="reference external" href="https://github.com/eeyhsong">Yonghao Song</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding einops in the requirements (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/466">#466</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Have moabb as an extra dependency (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/467">#467</a> by <a class="reference external" href="https://github.com/ZamboniMarco99">Marco Zamboni</a>)</p></li>
+<li><p>Replacing the replacing Pytorch layers to Rearrange from einops #468  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/468">#468</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Refactoring the documentation and creating a sub-structure for the examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/470">#470</a> by <a class="reference external" href="https://github.com/dengemann">Denis A. Engemann</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Solving issues with slow conda and splitting the doc and test .yml to speed the CI. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/479">#479</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving the GitHub Actions CI and solving the skorch compatibility in the examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/472">#472</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Changing the documentation order (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/489">#489</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improve the documentation for the Temple University Hospital (TUH) EEG Corpus with discrete targets (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/485">#485</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving documentation for MOABB dataset, Trialwise Decoding &amp; Cropped Decoding (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/490">#490</a> by <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>)</p></li>
+<li><p>Improving the documentation for the sleep stage examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/487">#487</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Improving the tutorial Hyperparameter tuning with scikit-learn (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/473">#473</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add <a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> base class for all braindecode models (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Normalize all models common parameters and leaving the old ones as deprecated (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Improving the tutorial with a Data Augmentation Search (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/495">#495</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Improving documentation for “Split Dataset” and “Process a big data EEG resource” examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/494">#494</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a>)</p></li>
+<li><p>Improving documentation for the Convolutional neural network regression model on fake data (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/491">#491</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Enforcing the eval mode in the function predict trial. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/497">#497</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Adding extra requirements for pip install, update doc, removing conda env file (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/505">#505</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Add models user-friendly representation with torchinfo tables to <a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/488">#488</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Merged temporal and spatial convolutions for Deep4 and ShallowFBCSP (by <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a> and <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+<li><p>Enabling data augmentation of single inputs (with no batch dimension). (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/503">#503</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <cite>randomize</cite> parameter to <a class="reference internal" href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/504">#504</a> by <a href="#id17"><span class="problematic" id="id18">`Théo Gnassounou`_</span></a>.)</p></li>
+<li><p>Creating new preprocessor objects based on mne’s raw/Epochs methods <a class="reference internal" href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Resample</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.DropChannels</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.SetEEGReference</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Filter</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Pick</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.preprocessing.Crop</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/500">#500</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a> and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Moving <a href="#id2"><span class="problematic" id="id3">:function:`braindecode.models.util.get_output_shape`</span></a> and <a href="#id4"><span class="problematic" id="id5">:function:`braindecode.models.util.to_dense_prediction_model`</span></a> to <a class="reference internal" href="generated/braindecode.models.EEGModuleMixin.html#braindecode.models.EEGModuleMixin" title="braindecode.models.base.EEGModuleMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.base.EEGModuleMixin</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/514">#514</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Automatically populate signal-related parameters in <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/517">#517</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Adding a pure PyTorch tutorial (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/523">#523</a> by <a class="reference external" href="https://github.com/remidbs">Remi Delbouys</a>  and <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">models_dict</span></code> to <code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.models.util</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/524">#524</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Keep using <cite>mne.Raw</cite> after windowing to speed up windowing, do not create <cite>mne.Epochs</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/515">#515</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+<li><p>Changing <a class="reference internal" href="generated/braindecode.models.Deep4Net.html#braindecode.models.Deep4Net" title="braindecode.models.Deep4Net"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.Deep4Net</span></code></a> <cite>final_conv_length</cite> default value to ‘auto’ (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/535">#535</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Add support for <a class="reference external" href="https://mne.tools/stable/generated/mne.Epochs.html#mne.Epochs" title="(in MNE v1.5)"><code class="xref py py-class docutils literal notranslate"><span class="pre">mne.Epochs</span></code></a> in <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/529">#529</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Allow passing only the name of a braindecode model to <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/528">#528</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Standardizing models’ last layer names (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/520">#520</a> by <a class="reference external" href="https://github.com/brunaafl">Bruna Lopes</a> and <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Add basic training example with MNE epochs (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/539">#539</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Log validation accuracy in <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/541">#541</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Better type hints in <code class="xref py py-mod docutils literal notranslate"><span class="pre">braindecode.augmentation.base</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/551">#551</a> by <a class="reference external" href="https://github.com/tgy">Valentin Iovene</a>)</p></li>
+<li><p>Support for MOABB 1.0.0 and switch to pyproject.toml (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/553">#553</a> by <a class="reference external" href="https://github.com/sylvchev">Sylvain Chevallier</a>)</p></li>
+<li><p>Adding pre-commit hooks (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/556">#556</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+</ul>
+</section>
+<section id="id6">
+<h3>Bugs<a class="headerlink" href="#id6" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Fixing conda env in the CI (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/461">#461</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Fixing E231 missing whitespace after ‘,’ untraceable error in old flake8 (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/460">#460</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing deprecation warning due to torch transposition in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.augmentation.functional._frequency_shift()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/446">#446</a> by <a class="reference external" href="https://github.com/matthieutrs">Matthieu Terris</a>)</p></li>
+<li><p>Fix padding’s device in <a class="reference internal" href="generated/braindecode.models.EEGResNet.html#braindecode.models.EEGResNet" title="braindecode.models.EEGResNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGResNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/451">#451</a> by <a class="reference external" href="https://github.com/PierreGtch">Pierre Guetschel</a>)</p></li>
+<li><p>Fix skorch version issue (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/465">#465</a> by <a class="reference external" href="https://github.com/ZamboniMarco99">Marco Zamboni</a>)</p></li>
+<li><p>Fix wrong <cite>kernel_size</cite> dtype when running torchinfo in <a class="reference internal" href="generated/braindecode.models.USleep.html#braindecode.models.USleep" title="braindecode.models.USleep"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.USleep</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/538">#538</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
+<li><p>Fix bug when using GPU and channel shuffle transform (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/546">#546</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
+</ul>
+</section>
+<section id="id7">
+<h3>API changes<a class="headerlink" href="#id7" title="Link to this heading">#</a></h3>
+<ul class="simple">
+<li><p>Renaming the <a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInception</span></code></a> network as <a class="reference internal" href="generated/braindecode.models.EEGInceptionERP.html#braindecode.models.EEGInceptionERP" title="braindecode.models.EEGInceptionERP"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInceptionERP</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/428">#428</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Removing support for Python 3.7 (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/397">#397</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Removing the LogSoftmax layer from the models and adding deprecated warnings and temporary flags (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/513">#513</a> by <a class="reference external" href="https://github.com/Sara04">Sara Sedlar</a>)</p></li>
+</ul>
+</section>
+</section>
+<section id="current-0-7-10-2022">
+<span id="changes-0-7-0"></span><h2>Current 0.7 (10-2022)<a class="headerlink" href="#current-0-7-10-2022" title="Link to this heading">#</a></h2>
+<section id="id8">
+<h3>Enhancements<a class="headerlink" href="#id8" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Adding EEG-Inception Network <a class="reference internal" href="generated/braindecode.models.EEGInception.html#braindecode.models.EEGInception" title="braindecode.models.EEGInception"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGInception</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/390">#390</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
 <li><p>Adding EEG-ITNet Network <a class="reference internal" href="generated/braindecode.models.EEGITNet.html#braindecode.models.EEGITNet" title="braindecode.models.EEGITNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.EEGITNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/400">#400</a> by <a class="reference external" href="https://github.com/GhBlg">Ghaith Bouallegue</a>)</p></li>
 <li><p>Allowing target_names as list for BaseDataset (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/371">#371</a> by <a class="reference external" href="https://github.com/MohammadJavadD">Mohammad Javad D</a> and <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
 <li><p>Adding tutorial with GridSearchCV for data augmentation on the BCIC IV 2a with module <cite>braindecode.augmentation</cite> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/389">#389</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
 <li><p>Adding tutorial with GridSearchCV to exemplify how to tune hyperparameters, for instance with the learning rate (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/349">#349</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a> and by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
-<li><p>Adding tutorial with a Unified Validation sheme (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/378">#378</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a>)</p></li>
+<li><p>Adding tutorial with a Unified Validation scheme (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/378">#378</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a>)</p></li>
 <li><p>Adding <cite>verbose</cite> parameter to <a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events()</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_target_channels.html#braindecode.preprocessing.create_windows_from_target_channels" title="braindecode.preprocessing.create_windows_from_target_channels"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_target_channels()</span></code></a>, and <a class="reference internal" href="generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/391">#391</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
 <li><p>Enable augmentation on GPU within <code class="xref py py-class docutils literal notranslate"><span class="pre">AugmentedDataloader</span></code> via a new <cite>device</cite> parameter (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/406">#406</a> by <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a>, <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
+<li><p>Adding <cite>randomize</cite> parameter to <a class="reference internal" href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/504">#504</a> by <a href="#id19"><span class="problematic" id="id20">`Théo Gnassounou`_</span></a>.)</p></li>
 </ul>
 </section>
-<section id="id2">
-<h3>Bugs<a class="headerlink" href="#id2" title="Permalink to this heading">#</a></h3>
+<section id="id9">
+<h3>Bugs<a class="headerlink" href="#id9" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Fixing parameter <cite>subject_ids</cite> to <cite>recoding_ids</cite> in TUHAbnormal example (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/402">#402</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
 <li><p>Bug fix <a class="reference internal" href="generated/braindecode.augmentation.functional.ft_surrogate.html#braindecode.augmentation.functional.ft_surrogate" title="braindecode.augmentation.functional.ft_surrogate"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.augmentation.functional.ft_surrogate()</span></code></a> and add option to sample independently per-channel (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/409">#409</a> by <a class="reference external" href="https://github.com/martinwimpff">Martin Wimpff</a> and <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>)</p></li>
 </ul>
 </section>
-<section id="id3">
-<h3>API changes<a class="headerlink" href="#id3" title="Permalink to this heading">#</a></h3>
+<section id="id10">
+<h3>API changes<a class="headerlink" href="#id10" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Renaming the method <cite>get_params</cite> to <cite>get_augmentation_params</cite> in augmentation classes. This makes the Transform module compatible with scikit-learn cloning mechanism (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/388">#388</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a> and <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
-<li><p>Delaying the deprecation of the preprocessing scale function <a class="reference internal" href="generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code></a> and updates tutorials where the function were used. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/413">#413</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
+<li><p>Delaying the deprecation of the preprocessing scale function <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code> and updates tutorials where the function were used. (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/413">#413</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
 <li><p>Removing deprecated functions and classes <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.zscore()</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.MNEPreproc</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.NumpyPreproc</span></code>  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/415">#415</a> by <a class="reference external" href="https://github.com/bruAristimunha">Bruno Aristimunha</a>)</p></li>
 <li><p>Setting <cite>iterator_train__drop_last=True</cite> by default for <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGClassifier</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.EEGRegressor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/411">#411</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
 </ul>
 </section>
 </section>
 <section id="version-0-6-2021-12-06">
-<span id="changes-0-6-0"></span><h2>Version 0.6 (2021-12-06)<a class="headerlink" href="#version-0-6-2021-12-06" title="Permalink to this heading">#</a></h2>
-<section id="id4">
-<h3>Enhancements<a class="headerlink" href="#id4" title="Permalink to this heading">#</a></h3>
+<span id="changes-0-6-0"></span><h2>Version 0.6 (2021-12-06)<a class="headerlink" href="#version-0-6-2021-12-06" title="Link to this heading">#</a></h2>
+<section id="id11">
+<h3>Enhancements<a class="headerlink" href="#id11" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Adding <a class="reference internal" href="generated/braindecode.samplers.SequenceSampler.html#braindecode.samplers.SequenceSampler" title="braindecode.samplers.SequenceSampler"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.samplers.SequenceSampler</span></code></a> along with support for returning sequences of windows in <a class="reference internal" href="generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset</span></code></a> and an updated sleep staging example to show how to train on sequences of windows (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/263">#263</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
-<li><p>Adding Thinker Invariance Network <a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.TIDNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/170">#170</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Dan Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
-<li><p>Adding a confusion matrix plot generator <a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/274">#274</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Dan Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Adding Thinker Invariance Network <a class="reference internal" href="generated/braindecode.models.TIDNet.html#braindecode.models.TIDNet" title="braindecode.models.TIDNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.TIDNet</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/170">#170</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Adding a confusion matrix plot generator <a class="reference internal" href="generated/braindecode.visualization.plot_confusion_matrix.html#braindecode.visualization.plot_confusion_matrix" title="braindecode.visualization.plot_confusion_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.visualization.plot_confusion_matrix()</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/274">#274</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a href="#id21"><span class="problematic" id="id22">`Dan Wilson`_</span></a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
 <li><p>Adding data <a class="reference internal" href="api.html#augmentation-api"><span class="std std-ref">Augmentation</span></a> module (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/254">#254</a> by <a class="reference external" href="https://cedricrommel.github.io">Cedric Rommel</a>, <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a> and <a class="reference external" href="https://tommoral.github.io">Thomas Moreau</a>)</p></li>
 <li><p>Adding Mixup augmentation <a class="reference internal" href="generated/braindecode.augmentation.Mixup.html#braindecode.augmentation.Mixup" title="braindecode.augmentation.Mixup"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.augmentation.Mixup</span></code></a> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/254">#254</a> by <a class="reference external" href="https://github.com/sbbrandt">Simon Brandt</a>)</p></li>
 <li><p>Adding saving of preprocessing and windowing choices in <a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a>, <a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_events.html#braindecode.preprocessing.create_windows_from_events" title="braindecode.preprocessing.create_windows_from_events"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_windows_from_events()</span></code></a> and <a class="reference internal" href="generated/braindecode.preprocessing.create_fixed_length_windows.html#braindecode.preprocessing.create_fixed_length_windows" title="braindecode.preprocessing.create_fixed_length_windows"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.create_fixed_length_windows()</span></code></a> to datasets to facilitate reproducibility (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/287">#287</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
@@ -435,28 +599,28 @@ <h3>Enhancements<a class="headerlink" href="#id4" title="Permalink to this headi
 <li><p>Adding <code class="docutils literal notranslate"><span class="pre">crop</span></code> parameter to <a class="reference internal" href="generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> dataset to speed up examples (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/367">#367</a> by <a class="reference external" href="http://alexandre.gramfort.net">Alex Gramfort</a>)</p></li>
 </ul>
 </section>
-<section id="id5">
-<h3>Bugs<a class="headerlink" href="#id5" title="Permalink to this heading">#</a></h3>
+<section id="id12">
+<h3>Bugs<a class="headerlink" href="#id12" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Correctly computing recording length in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_fixed_length_windows()</span></code> in case recording was cropped (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/304">#304</a> by <a class="reference external" href="https://github.com/gemeinl">Lukas Gemein</a>)</p></li>
 <li><p>Fixing <a class="reference internal" href="generated/braindecode.datasets.SleepPhysionet.html#braindecode.datasets.SleepPhysionet" title="braindecode.datasets.SleepPhysionet"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datasets.SleepPhysionet</span></code></a> to allow serialization and avoid mismatch in channel names attributes (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/327">#327</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 <li><p>Propagating <cite>target_transform</cite> to all datasets when using <code class="xref py py-meth docutils literal notranslate"><span class="pre">braindecode.datasets.BaseConcatDataset.subset()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/261">#261</a> by <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
 </ul>
 </section>
-<section id="id6">
-<h3>API changes<a class="headerlink" href="#id6" title="Permalink to this heading">#</a></h3>
+<section id="id13">
+<h3>API changes<a class="headerlink" href="#id13" title="Link to this heading">#</a></h3>
 <ul class="simple">
-<li><p>Removing the default sampling frequency sfreq value in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datasets.create_windows_from_events()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/256">#256</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Dan Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
-<li><p>Made windowing arguments optional in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_fixed_length_windows()</span></code> &amp; <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_windows_from_events()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/269">#269</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Dan Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
-<li><p>Deprecating preprocessing functions <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.zscore()</span></code> and <a class="reference internal" href="generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code></a> in favour of sklearn’s implementation (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/292">#292</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
+<li><p>Removing the default sampling frequency sfreq value in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datasets.create_windows_from_events()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/256">#256</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a class="reference external" href="https://github.com/dcwil">Daniel Wilson</a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Made windowing arguments optional in <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_fixed_length_windows()</span></code> &amp; <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.windowers.create_windows_from_events()</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/269">#269</a> by <a class="reference external" href="https://github.com/Ann-KathrinKiessner">Ann-Kathrin Kiessner</a>, <a href="#id23"><span class="problematic" id="id24">`Dan Wilson`_</span></a>, <a class="reference external" href="https://github.com/HenrikBons">Henrik Bonsmann</a>, <a class="reference external" href="https://github.com/vytjan">Vytautas Jankauskas</a>)</p></li>
+<li><p>Deprecating preprocessing functions <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.zscore()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.scale()</span></code> in favour of sklearn’s implementation (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/292">#292</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 <li><p><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.preprocessing.preprocess()</span></code></a> now returns a <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.dataset.BaseConcatDataset</span></code> object (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/277">#277</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 </ul>
 </section>
 </section>
 <section id="version-0-5-1-2021-07-14">
-<span id="changes-0-5-1"></span><h2>Version 0.5.1 (2021-07-14)<a class="headerlink" href="#version-0-5-1-2021-07-14" title="Permalink to this heading">#</a></h2>
-<section id="id7">
-<h3>Enhancements<a class="headerlink" href="#id7" title="Permalink to this heading">#</a></h3>
+<span id="changes-0-5-1"></span><h2>Version 0.5.1 (2021-07-14)<a class="headerlink" href="#version-0-5-1-2021-07-14" title="Link to this heading">#</a></h2>
+<section id="id14">
+<h3>Enhancements<a class="headerlink" href="#id14" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Adding <cite>n_jobs</cite> parameter to windowers <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_windows_from_events()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_fixed_length_windows()</span></code> to allow for parallelization of the windowing process (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/199">#199</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 <li><p>Adding support for on-the-fly transforms (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/198">#198</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
@@ -475,8 +639,8 @@ <h3>Enhancements<a class="headerlink" href="#id7" title="Permalink to this headi
 <li><p>Adding <a class="reference internal" href="generated/braindecode.models.SleepStagerEldele2021.html#braindecode.models.SleepStagerEldele2021" title="braindecode.models.SleepStagerEldele2021"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerEldele2021</span></code></a> and <a class="reference internal" href="generated/braindecode.models.SleepStagerBlanco2020.html#braindecode.models.SleepStagerBlanco2020" title="braindecode.models.SleepStagerBlanco2020"><code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.models.SleepStagerBlanco2020</span></code></a> models for sleep staging  (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/341">#341</a> by <a class="reference external" href="https://github.com/Div12345">Divyesh Narayanan</a>)</p></li>
 </ul>
 </section>
-<section id="id8">
-<h3>Bugs<a class="headerlink" href="#id8" title="Permalink to this heading">#</a></h3>
+<section id="id15">
+<h3>Bugs<a class="headerlink" href="#id15" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Amplitude gradients are correctly computed for layers with multiple filters
 (before, they were accidentally summed over all previous filters in the layer) (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/167">#167</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a>)</p></li>
@@ -490,193 +654,128 @@ <h3>Bugs<a class="headerlink" href="#id8" title="Permalink to this heading">#</a
 <li><p>Make EEGResNet work for <code class="code docutils literal notranslate"><span class="pre">final_pool_length='auto'</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/223">#223</a> by <a class="reference external" href="https://github.com/robintibor">Robin Tibor Schirrmeister</a> and <a class="reference external" href="https://github.com/sliwy">Maciej Śliwowski</a>)</p></li>
 </ul>
 </section>
-<section id="id9">
-<h3>API changes<a class="headerlink" href="#id9" title="Permalink to this heading">#</a></h3>
+<section id="id16">
+<h3>API changes<a class="headerlink" href="#id16" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>Preprocessor classes <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.MNEPreproc</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.NumpyPreproc</span></code> are deprecated in favor of <code class="xref py py-class docutils literal notranslate"><span class="pre">braindecode.datautil.Preprocessor</span></code> (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/197">#197</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 <li><p>Parameter <cite>stop_offset_samples</cite> of <code class="xref py py-func docutils literal notranslate"><span class="pre">braindecode.datautil.create_fixed_length_windows()</span></code> must now be set to <cite>None</cite> instead of 0 to indicate the end of the recording (<a class="reference external" href="https://github.com/braindecode/braindecode/issues/152">#152</a> by <a class="reference external" href="https://github.com/hubertjb">Hubert Banville</a>)</p></li>
 </ul>
 </section>
 <section id="authors">
-<h3>Authors<a class="headerlink" href="#authors" title="Permalink to this heading">#</a></h3>
+<h3>Authors<a class="headerlink" href="#authors" title="Link to this heading">#</a></h3>
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+
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   </body>
 </html>
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